Question: Rumor identification and categorization in Twitter data analysis.

This paper delves into the domain of sentiment analysis, specifically focusing on Twitter data. Our key contributions are twofold: Firstly, we propose the integration of Part-of-Speech (POS)-specific prior polarity features, a novel approach aimed at enhancing the accuracy of sentiment detection. Secondly, we investigate the efficacy of employing a tree kernel method as a means to bypass the laborious process of feature engineering. Our results reveal that both the introduction of POS-specific features and the application of the tree kernel method yield comparable performance improvements, with each significantly surpassing the current state-of-the-art baseline. This research underscores the potential of these innovative techniques in advancing the field of sentiment analysis on social media platforms.

**Question**: A Direct Search Method to solve Economic Dispatch Problem with Valve-Point Effect

**Documents**: A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
An evolutionary recurrent network which automates the design of recurrent neural/fuzzy networks using a new evolutionary learning algorithm is proposed in this paper. This new evolutionary learning algorithm is based on a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO), and is thus called HGAPSO. In HGAPSO, individuals in a new generation are created, not only by crossover and mutation operation as in GA, but also by PSO. The concept of elite strategy is adopted in HGAPSO, where the upper-half of the best-performing individuals in a population are regarded as elites. However, instead of being reproduced directly to the next generation, these elites are first enhanced. The group constituted by the elites is regarded as a swarm, and each elite corresponds to a particle within it. In this regard, the elites are enhanced by PSO, an operation which mimics the maturing phenomenon in nature. These enhanced elites constitute half of the population in the new generation, whereas the other half is generated by performing crossover and mutation operation on these enhanced elites. HGAPSO is applied to recurrent neural/fuzzy network design as follows. For recurrent neural network, a fully connected recurrent neural network is designed and applied to a temporal sequence production problem. For recurrent fuzzy network design, a Takagi-Sugeno-Kang-type recurrent fuzzy network is designed and applied to dynamic plant control. The performance of HGAPSO is compared to both GA and PSO in these recurrent networks design problems, demonstrating its superiority.

**Similar Documents**: This paper presents a novel evolutionary learning algorithm, termed Hybrid Genetic Algorithm-Particle Swarm Optimization (HGAPSO), aimed at automating the design of recurrent neural/f

Sarcasm is a form of speech act in which the speakers convey their message in an implicit way. The inherently ambiguous nature of sarcasm sometimes makes it hard even for humans to decide whether an utterance is sarcastic or not. Recognition of sarcasm can benefit many sentiment analysis NLP applications, such as review summarization, dialogue systems and review ranking systems. In this paper we experiment with semisupervised sarcasm identification on two very different data sets: a collection of 5.9 million tweets collected from Twitter, and a collection of 66000 product reviews from Amazon. Using the Mechanical Turk we created a gold standard sample in which each sentence was tagged by 3 annotators, obtaining F-scores of 0.78 on the product reviews dataset and 0.83 on the Twitter dataset. We discuss the differences between the datasets and how the algorithm uses them (e.g., for the Amazon dataset the algorithm makes use of structured information). We also discuss the utility of Twitter #sarcasm hashtags for the task.

In Twitter, users can annotate tweets with hashtags to indicate the ongoing topics. Hashtags provide users a convenient way to categorize tweets. From the system's perspective, hashtags play an important role in tweet retrieval, event detection, topic tracking, and advertising, etc. Annotating tweets with the right hashtags can lead to a better user experience. However, two problems remain unsolved during an annotation: (1) Before the user decides to create a new hashtag, is there any way to help her/him find out whether some related hashtags have already been created and widely used? (2) Different users may have different preferences for categorizing tweets. However, few work has been done to study the personalization issue in hashtag recommendation. To address the above problems, we propose a statistical model for personalized hashtag recommendation in this paper. With millions of <;tweet, hashtag> pairs being published everyday, we are able to learn the complex mappings from tweets to hashtags with the wisdom of the crowd. Two questions are answered in the model: (1) Different from traditional item recommendation data, users and tweets in Twitter have rich auxiliary information like URLs, mentions, locations, social relations, etc. How can we incorporate these features for hashtag recommendation? (2) Different hashtags have different temporal characteristics. Hashtags related to breaking events in the physical world have strong rise-and-fall temporal pattern while some other hashtags remain stable in the system. How can we incorporate hashtag related features to serve for hashtag recommendation? With all the above factors considered, we show that our model successfully outperforms existing methods on real datasets crawled from Twitter.

