Question: Controlling the dissemination of false information on social media networks through assessing users' credibility.

This document outlines an innovative strategy for social media platforms to combat the proliferation of fake news and misinformation through crowd-sourced monitoring. Platforms are exploring a system where users can signal concern about the veracity of a post by flagging it. If a post receives a threshold number of flags, it is forwarded to an independent fact-checking entity. Upon confirmation of the misinformation, the post is labeled as disputed. However, this method necessitates a robust algorithmic approach to balance the uncertainty of user exposure, the expense of fact-checking, and the correlation between flags and exposure rates, as currently, no effective algorithms exist for this purpose. In this paper, we propose a versatile modeling framework using marked temporal point processes to represent the flagging mechanism. Subsequently, we introduce CURB, a scalable online algorithm designed to determine which flagged stories should be prioritized for fact-checking and when, with the aim of efficiently curtailing misinformation spread while providing theoretical assurances. This algorithm necessitates the resolution of a unique stochastic optimal control problem for stochastic differential equations with jumps, which is of general interest. Our empirical evaluations on datasets sourced from Twitter and Weibo demonstrate CURB's potential to significantly mitigate the dissemination of fake news and misinformation.

In this paper, we address the important issue of uncertainty in the edge influence probability estimates for the well studied influence maximization problem --- the task of finding k seed nodes in a social network to maximize the influence spread. We propose the problem of robust influence maximization, which maximizes the worst-case ratio between the influence spread of the chosen seed set and the optimal seed set, given the uncertainty of the parameter input. We design an algorithm that solves this problem with a solution-dependent bound. We further study uniform sampling and adaptive sampling methods to effectively reduce the uncertainty on parameters and improve the robustness of the influence maximization task. Our empirical results show that parameter uncertainty may greatly affect influence maximization performance and prior studies that learned influence probabilities could lead to poor performance in robust influence maximization due to relatively large uncertainty in parameter estimates, and information cascade based adaptive sampling method may be an effective way to improve the robustness of influence maximization.

We present Aardvark, a social search engine. With Aardvark, users ask a question, either by instant message, email, web input, text message, or voice. Aardvark then routes the question to the person in the user's extended social network most likely to be able to answer that question. As compared to a traditional web search engine, where the challenge lies in finding the right document to satisfy a user's information need, the challenge in a social search engine like Aardvark lies in finding the right person to satisfy a user's information need. Further, while trust in a traditional search engine is based on authority, in a social search engine like Aardvark, trust is based on intimacy. We describe how these considerations inform the architecture, algorithms, and user interface of Aardvark, and how they are reflected in the behavior of Aardvark users.

The explosive growth in social network content suggests that the largest "sensor network" yet might be human. Extending the participatory sensing model, this paper explores the prospect of utilizing social networks as sensor networks, which gives rise to an interesting reliable sensing problem. In this problem, individuals are represented by sensors (data sources) who occasionally make observations about the physical world. These observations may be true or false, and hence are viewed as binary claims. The reliable sensing problem is to determine the correctness of reported observations. From a networked sensing standpoint, what makes this sensing problem formulation different is that, in the case of human participants, not only is the reliability of sources usually unknown but also the original data provenance may be uncertain. Individuals may report observations made by others as their own. The contribution of this paper lies in developing a model that considers the impact of such information sharing on the analytical foundations of reliable sensing, and embed it into a tool called Apollo that uses Twitter as a "sensor network" for observing events in the physical world. Evaluation, using Twitter-based case-studies, shows good correspondence between observations deemed correct by Apollo and ground truth.

This paper describes an original method for evaluating peer review in online systems by calculating the helpfulness of an individual reviewer's response. We focus on the development of specific and machine scoreable indicators for quality in online peer review.

Social psychology has demonstrated that lying is an important, and frequent, part of everyday social interactions. As communication technologies become more ubiquitous in our daily interactions, an important question for developers is to determine how the design of these technologies affects lying behavior. The present research reports the results of a diary study, in which participants recorded all of their social interactions and lies for seven days. The data reveal that participants lied most on the telephone and least in email, and that lying rates in face-to-face and instant messaging interactions were approximately equal. This pattern of results suggests that the design features of communication technologies (e.g., synchronicity, recordability, and copresence) affect lying behavior in important ways, and that these features must be considered by both designers and users when issues of deception and trust arise. The implications for designing applications that increase, decrease or detect deception are discussed.

We provide a systematic study of the problem of finding the source of a computer virus in a network. We model virus spreading in a network with a variant of the popular SIR model and then construct an estimator for the virus source. This estimator is based upon a novel combinatorial quantity which we term rumor centrality. We establish that this is an ML estimator for a class of graphs. We find the following surprising threshold phenomenon: on trees which grow faster than a line, the estimator always has non-trivial detection probability, whereas on trees that grow like a line, the detection probability will go to 0 as the network grows. Simulations performed on synthetic networks such as the popular small-world and scale-free networks, and on real networks such as an internet AS network and the U.S. electric power grid network, show that the estimator either finds the source exactly or within a few hops in different network topologies. We compare rumor centrality to another common network centrality notion known as distance centrality. We prove that on trees, the rumor center and distance center are equivalent, but on general networks, they may differ. Indeed, simulations show that rumor centrality outperforms distance centrality in finding virus sources in networks which are not tree-like.

Modern social media are becoming overloaded with information because of the rapidly-expanding number of information feeds. We analyze the user-generated content in Sina Weibo, and find evidence that the spread of popular messages often follow a mechanism that differs from the spread of disease, in contrast to common belief. In this mechanism, an individual with more friends needs more repeated exposures to spread further the information. Moreover, our data suggest that for certain messages the chance of an individual to share the message is proportional to the fraction of its neighbours who shared it with him/her, which is a result of competition for attention. We model this process using a fractional susceptible infected recovered (FSIR) model, where the infection probability of a node is proportional to its fraction of infected neighbors. Our findings have dramatic implications for information contagion. For example, using the FSIR model we find that real-world social networks have a finite epidemic threshold in contrast to the zero threshold in disease epidemic models. This means that when individuals are overloaded with excess information feeds, the information either reaches out the population if it is above the critical epidemic threshold, or it would never be well received.

Using social networking services is becoming more popular day by day. Social network analysis views relationships in terms of nodes (people) and edges (links or connections—the relationship between the people). The websites of the social networks such as Facebook currently are among the most popular internet services just after giant portals such as Yahoo, MSN and search engines such as Google. One of the main problems in analyzing these networks is the prediction of relationships between people in the network. It is hard to find one method that can identify relation between people in the social network. The purpose of this paper is to forecast the friendship relation between individuals among a social network, especially the likelihood of a relation between an existing member with a new member. For this purpose, we used a few hypotheses to make the graph of relationships between members of social network, and we used the method of logistic regression to complete the graph. Test data from Flickr website are used to evaluate the proposed method. The results show that the method has achieved 99 % accuracy in prediction of friendship relationships.

Content organization over the Internet went through several interesting phases of evolution: from structured di rectories to unstructured Web search engines and more recently, to tagging as a way for aggregating information, a step toward s the semantic web vision. Tagging allows ranking and dat a organization to directly utilize inputs from end us er , enabling machine processing of Web content. Since tags are c reated by individual users in a free form, one important prob lem facing tagging is to identify most appropriate tags, while eliminating noise and spam. For this purpose, we define a set o f general criteria for a good tagging system. These criteria include high coverage of multiple facets to ensure good recall, least effort to reduce the cost involved in browsing, and high popu larity to ensure tag quality. We propose a collaborative tag su gestion algorithm using these criteria to spot high-quality ags. The proposed algorithm employs a goodness measure for t ags derived from collective user authorities to combat spam. Th e goodness measure is iteratively adjusted by a reward-penalty lgorithm, which also incorporates other sources of tags, e.g. , content-based auto-generated tags. Our experiments based on My We b 2.0 show that the algorithm is effective.

This talk presents ongoing work on the study of information diffusion in social media, focusing in particular on political communication in the Twitter microblogging network. Social media platforms play an important role in shaping political discourse in the US and around the world. The truthy.indiana.edu infrastructure allows us to mine and visualize a large stream of social media data related to political themes. The analyses in this keynote address polarization and cross-ideological communication, and partisan asymmetries in the online political activities of social media users. Machine learning efforts can successfully leverage the structure of meme diffusion networks to detect orchestrated astroturf attacks that simulate grassroots campaigns, and to predict the political affiliation of active users. The retweet network segregates individuals into two distinct, homogenous communities of left- and right-leaning users. The mention network does not exhibit this kind of segregation, instead forming a communication bridge across which information flows between these two partisan communities. We propose a mechanism of action to explain these divergent topologies and provide statistical evidence in support of this hypothesis. Related to political communication are questions about the birth of online social movements. Social media data provides an opportunity to look for signatures that capture these seminal events. Finally, I will introduce a model of the competition for attention in social media. A dynamic of information diffusion emerges from this process, where a few ideas go viral while most do not. I will show that the relative popularity of different topics, the diversity of information to which we are exposed, and the fading of our collective interests for specific memes, can all be explained as deriving from a combination between the competition for limited attention and the structure of social networks. Surprisingly, one can reproduce the massive heterogeneity in the popularity and persistence of ideas without the need to assume different intrinsic values among those ideas.