With their blistering expansions in recent years, popular on-line social sites such as Twitter, Facebook and Bebo, have become some of the major news sources as well as the most effective channels for viral marketing nowadays. However, alongside these promising features comes the threat of misinformation propagation which can lead to undesirable effects, such as the widespread panic in the general public due to faulty swine flu tweets on Twitter in 2009. Due to the huge magnitude of online social network (OSN) users and the highly clustered structures commonly observed in these kinds of networks, it poses a substantial challenge to efficiently contain viral spread of misinformation in large-scale social networks.
 In this paper, we focus on how to limit viral propagation of misinformation in OSNs. Particularly, we study a set of problems, namely the β1T -- Node Protectors, which aims to find the smallest set of highly influential nodes whose decontamination with good information helps to contain the viral spread of misinformation, initiated from the set I, to a desired ratio (1 − β) in T time steps. In this family set, we analyze and present solutions including inapproximability result, greedy algorithms that provide better lower bounds on the number of selected nodes, and a community-based heuristic method for the Node Protector problems. To verify our suggested solutions, we conduct experiments on real world traces including NetHEPT, NetHEPT_WC and Facebook networks. Empirical results indicate that our methods are among the best ones for hinting out those important nodes in comparison with other available methods.

In this paper, we present overview of our approach for clustering tweets. Due to short text of tweets, traditional text clustering mechanisms alone may not produce optimal results. We believe that there is an underlying theme/topic present in majority of tweets which is evident in growing usage of hashtag feature in the Twitter network. Clustering tweets based on these themes seems a more natural way for grouping. We propose to use Wikipedia topic taxonomy to discover the themes from the tweets and use the themes along with traditional word based similarity metric for clustering. We show some of our initial results to demonstrate the effectiveness of our approach.

Recently, microblogging sites such as Twitter have garnered a great deal of attention as an advanced form of location-aware social network services, whereby individuals can easily and instantly share their most recent updates from any place. In this study, we aim to develop a geo-social event detection system by monitoring crowd behaviors indirectly via Twitter. In particular, we attempt to find out the occurrence of local events such as local festivals; a considerable number of Twitter users probably write many posts about these events. To detect such unusual geo-social events, we depend on geographical regularities deduced from the usual behavior patterns of crowds with geo-tagged microblogs. By comparing these regularities with the estimated ones, we decide whether there are any unusual events happening in the monitored geographical area. Finally, we describe the experimental results to evaluate the proposed unusuality detection method on the basis of geographical regularities obtained from a large number of geo-tagged tweets around Japan via Twitter.

On virtual spaces, some individuals use multiple usernames or copycat/forge other users (usually called ‘‘sock puppet’’) to communicate with others. Those sock puppets are fake identities through which members of Internet community praise or create the illusion of support for the product or one’s work, pretending to be a different person. A fundamental problem is how to identify these sock puppets. In this paper, we propose a sock puppet detection algorithm which combines authorship-identification techniques and link analysis. Firstly, we propose an interesting social network model in which links between two IDs are built if they have similar attitudes to most topics that both of them participate in; then, the edges are pruned according a hypothesis test, which consider the impact of their writing styles; finally, the link-based community detection for pruned network is performed. Compared to traditional methods, our approach has three advantages: (1) it conforms to the practical meanings of sock puppet community; (2) it can be applied in online situation; (3) it increases the efficiency of link analysis. In the experimental work, we evaluate our method using real datasets and compared our approach with several previous methods; the results have proved above advantages. 2012 Elsevier B.V. All rights reserved.

Twitter spam has long been a critical but difficult problem to be addressed. So far, researchers have developed a series of machine learning-based methods and blacklisting techniques to detect spamming activities on Twitter. According to our investigation, current methods and techniques have achieved the accuracy of around 80%. However, due to the problems of spam drift and information fabrication, these machine-learning based methods cannot efficiently detect spam activities in real-life scenarios. Moreover, the blacklisting method cannot catch up with the variations of spamming activities as manually inspecting suspicious URLs is extremely time-consuming. In this paper, we proposed a novel technique based on deep learning techniques to address the above challenges. The syntax of each tweet will be learned through WordVector Training Mode. We then constructed a binary classifier based on the preceding representation dataset. In experiments, we collected and implemented a 10-day real Tweet datasets in order to evaluate our proposed method. We first studied the performance of different classifiers, and then compared our method to other existing text-based methods. We found that our method largely outperformed existing methods. We further compared our method to non-text-based detection techniques. According to the experiment results, our proposed method was more accurate.

Nowadays, review sites are more and more confronted with the spread of misinformation, i.e., opinion spam, which aims at promoting or damaging some target businesses, by misleading either human readers, or automated opinion mining and sentiment analysis systems. For this reason, in the last years, several data-driven approaches have been proposed to assess the credibility of user-generated content diffused through social media in the form of on-line reviews. Distinct approaches often consider different subsets of characteristics, i.e., features, connected to both reviews and reviewers, as well as to the network structure linking distinct entities on the review-site in exam. This article aims at providing an analysis of the main review- and reviewer-centric features that have been proposed up to now in the literature to detect fake reviews, in particular from those approaches that employ supervised machine learning techniques. These solutions provide in general better results with respect to purely unsupervised approaches, which are often based on graph-based methods that consider relational ties in review sites. Furthermore, this work proposes and evaluates some additional new features that can be suitable to classify genuine and fake reviews. For this purpose, a supervised classifier based on Random Forests have been implemented, by considering both well-known and new features, and a large-scale labeled dataset from which all these features have been extracted. The good results obtained show the effectiveness of new features to detect in particular singleton fake reviews, and in general the utility of this study.

This work examines the predictive power of public data by aggregating information from multiple online sources. Our sources include microblogging sites like Twitter, online message boards like Yahoo! Finance, and traditional news articles. The subject of prediction are daily stock price movements from Standard & Poor’s 500 index (S&P 500) during a period from June 2011 to November 2011. To forecast price movements we filter messages by stocks, apply state-of-the-art sentiment analysis to message texts, and aggregate message sentiments to generate trading signals for daily buy and sell decisions. We evaluate prediction quality through a simple trading model considering real-world limitations like transaction costs or broker commission fees. Considering 833 virtual trades, our model outperformed the S&P 500 and achieved a positive return on investment of up to ~0.49% per trade or ~0.24% when adjusted by market, depending on supposed trading costs.

Abstract – Now-a-days social networking sites are at the boom, so large amount of data is generated. Millions of people are sharing their views daily on micro blogging sites, since it contains short and simple expressions. In this paper, we will discuss about a paradigm to extract the sentiment from a famous micro blogging service, Twitter, where users post their opinions for everything. In this paper, we will discuss the existing analysis of twitter dataset with data mining approach such as use of Sentiment analysis algorithm using machine learning algorithms. An approach is introduced that automatically classifies the sentiments of Tweets taken from Twitter dataset as in [1]. These messages or tweets are classified as positive, negative or neutral with respect to a query term. This is very useful for the companies who want to know the feedback about their product brands or the customers who want to search the opinion from others about product before purchase. We will use machine learning algorithms for classifying the sentiment of Twitter messages using distant supervision which is discussed in [8]. The training data consists of Twitter messages with emoticons, acronyms which are used as noisy labels discussed in [4]. We examine sentiment analysis on Twitter data. The contributions of this survey paper are: (1) we use Parts Of Speech (POS)-specific prior polarity features. (2) We also use a tree kernel to prevent the need for monotonous feature engineering.


Question: Rumor identification and categorization for Twitter dataset analysis.

This study delves into the evaluation of information quality and cognitive authority in the context of the World Wide Web (WWW), where users are exposed to an unprecedented volume and variety of information. Given the absence of traditional quality control mechanisms on the WWW, the conventional criteria for assessing authority and information quality may no longer be applicable. The research examines how individuals determine the quality and authority of online information. Seven key aspects of information quality judgment were identified: source, content, format, presentation, currency, accuracy, and loading speed. Source credibility was considered at two levels: institutional and individual. Institutional credibility encompassed factors such as the institutional domain indicated by the URL, the type of institution, and its recognized reputation. Individual credibility involved the identification of the creator/author, their affiliation, and the author's name. The level of concern for evaluating information quality varied based on the potential consequences of using the information, actions or commitments based on the information, and the focus of the inquiry. Interestingly, participants perceived the WWW as an institution to be less authoritative and credible compared to other information systems.

**Question**: An investigation into the use of blockchain technology in supply chain management

**Documents**: Blockchain: A Review of the Literature and Implications for Supply Chain Management
Blockchain technology has been widely recognized for its potential to transform supply chain management (SCM) by enhancing transparency, traceability, and trust among supply chain partners. This paper presents a review of the literature on blockchain in SCM, with a focus on its applications, benefits, challenges, and future research directions. The review covers a range of blockchain applications in SCM, including food safety, pharmaceuticals, and the automotive industry. The benefits of blockchain in SCM are identified as improved traceability, reduced transaction costs, and enhanced trust and collaboration among supply chain partners. However, the adoption of blockchain in SCM is hindered by several challenges, such as data privacy concerns, interoperability issues, and the need for standardization. The paper also discusses the potential of blockchain to enable new business models and disrupt existing ones in SCM. Finally, the paper identifies several future research directions in the area of blockchain and SCM, including the need for empirical studies, the development of blockchain-based SCM systems, and the exploration of blockchain's role in sustainable and responsible supply chains.