Question: Restricting the dissemination of false information on social networks through assessing user credibility.

This paper introduces a distinctive collaborative filtering technique that harnesses expert opinions from the web to enhance the recommendation system for internet users. Traditional nearest-neighbor collaborative filtering, while effective, is marred by issues like data sparsity, noise, the cold-start problem, and scalability challenges. Our proposed method breaks from convention by leveraging expert opinions instead of solely relying on user-rating data. In this variant, predictions are derived from a set of expert neighbors sourced from a separate dataset, with the weight of their opinions determined by their alignment with the user's preferences. This approach aims to mitigate the drawbacks of conventional collaborative filtering, all while preserving or improving recommendation accuracy. To substantiate the efficacy of our method, we apply it to a segment of the Netflix dataset. We utilize ratings scraped from a web-based repository of expert reviews, evaluating our results not only on prediction accuracy but also on the precision of recommendation lists. Moreover, we conduct a user study to gauge the practical utility of our recommendations, demonstrating that users tend to favor those generated by our novel approach. This method has the potential to significantly improve the quality and relevance of recommendations by tapping into the wisdom of experts.

With social networks like Facebook, Twitter and Google+ attracting audiences of millions of users, they have been an important communication platform in daily life. This in turn attracts malicious users to the social networks as well, causing an increase in the incidence of low quality information. Low quality information such as spam and rumors is a nuisance to people and hinders them from consuming information that is pertinent to them or that they are looking for. Although individual social networks are capable of filtering a significant amount of low quality information they receive, they usually require large amounts of resources (e.g, personnel) and incur a delay before detecting new types of low quality information. Also the evolution of various low quality information posts lots of challenges to defensive techniques. My PhD thesis work focuses on the analysis and detection of low quality information in social networks. We introduce social spam analytics and detection framework SPADE across multiple social networks showing the efficiency and flexibility of cross-domain classification and associative classification. For evolutionary study of low quality information, we present the results on large-scale study on Web spam and email spam over a long period of time. Furthermore, we provide activity-based detection approaches to filter out low quality information in social networks: click traffic analysis of short URL spam, behavior analysis of URL spam and information diffusion analysis of rumor. Our framework and detection techniques show promising results in analyzing and detecting low quality information in social networks.

Over the past few years, a number of new “fringe” communities, like 4chan or certain subreddits, have gained traction on the Web at a rapid pace. However, more often than not, little is known about how they evolve or what kind of activities they attract, despite recent research has shown that they influence how false information reaches mainstream communities. This motivates the need to monitor these communities and analyze their impact on the Web’s information ecosystem. In August 2016, a new social network called Gab was created as an alternative to Twitter. It positions itself as putting “people and free speech first”, welcoming users banned or suspended from other social networks. In this paper, we provide, to the best of our knowledge, the first characterization of Gab. We collect and analyze 22M posts produced by 336K users between August 2016 and January 2018, finding that Gab is predominantly used for the dissemination and discussion of news and world events, and that it attracts alt-right users, conspiracy theorists, and other trolls. We also measure the prevalence of hate speech on the platform, finding it to be much higher than Twitter, but lower than 4chan’s Politically Incorrect board.

Rumor stance classification is the task of determining the stance towards a rumor in text. This is the first step in effective rumor tracking on social media which is an increasingly important task. In this work, we analyze Twitter users' stance toward a rumorous tweet, in which users could support, deny, query, or comment upon the rumor. We propose a deep attentional CNN-LSTM approach, which takes the sequence of tweets in a thread of conversation as the input. We use neighboring tweets in the timeline as context vectors to capture the temporal dynamism in users' stance evolution. In addition, we use extra features such as friendship, to leverage useful relational features that are readily available in social media. Our model achieves the state-of-the-art results on rumor stance classification on a recent SemEval dataset, improving accuracy and F1 score by 3.6% and 4.2% respectively.

The World Wide Web (WWW) has become a rapidly growing platform consisting of numerous sources which provide supporting or contradictory information about claims (e.g., “Chicken meat is healthy”). In order to decide whether a claim is true or false, one needs to analyze content of different sources of information on the Web, measure credibility of information sources, and aggregate all these information. This is a tedious process and the Web search engines address only part of the overall problem, viz., producing only a list of relevant sources. In this paper, we present ClaimEval, a novel and integrated approach which given a set of claims to validate, extracts a set of pro and con arguments from the Web information sources, and jointly estimates credibility of sources and correctness of claims. ClaimEval uses Probabilistic Soft Logic (PSL), resulting in a flexible and principled framework which makes it easy to state and incorporate different forms of prior-knowledge. Through extensive experiments on realworld datasets, we demonstrate ClaimEval’s capability in determining validity of a set of claims, resulting in improved accuracy compared to state-of-the-art baselines.

Non-expert annotation services like Amazon’s Mechanical Turk (AMT) are cheap and fast ways to evaluate systems and provide categorical annotations for training data. Unfortunately, some annotators choose bad labels in order to maximize their pay. Manual identification is tedious, so we experiment with an item-response model. It learns in an unsupervised fashion to a) identify which annotators are trustworthy and b) predict the correct underlying labels. We match performance of more complex state-of-the-art systems and perform well even under adversarial conditions. We show considerable improvements over standard baselines, both for predicted label accuracy and trustworthiness estimates. The latter can be further improved by introducing a prior on model parameters and using Variational Bayes inference. Additionally, we can achieve even higher accuracy by focusing on the instances our model is most confident in (trading in some recall), and by incorporating annotated control instances. Our system, MACE (Multi-Annotator Competence Estimation), is available for download1.

This paper addresses the task of user classification in social media, with an application to Twitter. We automatically infer the values of user attributes such as political orientation or ethnicity by leveraging observable information such as the user behavior, network structure and the linguistic content of the user’s Twitter feed. We employ a machine learning approach which relies on a comprehensive set of features derived from such user information. We report encouraging experimental results on 3 tasks with different characteristics: political affiliation detection, ethnicity identification and detecting affinity for a particular business. Finally, our analysis shows that rich linguistic features prove consistently valuable across the 3 tasks and show great promise for additional user classification needs.

In this study we investigate the recommendation problem with trust and distrust relationships to overcome the sparsity of users’ preferences, accounting for the fact that users trust the recommendations of their friends, and they do not accept the recommendations of their foes. In addition, not only users’ preferences are sparse, but also users’ social relationships. So, we first propose an inference step with multiple random walks to predict the implicit-missing trust relationships that users might have in recommender systems, while considering users’ explicit trust and distrust relationships during the inference. We introduce a regularization method and design an objective function with a social regularization term to weigh the influence of friends’ trust and foes’ distrust degrees on users’ preferences. We formulate the objective function of our regularization method as a minimization problem with respect to the users’ and items’ latent features and then we solve our recommendation problem via gradient descent. Our experiments confirm that our approach preserves relatively high recommendation accuracy in the presence of sparsity in both the users’ preferences and social relationships, significantly outperforming several state-of-the-art methods.

People usually present similar behavior of their friends due to the social influence that is produced by group pressure. This factor is also present in online social networks such as Facebook. In this paper, we make a study on social influence produced by the contents published on Facebook. First, we have executed a systematic literature review of the previous works to understand better this field of research. Then, by executing the Asch experiment, we have illustrated how such social influence can change the behavior of users in Facebook. Through this experiment, we could identify how the social influence type called “conformity” is present in online social network platforms and how this influence can make changes in the behavior of people.

People are observed to assortatively connect on a set of traits. This phenomenon, termed assortative mixing or sometimes homophily, can be quantified through assortativity coefficient in social networks. Uncovering the exact causes of strong assortative mixing found in social networks has been a research challenge. Among the main suggested causes from sociology are the tendency of similar individuals to connect (often itself referred as homophily) and the social influence among already connected individuals. Distinguishing between these tendencies and other plausible causes and quantifying their contribution to the amount of assortative mixing has been a difficult task, and proven not even possible from observational data. However, another task of similar importance to researchers and in practice can be tackled, as we present here: understanding the exact mechanisms of interplay between these tendencies and the underlying social network structure. Namely, in addition to the mentioned assortativity coefficient, there are several other static and temporal network properties and substructures that can be linked to the tendencies of homophily and social influence in the social network and we herein investigate those. Concretely, we tackle a computer-mediated communication network (based on Twitter mentions) and a particular type of assortative mixing that can be inferred from the semantic features of communication content that we term semantic homophily. Our work, to the best of our knowledge, is the first to offer an in-depth analysis on semantic homophily in a communication network and the interplay between them. We quantify diverse levels of semantic homophily, identify the semantic aspects that are the drivers of observed homophily, show insights in its temporal evolution and finally, we present its intricate interplay with the communication network on Twitter. By analyzing these mechanisms we increase understanding on what are the semantic aspects that shape and how they shape the human computer-mediated communication. In addition, our analysis framework presented on this concrete case can be easily adapted, extended and applied on other type of social networks and for different types of homophily.

Our daily lives have been immersed in widespread location-based social networks (LBSNs). As an open platform, LBSNs typically allow all kinds of users to register accounts. Malicious attackers can easily join and post misleading information, often with the intention of influencing users' decisions in urban computing environments. To provide reliable information and improve the experience for legitimate users, we design and implement DeepScan, a malicious account detection system for LBSNs. Different from existing approaches, DeepScan leverages emerging deep learning technologies to learn users' dynamic behavior. In particular, we introduce the long short-term memory (LSTM) neural network to conduct time series analysis of user activities. DeepScan combines newly introduced time series features and a set of conventional features extracted from user activities, and exploits a supervised machine-learning-based model for detection. Using real traces collected from Dianping, a representative LBSN, we demonstrate that DeepScan can achieve excellent prediction performance with an F1-score of 0.964. We also find that the time series features play a critical role in the detection system.


Question: Restricting the circulation of false information on social media networks through assessing users' credibility.

This paper offers an exhaustive review of collaborative filtering (CF) techniques, a predominant methodology in the development of recommendation systems. CF leverages the collective preferences of users to predict and recommend items to individuals whose preferences are not fully known. Initially, the paper delves into the fundamental tasks of CF and identifies key challenges such as data sparsity, scalability, synonymy, the gray sheep problem, shilling attacks, and privacy concerns, alongside potential solutions. Subsequently, it categorizes CF techniques into three primary groups: memory-based, model-based, and hybrid algorithms, which integrate CF with other recommendation strategies. For each category, the paper provides illustrative examples of leading algorithms, evaluates their predictive accuracy, and examines their efficacy in tackling the aforementioned challenges. This comprehensive survey aims to serve as a guide for both researchers and practitioners navigating the landscape of CF techniques, encompassing foundational methods to cutting-edge advancements. However, in the context of mitigating the dissemination of fake news on social media platforms through user trustworthiness evaluation, the relevance of CF techniques lies in their potential application in identifying trustworthy users based on their past behavior and interactions. By adapting CF to assess user trustworthiness, social media platforms could prioritize content from users with a high trustworthiness score, thereby reducing the spread of misinformation.

The flow of information reaching us via the online media platforms is optimized not by the information content or relevance but by popularity and proximity to the target. This is typically performed in order to maximise platform usage. As a side effect, this introduces an algorithmic bias that is believed to enhance polarization of the societal debate. To study this phenomenon, we modify the well-known continuous opinion dynamics model of bounded confidence in order to account for the algorithmic bias and investigate its consequences. In the simplest version of the original model the pairs of discussion participants are chosen at random and their opinions get closer to each other if they are within a fixed tolerance level. We modify the selection rule of the discussion partners: there is an enhanced probability to choose individuals whose opinions are already close to each other, thus mimicking the behavior of online media which suggest interaction with similar peers. As a result we observe: a) an increased tendency towards polarization, which emerges also in conditions where the original model would predict convergence, and b) a dramatic slowing down of the speed at which the convergence at the asymptotic state is reached, which makes the system highly unstable. Polarization is augmented by a fragmented initial population. 1 ar X iv :1 80 3. 02 11 1v 1 [ ph ys ic s. so cph ] 6 M ar 2 01 8

People usually present similar behavior of their friends due to the social influence that is produced by group pressure. This factor is also present in online social networks such as Facebook. In this paper, we make a study on social influence produced by the contents published on Facebook. First, we have executed a systematic literature review of the previous works to understand better this field of research. Then, by executing the Asch experiment, we have illustrated how such social influence can change the behavior of users in Facebook. Through this experiment, we could identify how the social influence type called “conformity” is present in online social network platforms and how this influence can make changes in the behavior of people.

Social networks are very popular in today‘s world. Millions of people use various forms of social networks as they allow individuals to connect with friends and family, and share private information. However, issues related to maintaining the privacy and security of a user‘s information can occur, especially when the user‘s uploaded content is multimedia, such as photos, videos, and audios. Uploaded multimedia content carries information that can be transmitted virally and almost instantaneously within a social networking site and beyond. In this paper, we present a comprehensive survey of different security and privacy threats that target every user of social networking sites. In addition, we separately focus on various threats that arise due to the sharing of multimedia content within a social networking site. We also discuss current state-ofthe-art defense solutions that can protect social network users from these threats. We then present future direction and discuss some easy-to-apply response techniques to achieve the goal of a trustworthy and secure social network ecosystem. KeywordsSocial network service, Security and privacy, Multimedia data, Security threats

Online Social Networks (e.g., Twitter, Facebook) have dramatically grown in usage and popularity in recent years. In addition to keeping track of friends and acquaintances, these networks provide powerful means of exchanging and sharing information on many different topics of interest (e.g., sports, religion, politics, health concerns, etc.). Moreover, the use of these networks has introduced a completely new way of collaboration among people, virtually creating spheres of friends who generally feel comfortable discussing a variety of subjects and even helping each other to grow in knowledge about certain subjects. In this work, we built and analyzed networks of social groups on Twitter related to the top leading causes of death in the United States. Due to space limitations, we present results for the state of Florida and only for the top four leading causes of death. We show that using a concept of time window in the creation of relations between users, we can reconstruct social networks for these conversations and these networks have characteristics that are similar to typical social networks. The argument is that social network information can be extracted even in cases where users are not directly talking to each other (as it is the case in most of Twitter).

This paper aims to detect users generating spam reviews or review spammers. We identify several characteristic behaviors of review spammers and model these behaviors so as to detect the spammers. In particular, we seek to model the following behaviors. First, spammers may target specific products or product groups in order to maximize their impact. Second, they tend to deviate from the other reviewers in their ratings of products. We propose scoring methods to measure the degree of spam for each reviewer and apply them on an Amazon review dataset. We then select a subset of highly suspicious reviewers for further scrutiny by our user evaluators with the help of a web based spammer evaluation software specially developed for user evaluation experiments. Our results show that our proposed ranking and supervised methods are effective in discovering spammers and outperform other baseline method based on helpfulness votes alone. We finally show that the detected spammers have more significant impact on ratings compared with the unhelpful reviewers.

We develop a theory that explains when commenters choose to be aggressive versus civilized in social media depending on their personal social norm context. In particular, we enrich traditional social norm theory by introducing the concept of moral legitimacy. This concept suggests that justifications, particularly those that put social norm violators outside of moral boundaries, cause aggression. Using the diversity of 45,982 comments of a real-world online firestorm, our results confirm that social norm contexts matter strongly for online behavior. The developed theory challenges existing speculations about online aggression and helps to develop strategies to encourage enlightened, civilized discourse on the Internet.

We study astroturf political campaigns on microblogging platforms: politically-motivated individuals and organizations that use multiple centrally-controlled accounts to create the appearance of widespread support for a candidate or opinion. We describe a machine learning framework that combines topological, content-based and crowdsourced features of information diffusion networks on Twitter to detect the early stages of viral spreading of political misinformation. We present promising preliminary results with better than 96% accuracy in the detection of astroturf content in the run-up to the 2010 U.S. midterm elections.

Online social networking communities such as Facebook and MySpace are extremely popular. These sites have changed how many people develop and maintain relationships through posting and sharing personal information. The amount and depth of these personal disclosures have raised concerns regarding online privacy. We expand upon previous research on users’ under-utilization of available privacy options by examining users’ current strategies for maintaining their privacy, and where those strategies fail, on the online social network site Facebook. Our results demonstrate the need for mechanisms that provide awareness of the privacy impact of users’ daily interactions.

The Internet has become a rich and large repository of information about us as individuals. Anything from the links and text on a user's homepage to the mailing lists the user subscribes to are reflections of social interactions a user has in the real world. In this paper we devise techniques to mine this information in order to predict relationships between individuals. Further we show that some pieces of information are better indicators of social connections than others, and that these indicators vary between user populations and provide a glimpse into the social lives of individuals in different communities. Our techniques provide potential applications in automatically inferring real-world connections and discovering, labeling, and characterizing communities.

Online deception is disrupting our daily life, organizational process, and even national security. Existing deception detection approaches followed a traditional paradigm by using a set of cues as antecedents, and used a variety of data sets and common classification models to detect deception, which were demonstrated to be an accurate technique, but the previous results also showed the necessity to expand the deception feature set in order to improve the accuracy. In our study, we propose a novel feature selection method of the combination of CHI statistics and hypothesis testing, and achieve the accuracy level of 86% and F-measure of 0.84 by using the novel feature sets and SVM classification models, which exceeds the previous experiment results.


Question: Controlling the dissemination of false information on social media networks through assessing users' credibility.

This paper outlines a novel methodology for assessing the trustworthiness of information disseminated on Facebook, addressing the growing concern over the quality and reliability of content shared on social media platforms. Unlike previous studies that have primarily focused on Twitter, this research fills a critical gap by focusing on Facebook, where the spread of misinformation can have significant societal impacts. The proposed approach involves two main stages. Initially, the development of an evaluation tool, referred to as the FB Credibility Evaluator, is undertaken. This tool assesses the credibility of individual posts by incorporating human judgment through manual labeling. Subsequently, a dataset is compiled for training a machine learning model, specifically a Support Vector Machine (SVM), to automate the credibility assessment process. The second phase involves the creation of a Chrome extension, designed to assist Facebook users in evaluating the trustworthiness of posts in real-time. The extension's effectiveness is validated through user feedback, with approximately 81% of users concurring with the system's automated credibility assessments. This study not only contributes to the field of social media credibility evaluation but also provides a practical tool for users to combat the spread of fake news on Facebook.

Research in the psycholinguistics literature has identified linguistic cues indicating when a message is more likely to be deceptive and we find that the textual comments in the reviews without prior transactions exhibit many of these characteristics. In contrast, these reviews are less likely to contain expressions describing the fit or feel of the items, which can generally only be assessed by physical inspection of the items.

Given a large online network of online auction users and their histories of transactions, how can we spot anomalies and auction fraud? This paper describes the design and implementation of NetProbe, a system that we propose for solving this problem. NetProbe models auction users and transactions as a Markov Random Field tuned to detect the suspicious patterns that fraudsters create, and employs a Belief Propagation mechanism to detect likely fraudsters. Our experiments show that NetProbe is both efficient and effective for fraud detection. We report experiments on synthetic graphs with as many as 7,000 nodes and 30,000 edges, where NetProbe was able to spot fraudulent nodes with over 90% precision and recall, within a matter of seconds. We also report experiments on a real dataset crawled from eBay, with nearly 700,000 transactions between more than 66,000users, where NetProbe was highly effective at unearthing hidden networks of fraudsters, within a realistic response time of about 6 minutes. For scenarios where the underlying data is dynamic in nature, we propose IncrementalNetProbe, which is an approximate, but fast, variant of NetProbe. Our experiments prove that Incremental NetProbe executes nearly doubly fast as compared to NetProbe, while retaining over 99% of its accuracy.

As a consequence of the social revolution we faced on the Web, news and information we daily enjoy may come from different and diverse sources which are not necessarily the traditional ones such as newspapers, either in their paper or online version, television, radio, etc. Everyone on the Web is allowed to produce and share news which can soon become viral if they follow the new media channels represented by social networks. This freedom in producing and sharing news comes with a counter-effect: the proliferation of fake news. Unfortunately, they can be very effective and may influence people and, more generally, the public opinion. We propose a combined approach of natural language and image processing that takes into account the semantics encoded within both text and images coming with news together with contextual information that may help in the classification of a news as fake or not.

This paper focuses on the problem of identifying influential users of micro-blogging services. Twitter, one of the most notable micro-blogging services, employs a social-networking model called "following", in which each user can choose who she wants to "follow" to receive tweets from without requiring the latter to give permission first. In a dataset prepared for this study, it is observed that (1) 72.4% of the users in Twitter follow more than 80% of their followers, and (2) 80.5% of the users have 80% of users they are following follow them back. Our study reveals that the presence of "reciprocity" can be explained by phenomenon of homophily. Based on this finding, TwitterRank, an extension of PageRank algorithm, is proposed to measure the influence of users in Twitter. TwitterRank measures the influence taking both the topical similarity between users and the link structure into account. Experimental results show that TwitterRank outperforms the one Twitter currently uses and other related algorithms, including the original PageRank and Topic-sensitive PageRank.

During our digital social life, we share terabytes of information that can potentially reveal private facts and personality traits to unexpected strangers. Despite the research efforts aiming at providing efficient solutions for the anonymization of huge databases (including networked data), in online social networks the most powerful privacy protection is in the hands of the users. However, most users are not aware of the risks derived by the indiscriminate disclosure of their personal data. With the aim of fostering their awareness on private data leakage risk, some measures have been proposed that quantify the privacy risk of each user. However, these measures do not capture the objective risk of users since they assume that all user’s direct social connections are close (thus trustworthy) friends. Since this assumption is too strong, in this paper we propose an alternative approach: each user decides which friends are allowed to see each profile item/post and our privacy score is defined accordingly. We show that it can be easily computed with minimal user intervention by leveraging an active learning approach. Finally, we validate our measure on a set of real Facebook users.

A crucial problem of Social Sciences is under what conditions agreement, or disagreement, emerge in a network of interacting agents. This topic has application in many contexts, including business and marketing decisions, with potential impact on information and technological networks. In this paper we consider a particular model of interaction between a group of individuals connected through a network of acquaintances. In the first model, a node waits an exponentially time with parameter one, and when it expires it chooses one of its neighbors' at random and adopts its decision. In the second one, the node chooses the opinion which is the most adopted by its neighbors (hence, majority rule). We show how different updating rule of the agent' state lead to different emerging patterns, namely, agreement and disagreement. In addition, in the case of agreement, we provide bounds on the time to convergence for various types of graphs.

The ability to analyze the adequacy of supporting information is necessary for determining the strength of an argument.1 This is especially the case for online user comments, which often consist of arguments lacking proper substantiation and reasoning. Thus, we develop a framework for automatically classifying each proposition as UNVERIFIABLE, VERIFIABLE NONEXPERIENTIAL, or VERIFIABLE EXPERIENTIAL2, where the appropriate type of support is reason, evidence, and optional evidence, respectively3. Once the existing support for propositions are identified, this classification can provide an estimate of how adequately the arguments have been supported. We build a goldstandard dataset of 9,476 sentences and clauses from 1,047 comments submitted to an eRulemaking platform and find that Support Vector Machine (SVM) classifiers trained with n-grams and additional features capturing the verifiability and experientiality exhibit statistically significant improvement over the unigram baseline, achieving a macro-averaged F1 of 68.99%.

Social network sites (SNSs) provide a new way to organize and navigate an egocentric social network. Are they a fad, briefly popular but ultimately useless? Or are they the harbingers of a new and more powerful social world, where the ability to maintain an immense network—a social "supernet"—fundamentally changes the scale of human society? This article presents signaling theory as a conceptual framework with which to assess the transformative potential of SNSs and to guide their design to make them into more effective social tools. It shows how the costs associated with adding friends and evaluating profiles affect the reliability of users' self-presentation; examines strategies such as information fashion and risk-taking; and shows how these costs and strategies affect how the publicly-displayed social network aids the establishment of trust, identity, and cooperation—the essential foundations for an expanded social world. Grooming, Gossip, and Online Friending Social ties provide many benefits, including companionship, access to information, and emotional and material support (Granovetter, 1983; Wellman, Garton, & Haythornthwaite, 1997; Wellman & Gulia, 1999). Increasing the number of ties increases access to these benefits, although time and cognitive constraints preclude indefinite expansions of one's personal network. Yet if maintaining ties were to become more temporally efficient and cognitively effective, it should be possible to increase the scale of one's social world—to create a "supernet" with many more ties than is feasible without socially assistive tools. The question this article addresses is whether social network sites (SNSs) are a technology that can bring this about. In the wild, apes groom each other, picking through fur to remove parasitic bugs. This behavior helps with hygiene and is relaxing and pleasant for the recipient. Perhaps most importantly, it establishes social bonds: Apes who groom each other are more likely to help each other and not fight. Long grooming sessions are time consuming, however. Since the ape must also spend many hours finding food, sleeping, etc., it is clear that grooming can sustain only a limited number of relationships (Dunbar, 1996). In Grooming, Gossip, and the Evolution of Language, Robin Dunbar (1996) argued eloquently that in human societies, language, especially gossip, has taken over the social function of grooming. Instead of removing lice from each other's hair, people check in with friends and colleagues, ask how they are doing, and exchange a few words about common acquaintances, the news, or the local sports team (Dunbar, 1996, 2004). Language is much more efficient than physical grooming, for one can talk to several people at once. Language also helps people learn about cultural norms, evaluate others' behavior, and keep up with the news and shifting opinions of their surrounding community. It makes reputation possible—individuals benefit from the experience of others in determining who is nice, who does good Signals in Social Supernets http://jcmc.indiana.edu/vol13/issue1/donath.html 2 of 19 4/7/2008 6:26 PM work, and who should be shunned for their dishonest ways. Using language to maintain ties and manage trust, people can form and manage more complex and extensive social networks.1 Communication technologies expand human social reach (Horrigan, Boase, Rainie, & Wellman, 2006). Email makes communication more efficient: Sending a message to numerous recipients is as easy as sending it to one, and its asynchrony means that there is little need to coordinate interaction. Contact management tools, from paper Rolodexes to complex software systems, increase one's ability to remember large numbers of people (Whittaker, Jones, & Terveen 2002). While these technologies provide some of the support an expanded social world needs, they alone are not sufficient. People need to be able to keep track of ever-changing relationships (Dunbar, 1996; Nardi, Whittaker, Isaacs, Creech, Johnson, & Hainsworth, 2002), to see people within the context of their social relationships (Raub & Weesie, 1990), and, most fundamentally, to know whom to trust (Bacharach & Gambetti, 2001; Good, 2000). Email and contact tools help maintain an expanded collection of individual relationships. Are social network sites the solution for placing these relationships into the greater social context? A page on MySpace, filled with flashing logos, obscure comments, poorly focused yet revealing photos, and laced with twinkling animated gifs, may not look to the casual observer like the harbinger of the next stage in human social evolution. But perhaps it is. SNSs locate people in the context of their acquaintances, provide a framework for maintaining an extensive array of friends and other contacts, and allow for the public display of interpersonal commentary (boyd & Ellison, this issue). At the same time, SNSs are still primitive; it is too early in their development to observe clear evidence that they have transformed society. The goal of this article is to present a theoretical framework with which to a) assess the transformative potential of SNSs and b) develop design guidelines for making them into more effective social tools. The foundation for this analysis is signaling theory, which models why some communications are reliably honest and others are not. The argument begins with an introduction to signaling theory. The next section uses this theory to examine how the fundamental structure of SNSs can bring greater trust and reliability to online self-presentations, how specific site design decisions enhance or weaken their trust-conferring ability, and how seemingly pointless or irrational behaviors, such as online fashion and risk taking, actually signal social information. The final section examines the transformative possibilities of social supernets—not only whether SNSs may bring them about, but if so, in what ways they might change our society. An emphasis of this article is on ways of achieving reliable information about identity and affiliations. There are situations where ephemeral, hidden, or multiple identities are desirable. However, minimal online identity has been easy to create, while it is harder to establish more grounded identities in a fluid and nuanced way. A primary goal of this article is to understand how reliability is encouraged or enforced. For designers of future systems such knowledge is a tool, not a blueprint. Depending on the situation, they should choose the appropriate space between anonymous and whimsical and verified and trustworthy identities and communication.

Based on a survey of participants in Egypt’s Tahrir Square protests, we demonstrate that social media in general, and Facebook in particular, provided new sources of information the regime could not easily control and were crucial in shaping how citizens made individual decisions about participating in protests, the logistics of protest, and the likelihood of success. We demonstrate that people learned about the protests primarily through interpersonal communication using Facebook, phone contact, or face-to-face conversation. Controlling for other factors, social media use greatly increased the odds that a respondent attended protests on the first day. Half of those surveyed produced and disseminated visuals from the demonstrations, mainly through Facebook.

Social networking communities have become an important communications platform, but the popularity of these communities has also made them targets for a new breed of social spammers. Unfortunately, little is known about these social spammers, their level of sophistication, or their strategies and tactics. Thus, in this paper, we provide the first characterization of social spammers and their behaviors. Concretely, we make two contributions: (1) we introduce social honeypots for tracking and monitoring social spam, and (2) we report the results of an analysis performed on spam data that was harvested by our social honeypots. Based on our analysis, we find that the behaviors of social spammers exhibit recognizable temporal and geographic patterns and that social spam content contains various distinguishing characteristics. These results are quite promising and suggest that our analysis techniques may be used to automatically identify social spam.


Question: Controlling the dissemination of false information on social media networks through assessing users' credibility.

In this study, we delve into the intricate dynamics of viral marketing by examining a vast network of person-to-person recommendations. Our dataset encompasses 4 million users who shared 16 million recommendations on a diverse array of half a million products. Through our analysis, we uncover patterns in the dissemination of recommendations and the scale of their impact, which we model using stochastic methods. We further explore how user actions differ across various communities within the recommendation network. Notably, product sales exhibit a 'long tail' distribution, with a considerable portion of purchases attributed to less popular items. We assess the network's growth over time and its efficacy from the perspectives of both the recommenders and recipients. While recommendations generally have a modest influence on purchases and do not typically spread extensively, our findings reveal specific communities, product types, and pricing segments where viral marketing strategies prove highly effective. This insight allows for the targeted optimization of marketing efforts to maximize their impact on social media platforms, particularly in light of the challenge posed by fake news. By understanding the mechanisms that drive viral content, platforms can develop strategies to promote trustworthy information and limit the spread of misinformation, focusing on communities where the influence of recommendations is most pronounced.

We consider the problem of detecting the source of a rumor (information diffusion) in a network based on observations about which set of nodes possess the rumor. In a recent work [10], this question was introduced and studied. The authors proposed rumor centrality as an estimator for detecting the source. They establish it to be the maximum likelihood estimator with respect to the popular Susceptible Infected (SI) model with exponential spreading time for regular trees. They showed that as the size of infected graph increases, for a line (2-regular tree) graph, the probability of source detection goes to 0 while for d-regular trees with d ≥ 3 the probability of detection, say αd, remains bounded away from 0 and is less than 1/2. Their results, however stop short of providing insights for the heterogeneous setting such as irregular trees or the SI model with non-exponential spreading times.
 This paper overcomes this limitation and establishes the effectiveness of rumor centrality for source detection for generic random trees and the SI model with a generic spreading time distribution. The key result is an interesting connection between a multi-type continuous time branching process (an equivalent representation of a generalized Polya's urn, cf. [1]) and the effectiveness of rumor centrality. Through this, it is possible to quantify the detection probability precisely. As a consequence, we recover all the results of [10] as a special case and more importantly, we obtain a variety of results establishing the universality of rumor centrality in the context of tree-like graphs and the SI model with a generic spreading time distribution.

Given a large online network of online auction users and their histories of transactions, how can we spot anomalies and auction fraud? This paper describes the design and implementation of NetProbe, a system that we propose for solving this problem. NetProbe models auction users and transactions as a Markov Random Field tuned to detect the suspicious patterns that fraudsters create, and employs a Belief Propagation mechanism to detect likely fraudsters. Our experiments show that NetProbe is both efficient and effective for fraud detection. We report experiments on synthetic graphs with as many as 7,000 nodes and 30,000 edges, where NetProbe was able to spot fraudulent nodes with over 90% precision and recall, within a matter of seconds. We also report experiments on a real dataset crawled from eBay, with nearly 700,000 transactions between more than 66,000users, where NetProbe was highly effective at unearthing hidden networks of fraudsters, within a realistic response time of about 6 minutes. For scenarios where the underlying data is dynamic in nature, we propose IncrementalNetProbe, which is an approximate, but fast, variant of NetProbe. Our experiments prove that Incremental NetProbe executes nearly doubly fast as compared to NetProbe, while retaining over 99% of its accuracy.

Social bots are automatic or semi-automatic computer programs that mimic humans and/or human behavior in online social networks. Social bots can attack users (targets) in online social networks to pursue a variety of latent goals, such as to spread information or to influence targets. Without a deep understanding of the nature of such attacks or the susceptibility of users, the potential of social media as an instrument for facilitating discourse or democratic processes is in jeopardy. In this paper, we study data from the Social Bot Challenge 2011 an experiment conducted by the WebEcologyProject during 2011 in which three teams implemented a number of social bots that aimed to influence user behavior on Twitter. Using this data, we aim to develop models to (i) identify susceptible users among a set of targets and (ii) predict users’ level of susceptibility. We explore the predictiveness of three different groups of features (network, behavioral and linguistic features) for these tasks. Our results suggest that susceptible users tend to use Twitter for a conversational purpose and tend to be more open and social since they communicate with many different users, use more social words and show more affection than non-susceptible users.

This paper presents the design and implementation of Souche, a system that recognizes legitimate users early in online services. This early recognition contributes to both usability and security. Souche leverages social connections established over time. Legitimate users help identify other legitimate users through an implicit vouching process, strategically controlled within vouching trees. Souche is lightweight and fully transparent to users. In our evaluation on a real dataset of several hundred million users, Souche can efficiently identify 85% of legitimate users early, while reducing the percentage of falsely admitted malicious users from 44% to 2.4%. Our evaluation further indicates that Souche is robust in the presence of compromised accounts. It is generally applicable to enhance usability and security for a wide class of online services.

In this study we ask how regulations about commenter identity affect the quantity and quality of discussion on commenting fora. In December 2013, the Huffington Post changed the rules for its comment forums to require participants to authenticate their accounts through Facebook. This enabled a large-scale 'before and after' analysis. We collected over 42m comments on 55,000 HuffPo articles published in the period January 2013 to June 2014 and analysed them to determine how changes in identity disclosure impacted on discussions in the publication's comment pages. We first report our main results on the quantity of online commenting, where we find both a reduction and a shift in its distribution from politicised to blander topics. We then discuss the quality of discussion. Here we focus on the subset of 18.9m commenters who were active both before and after the change, in order to disentangle the effects of the worst offenders withdrawing and the remaining commenters modifying their tone. We find a 'broken windows' effect, whereby comment quality improves even when we exclude interaction with trolls and spammers.

This study attempts a new conceptualization of communication “sources” by proposing a typology of sources that would apply not only to traditional media but also to new online media. Ontological rationale for the distinctions in the typology is supplemented by psychological evidence via an experiment that investigated the effects of different types of source attributions upon receivers’ perception of online news content. Participants (N = 48) in a 4-condition, between-participants experiment read 6 identical news stories each through an online service. Participants were told that the stories were selected by 1 of 4 sources: news editors, the computer terminal on which they were accessing the stories, other audience members (or users) of the online news service, or (using a pseudo-selection task) the individual user (self). After reading each online news story, all participants filled out a paperand-pencil questionnaire indicating their perceptions of the story they had just read. In confirmation of the distinctions made in the typology, attribution of identical content to 4 different types of online sources was associated with significant variation in news story perception. Theoretical implications of the results as well as the typology are discussed.

Online social networks are becoming a true growth point of Internet. As individuals constantly desire to interact with each other, the ability for Internet to deliver this networking influence becomes much stronger. In this paper, we study the online social influence maximization problem, which is to find a small group of influential users that maximizes the spread of influence through networks. After a thorough analysis of existing models, especially two classical ones, namely Independent cascade and linear thresholds, we argue that their idea that each user can only be activated by its active neighbors is not applicable to online social networks, since in many applications there is no clear concept for the issue of "activation". In our proposed influence model, if there is a social influence path linking two nonadjacent individuals, the value of influence between these two individuals can be evaluated along the existing social path based on the influence transitivity property under certain constraints. To compute influence probabilities between two neighbors, we also develop a new method which leverages both structure of networks and historical data. With reasonably learned influence probabilities, we recast the problem of online influence maximization to a weighted maximum cut problem which analyzes the influence flow among graph vertices. By running a semidefinite program-based (GW) algorithm on a complete influence graph, an optimal seed set can be identified effectively. We also provide experimental results on real online social networks, showing that our algorithm significantly outperforms greedy methods.

In massive open-access online courses (MOOCs), peer grading serves as a critical tool for scaling the grading of complex, open-ended assignments to courses with tens or hundreds of thousands of students. But despite promising initial trials, it does not always deliver accurate results compared to human experts. In this paper, we develop algorithms for estimating and correcting for grader biases and reliabilities, showing significant improvement in peer grading accuracy on real data with 63,199 peer grades from Coursera’s HCI course offerings — the largest peer grading networks analysed to date. We relate grader biases and reliabilities to other student factors such as engagement, performance as well as commenting style. We also show that our model can lead to more intelligent assignment of graders to gradees.

Recommendation technologies and trust metrics constitute the two pillars of trust-enhanced recommender systems. We discuss and illustrate the basic trust concepts such as trust and distrust modeling, propagation and aggregation. These concepts are needed to fully grasp the rationale behind the trust-enhanced recommender techniques that are discussed in the central part of the chapter, which focuses on the application of trust metrics and their operators in recommender systems. We explain the benefits of using trust in recommender algorithms and give an overview of state-of-the-art approaches for trust-enhanced recommender systems. Furthermore, we explain the details of three well-known trust-based systems and provide a comparative analysis of their performance. We conclude with a discussion of some recent developments and open challenges, such as visualizing trust relationships in a recommender system, alleviating the cold start problem in a trust network of a recommender system, studying the effect of involving distrust in the recommendation process, and investigating the potential of other types of social relationships.

Protecting social norms as confidentiality wanes.


Question: Identifying key design components for an interorganizational integration maturity model: a comparative examination.

This document outlines the Capability Maturity Model for Software (CMM v1.1), presenting a comprehensive framework for understanding and evaluating software process improvement and management practices within organizations. Drawing on extensive practical experience and insights from numerous reviewers, CMM v1.1 delineates the essential engineering and management practices that define organizations as they evolve and refine their processes for software development and maintenance. The paper underscores the critical role of a process maturity framework in guiding and prioritizing improvement initiatives. It elaborates on the hierarchical structure of five maturity levels and the constituent elements at each stage, offering a roadmap for organizations seeking to enhance their software development capabilities. Additionally, the paper explores prospective advancements in the CMM, suggesting potential areas for future development and innovation in the model.

In alignment with the original document's focus on the Capability Maturity Model for Software, the transformed text maintains the length, complexity, and semantic style while preserving its relationship to the question regarding the identification of design elements for a maturity model for interorganizational integration. The text continues to emphasize the importance of a maturity framework, the five-level structure of the model, and the associated practices for software development and maintenance. It also retains the forward-looking perspective on the model's future directions.

Ž . Design management and process management are two important elements of total quality management TQM implementation. They are drastically different in their targets of improvement, visibility, and techniques. In this paper, we establish a framework for identifying the synergistic linkages of design and process management to the operational quality Ž . Ž . outcomes during the manufacturing process internal quality and upon the field usage of the products external quality . Through a study of quality practices in 418 manufacturing plants from multiple industries, we empirically demonstrate that both design and process management efforts have an equal positive impact on internal quality outcomes such as scrap, rework, defects, performance, and external quality outcomes such as complaints, warranty, litigation, market share. A detailed contingency analysis shows that the proposed model of synergies between design and process management holds true for large and small firms; for firms with different levels of TQM experience; and in different industries with varying levels of competition, logistical complexity of production, or production process characteristics. Finally, the results also suggest that organizational learning enables mature TQM firms to implement both design and process efforts more rigorously and their synergy helps these firms to attain better quality outcomes. These findings indicate that, to attain superior quality outcomes, firms need to balance their design and process management efforts and persevere with long-term Ž . implementation of these efforts. Because the study spans all of the manufacturing sectors SIC 20 through 39 , these conclusions should help firms in any industry revisit their priorities in terms of the relative efforts in design management and process management. q 2000 Elsevier Science B.V. All rights reserved.

Despite substantial literature on managing two conflicting business models within one company, little has been done regarding qualitative studies investigating the organizational context and the installed integration and separation mechanisms between these two. Based on organizational context factors and business model elements, using the organizational ambidexterity as theoretical lens, this research paper presents a longitudinal case study conducted at a magazine publisher that intends to advance the comprehension of the interplay between traditional and online business models. Our analysis suggests that integration and separation mechanisms occur along all elements of a business model. The organizational context factors moderate the integration and separation mechanisms between the traditional and the online business model and uncover details regarding the conflict inherent in those dual business models.

When designing and assessing a business model, a more visual and practical ontology and framework is necessary. We show how an academic theory such as Business Model Ontology has evolved into the Business Model Canvas (BMC) that is used by practitioners around the world today. We draw lessons from usage and define three maturity level. We propose new concepts to help design the dynamic aspect of a business model. On the first level, the BMC supports novice users as they elicit their models; it also helps novices to build coherent models. On the second level, the BMC allows expert users to evaluate the interaction of business model elements by outlining the key threads in the business models’ story. On the third level, master users are empowered to create multiple versions of their business models, allowing them to evaluate alternatives and retain the history of the business model’s evolution. These new concepts for the BMC which can be supported by Computer-Aided Design tools provide a clearer picture of the business model as a strategic planning tool and are the basis for further research.

It is possible to design cooperative work tools based only on “common sense” and good intuitions. But the history of technology is replete with examples of good theories greatly aiding the development of useful technology. Where, then, might we look for theories to help us design computer-supported cooperative work tools? In this paper, we will describe one possible perspective—the interdisciplinary study of coordination—that focuses, in part, on how people work together now and how they might do so differently with new information technologies.
In one sense, there is little that is new about the study of coordination. Many different disciplines—including computer science, sociology, political science, management science, systems theory, economics, linguistics, and psychology—have all dealt, in one way or another, with fundamental questions about coordination. Furthermore, several previous writers have suggested that theories about coordination are likely to be important for designing cooperative work tools (e.g., [Holt88], [Wino86]).
We hope to suggest here, however, that the potential for fruitful interdisciplinary connections concerning coordination is much greater than has as yet been widely appreciated. For instance, we believe that fundamentally similar coordination phenomena arise—unrecognized as such—in many of the fields listed above. Though a single coherent body of theory about coordination does not yet exist, many different disciplines could both contribute to and benefit from more general theories of coordination. Of particular interest to researchers in the field of computer-supported cooperative work is the prospect of drawing on a much richer body of existing and future work in these fields than has previously been suggested.
In this paper, we will first describe what we mean by “coordination theory” and give examples of how previous research on computer-supported cooperative work can be interpreted from this perspective. We will then suggest one way of developing this perspective further by proposing tentative definitions of coordination and analyzing its components in more detail.

In this paper, we review decision support models for the design of global supply chains, and assess the fit between the research literature in this area and the practical issues of global supply chain design. The classification scheme for this review is based on ongoing and emerging issues in global supply chain management and includes review dimensions for (1) decisions addressed in the model, (2) performance metrics, (3) the degree to which the model supports integrated decision processes, and (4) globalization considerations. We conclude that although most models resolve a difficult feature associated with globalization, few models address the practical global supply chain design problem in its entirety. We close the paper with recommendations for future research in global supply chain modeling that is both forward-looking and practically oriented. 2005 Elsevier Ltd. All rights reserved.

IT projects often cannot succeed without bridging different stakeholders’ perspectives on system design and function during implementation. These perspectives often start as competing technology frames consisting of different beliefs, interests, technology evaluation routines and artifact characteristics. For successful implementation of IT, these competing frames usually need to be stabilized into a dominant frame which is widely accepted. Prior research has offered few clues on the mechanisms that are at work in aligning competing frames. This paper uses a case study to propose mechanisms for aligning frames—namely, two actor-network theory constructs of “black-boxing” and “obligatory points of passage”—that aid in translating the competing frames into a truce frame. The examined properties of these mechanisms are described and analyzed, thereby extending the literature on technology frames by providing evidence of generic aligning mechanisms.

a r t i c l e i n f o Keywords: Data warehousing Data warehouse architecture Archictecture selection Independent data marts Bus architecture Enterprise data warehouse Hub and spoke Federated Even though data warehousing has been in existence for over a decade, companies are still uncertain about a critical decision — which data warehouse architecture to implement? Based on the existing literature, theory, and interviews with experts, a research model was created that identifies the various contextual factors that affect the selection decision. The results from the field survey and multinomial logistic regression suggest that various combinations of organizational factors influence data warehouse architecture selection. The strategic view of the data warehouse prior to implementation emerged as a key determinant. The research suggests an overall model for predicting the data warehouse architecture selection decision. Over the past decade, many companies have made data warehouses the foundation of their decision support infrastructures. These data repositories provide a solution to a recurring problem that has plagued companies' decision support initiatives — the lack of clean, accurate, timely, and integrated data. Whether the data is used for queries and reporting, decision support systems (DSS), executive information systems (EIS), online analytical processing (OLAP), or data mining, data warehouses provide the data that " fuels " these applications [36]. Data warehouses are also critical enablers of current strategic initiatives such as customer relationship management (CRM), business performance management (BPM), and supply chain management [18,23,35,85]. Despite the importance and growing experience with data ware-housing, there is still a considerable discussion and disagreement over which architecture to use. Bill Inmon, commonly referred to as " the father of data warehousing, " advocates what is variously called the hub and spoke architecture (i.e., a centralized data warehouse with dependent data marts), Corporate Information Factory, DW 2.0 (i.e., Inmon's current term), or enterprise data warehouse architecture [23]. Another architecture alternative is the data mart bus architecture with linked dimensional data marts (i.e., bus architecture), advocated by Ralph Kimball, the other preeminent figure in data warehousing [52]. 1 These and other architectures (e.g., independent data marts and federated) have fundamental differences and strong advocates. The selection of an appropriate architecture is an important decision. A study by the Meta Group found that architecture selection is one of the key factors influencing data warehousing success [54]. A Gartner report identified the architecture selection decision as one of the five problem …

With the rapid change of business competitive environment, enterprise resource integration and innovative issues of business operation have gradually become the most important issues for businesses. Furthermore, many enterprises have implemented novel information technology and developing the innovative e-business applications systems such as enterprise resource planning (ERP), customer relationship management (CRM), knowledge management (KM) and supply chain management (SCM) to enhance their competitive advantages. CRM systems can help organizations to gain the potential new customers, promote the existing customers’ purchase, maintain good relationship with customers as well as to enhance the customer value, thus can improve the enterprise images. Moreover, the development and applications of CRM systems have also been considered as important issues for researchers and practitioners in recent years. For Taiwan’s industry, it has been gradually transferred from manufacturing-oriented to a service-oriented. Thus, the service industry has higher percentage in the GDP and in which the distribution service industry is the biggest one and be a core industry in the whole of service industry. The main purpose of this study is to explore the factors affecting the acceptance and use of CRM systems. Furthermore, the proposed research model was built on the basis of unified theory of acceptance and use of technology (UTAUT) and task-technology fit (TTF) framework as well as technological and managerial theories. The implications of findings for practice will be discussed. 2010 Elsevier Ltd. All rights reserved.

This paper presents a conceptual framework aiming to better research, understand and develop Green IT within organizations. Based on a literature review on Green IT, regarding the concepts of responsibility and sustainability, we propose an initial framework with five dimensions: ethical, technological, economical, social and environmental. These dimensions compose Green IT strategies and practices. Additionally, the framework considers that environmental changing requirements, strategic requirements and dynamic capabilities are the forces which move organizations toward green practices and foster innovation. This framework is part of a five-year horizon project that began in 2009 and aims to contribute to the theory in the field, having as initial goal to identify the constructs associated with Green IT.

Companies when strategizing are looking for innovative ways to have a competitive advantage over their competitors. One way in which they compete is by the adoption of social media. Social media has evolved over the years and as a result, new concepts and applications are developed which promises to provide business value to a company. However, despite the usefulness of social media, many businesses fail to reap its full benefits. The current literature shows evidence of lack of strategically designed process for companies to successfully implement social media. The purpose of this study is to suggest a framework which provides the necessary alignment between social media goals with business objectives. From the literature review, a social media strategy framework was derived to offer an effective step by step approach to the development and imple‐ mentation of social media goals aligned with a company’s business objectives. The contribution to this study is the development of a social media strategy framework that can be used by organisations for business value.


Question: Identifying design components for a maturity model in interorganizational integration: a comparative study.

Introducing the Framework for Conceptual Interoperability Levels: A Bridge from Design to Implementation

Interoperability among systems is not a one-size-fits-all solution; rather, it spans a continuum from complete incompatibility to seamless integration. While various models have been established to gauge interoperability levels in the technical sphere, such frameworks are notably absent in the conceptual modeling domain. This paper presents a comprehensive model designed to address the spectrum of conceptual interoperability, surpassing the scope of existing technical reference models for interoperable systems.

The proposed model serves as a crucial link between conceptual design and technical implementation, integration, or federation. Its application is envisioned to facilitate the standardization of verification and validation (V&V) procedures, as well as the documentation of systems intended for federation. Furthermore, it enables early identification of the feasibility of meaningful interoperability between systems in the development phase, transcending the current focus on data exchange through standardized formats and interfaces.

Practically, this model complements and extends the recently published DoD Net-Centric Data Strategy for the Global Information Grid (GIG). It provides a roadmap for deriving the necessary metadata to achieve the DoD's ambitious Data Goal of "enabling data to be understandable." This framework not only fills a critical gap in the conceptual modeling domain but also empowers system designers to make informed decisions regarding interoperability at the earliest stages of development.

Creating superior competitiveness is central to open data organization's survivability in the fast changing and competitive open data market. In their quest to develop and increase competitiveness and survivability, many of these organizations are moving towards developing open data capabilities. Research-based knowledge on open data capabilities and how they relate to each other remains sparse, however, with most of the open data literature focusing on social and economic value of open data, not capabilities required. By exploring the related literature on business and organizational capabilities and linking the findings to the empirical evidence collected through the survey of 49 open data organizations around the world, this study develops an open data capability model. The model emerged from our deductive research process improves both theoretical and practical understanding of open data capabilities and their relationships required to help increase competitiveness and survivability of these types of organizations.

Article history: Received 18 June 2007 Received in revised form 9 June 2008 Accepted 20 June 2008 Available online 25 June 2008 Firms increasingly rely on outsourcing for strategic IT decisions, and themany sophisticated forms of outsourcing require significant management attention to ensure their success. Two forms of interorganizational governance–formal control and relational–have been used to examine the management of IT outsourcing relationships. Contrary to the conventional substitution view, recent studies have found that these two governance modes are complementary; however, the dynamics of their interactions remain unexplored. Based on the commitment–trust theory, this paper focuses on how the formal controlmechanism can influence the relational governance in an outsourcing engagement. Using service level agreements (SLAs) as a proxy for formal control, this studyfinds that eleven contractual elements, characterized as foundation, governance, and change management variables in an SLA, are positively related to the trust and relationship commitment among the parties. Trust and commitment, in turn, positively influence relational outcomes that we theorize would contribute to outsourcing success. Both research and practical implications of the results are discussed. © 2008 Elsevier B.V. All rights reserved.

I To cope with issues related to optimization, rationalization, risk management, economic value of technology and information assets, the implementation of appropriate IT governance seems an important need in public and private organizations. It’s one of these concepts that suddenly emerged and became an important issue in the information technology area. Many organizations started with the implementation of IT governance to achieve a better alignment between business and IT, however, there is no method to apply the IT governance principles in companies. This paper proposes a new approach to implement IT governance based on five iterative phases. This approach is a critical business process that ensures that the business meets its strategic objectives and depending on IT resources for execution. Keywords—Information technology, IT governance, lifecycle, strategic alignment, value

Decision support system for servitization of industrial SMEs: a modelling and simulation approach M. Chalal, X. Boucher & G. Marques To cite this article: M. Chalal, X. Boucher & G. Marques (2015) Decision support system for servitization of industrial SMEs: a modelling and simulation approach, Journal of Decision Systems, 24:4, 355-382, DOI: 10.1080/12460125.2015.1074836 To link to this article: http://dx.doi.org/10.1080/12460125.2015.1074836

Web services are redefining the way organizations exchange business-critical information internally and externally with customers, suppliers, and other business associates. In this paper, we develop an integrative model for understanding value creation in web services from a provider’s perspective. The model integrates the static representation of conventional business values with the fluidity of the emergent IT services domain. It captures the complexity and contradictions facing Web services providers in their drive towards market leadership, strategic differentiation and revenue generation from web services. The model comprises twelve propositions to guide our understanding and future research and practice in this increasingly important segment in the IT field. 2005 Elsevier B.V. All rights reserved.

The present thesis is concerned with enterprise software systems of companies in the Swedish electricity industry, an industry that for the past few years has been exposed to a fairly tumultuous change process as a consequence of legislative reforms. Previously, the business operations of electric utilities – as well as those of most companies in the computerized world – were supported by a number of isolated software systems performing specific tasks. In recent years, these systems have been extended, and more importantly, integrated into a company-wide system in its own right, in this thesis referred to as the enterprise software system. As enterprise software systems have evolved, so has a need for strategies, methods and techniques for their management. Enterprise software system management involves a number of concerns; of these concerns, software integration is one of the most prominent. The discipline of software architecture is concerned with the modeling of large-scale structures of software systems. While the generated models are employed for a number of purposes , their perhaps most significant function is to serve as a base for reasoning about the represented system. Several methods for analysis of architectures have been proposed in recent years, and although software architecture analysis has displayed considerable success for a number of systems, enterprise software systems have to a large extent been ignored. In the empirical context of the Scandinavian electricity industry, this thesis explores the applicability of software architecture analysis to enterprise software system integration. Two conceptually different architectural analysis methods – deduction-and induction-based approaches – are considered, as well as the engineering process in which architectural analysis is performed. As a result of the investigations, the thesis proposes a modified process for architectural analysis, presents an evaluation of deduction-based analysis methods , and proposes an adaptation of induction-based analysis methods to the enterprise software system context.

The present study proposes a design for an Information Technology Balanced Scorecard (IT BSC) that integrates with business and environment and balances and optimizes deployment and control of the Information Technology strategy. This Strategic and Tactical IT Planning model enhances IT's role in obtaining and measuring its contribution to business value: optimizing IT operational efficiency or proposing new business ideas with a high IT component. The proposed model stimulates innovation in each of the parts of the IT Services lifecycle, is based on IT Service Management best practices and uses ideas from the ITIL v3 standard.

Public-private partnerships (PPPs) offer a popular means by which the public sector can obtain infor-mation technology (IT) innovations and management know-how from private firms. However, these IT PPPs are extremely difficult to realize, especially considering the divergent interests of public- and private-side stake-holders. Our case study of an IT PPP reveals public- and private-side differences that initially impeded the establishment of a partnership; using institutional logics theory as meta-theoretical lens, we propose a model that explains how public and private parties managed to negotiate their mode of collaboration by balancing their competing institutional norms and practices which ultimately resulted in the convergence of the two divergent logics. Our paper contributes to theory and practice by (1) elucidating the theoretical foundations and role of institutional logics for IT project management that we found dominated by public and private norms and practices, (2) explaining why collaboration in IT PPPs is so diffi-cult, and (3) how eventually an IT PPP can be estab-lished. We discuss theoretical and practical implica-tions in the paper.

Structured Abstract Purpose – Designing a model and tools that are (a) capable of representing and handling personal knowledge in different degrees of structuredness and formalisation and (b) usable and extensible by end-users. Design/methodology/approach – This paper presents the result of analysing literature and various data-models and formalism used to structure information on the desktop. Findings – The unified data model (CDS) is capable of representing structures from various information tools, e. g. documents, file system, hypertext, tagging, and mind maps. The five knowledge axes of CDS are: identity, order, hierarchy, annotation and linking. Research limitations / implications – The CDS model is based on text. Extensions for multimedia annotations have not been investigated. Practical implications – Future PKM tools should take the mentioned shortcoming of existing PKM tools into account. Implementing the CDS model can be a way to make PKM tools interoperable. Originality/value – This paper presents research combining cognitive psychology, personal knowledge management and semantic web technologies. The CDS model shows a way to let end-users work on different levels of granularity and different levels of formality in one environment. Introduction " The most important contribution of management in the 20th century was to increase manual worker productivity fifty-fold. The most important contribution of management in the 21st century will be to increase knowledge worker productivity – hopefully by the same percentage. [...] The methods, however, are totally different from those that increased the productivity of manual workers. " Drucker (1999a, p. 79) What might these methods be? The field of knowledge management investigates since 1995 (Stankosky, 2005) how people and knowledge work together. North (2007) defines knowledge work as work based on knowledge with an immaterial result; value creation is based on processing, generating and communicating knowledge. Polanyi (1966) makes a distinction between explicit knowledge encoded in artefacts such as books or web pages, and tacit knowledge which resides in the individual. The SECI-model of Nonaka (1994) describes knowledge transfers between humans and artefacts. The field of knowledge management has focused on knowledge transfer between people, either via socialisation or via externalised artefacts. The high expectations of central enterprise knowledge repositories have often not been met (Braganza and Mollenkramer, 2002). The following wave of expert finders and corporate white pages focused mostly on connecting the right people and let them communicate. Today, knowledge workers are flooded with information (Alvarado et al., 2003). The field of Personal …

Article history: Received 7 January 2008 Received in revised form 24 June 2008 Accepted 31 July 2008 Available online 5 April 2009 Outsourcing is a phenomenon that as a practice originated in the 1950s, but it was not until the 1980s when the strategy became widely adopted in organizations. Since then, the strategy has evolved from a strictly cost focused approach towards more cooperative nature, in which cost is only one, often secondary, decision-making criterion. In the development of the strategy, three broad and somewhat overlapping, yet distinct phases can be identified: the era of the Big Bang, the era of the Bandwagon, and the era of Barrierless Organizations. This paper illustrates that the evolution of the practice has caused several contradictions among researchers, as well as led to the situation where the theoretical background of the phenomenon has recently become much richer. Through examining existing research, this paper intends to identify the development of outsourcing strategy from a practical as well as a theoretical perspective from its birth up to today. In addition, through providing insights from managers in the information technology industry, this paper aims at providing a glimpse from the future – that is – what may be the future directions and research issues in this complex phenomenon? © 2009 Elsevier Inc. All rights reserved.