**Similar Documents**: This paper offers an in-depth review of the literature surrounding blockchain technology's role in supply chain management (SCM), highlighting its transformative potential to increase transparency, traceability, and trust among SCM stakeholders. The review encompasses a variety of blockchain applications within

Our personal social networks are big and cluttered, and currently there is no good way to organize them. Social networking sites allow users to manually categorize their friends into social circles (e.g. ‘circles’ on Google+, and ‘lists’ on Facebook and Twitter), however they are laborious to construct and must be updated whenever a user’s network grows. We define a novel machine learning task of identifying users’ social circles. We pose the problem as a node clustering problem on a user’s ego-network, a network of connections between her friends. We develop a model for detecting circles that combines network structure as well as user profile information. For each circle we learn its members and the circle-specific user profile similarity metric. Modeling node membership to multiple circles allows us to detect overlapping as well as hierarchically nested circles. Experiments show that our model accurately identifies circles on a diverse set of data from Facebook, Google+, and Twitter for all of which we obtain hand-labeled ground-truth.

Once an email problem, spam has nowadays branched into new territories with disruptive effects. In particular, spam has established itself over the recent years as a ubiquitous, annoying, and sometimes threatening aspect of online social networks. Due to its prevalent existence, many works have tackled spam on Twitter from different angles. Spam is, however, a moving target. The new generation of spammers on Twitter has evolved into online creatures that are not easily recognizable by old detection systems. With the strong tangled spamming community, automatic tweeting scripts, and the ability to massively create Twitter accounts with a negligible cost, spam on Twitter is becoming smarter, fuzzier and harder to detect. Our own analysis of spam content on Arabic trending hashtags in Saudi Arabia results in an estimate of about three quarters of the total generated content. This alarming rate makes the development of adaptive spam detection techniques a very real and pressing need. In this paper, we analyze the spam content of trending hashtags on Saudi Twitter, and assess the performance of previous spam detection systems on our recently gathered dataset. Due to the escalating manipulation that characterizes newer spamming accounts, simple manual labeling currently leads to inaccurate results. In order to get reliable ground-truth data, we propose an updated manual classification algorithm that avoids the deficiencies of older manual approaches. We also adapt the previously proposed features to respond to spammers evading techniques, and use these features to build a new data-driven detection system.

Social media play a decisive role in communicating and spreading information during global events. In particular, real-time microblogging platforms such as Twitter have become prevalent. Researchers have used microblogging for a number of tasks, including past events analysis, predictions, and information retrieval. Nevertheless, little attention has been given to quantitative data extraction. In this paper, we address two questions: can we develop a mechanism to extract quantitative data from a collection of tweets, and can we use the salient findings to describe an event? To answer the first question, we introduce Raimond, a virtual text curator, specialized in quantitative data extraction from Twitter. To address the second question, we use our system on three events and evaluate its output using a crowdsourcing strategy. We demonstrate the e↵ectiveness of our approach with a number of real world examples.

Social media now plays a pivotal role in electoral campaigns. Rapid dissemination of information through platforms such as Twitter has enabled politicians to broadcast their message to a wide audience. In this paper, we investigated the sentiment of tweets by the two main presidential candidates, Hillary Clinton and Donald Trump, along with almost 2.9 million tweets by Twitter users during the 2016 US Presidential Elections. We analyzed these short texts to evaluate how accurately Twitter represented the public opinion and real world events of significance related with the elections. We also analyzed the behavior of over a million distinct Twitter users to identify whether the platform was used to share original opinions and to interact with other users or whether few opinions were repeated over and over again with little inter-user dialogue. Finally, we wanted to assess the sentiment of tweets by both candidates and their impact on the election related discourse on Twitter. Some of our findings included the discovery that little original content was created by users and Twitter was primarily used for rebroadcasting already present opinions in the form of retweets with little communication between users. Also of significance was the finding that sentiment and topics expressed on Twitter can be a good proxy of public opinion and important election related events. Moreover, we found that Donald Trump offered a more optimistic and positive campaign message than Hillary Clinton and enjoyed better sentiment when mentioned in messages by Twitter users.

Social Network Analysis (SNA) is a field of study that focuses on analyzing user profiles and participations on social network channels in order to model relationships between people and to predict certain behaviors or knowledge. To achieve their goals, researchers, interested in SNA, have to extract content and structure from the numerous social networks available today. Existing tools, which help in this task, often require substantial pre-processing or good programming skills which may not be available for all SNA researchers. This paper describes RUM, a data extraction tool which allows researchers to easily extract several types of content and structure that are available on Facebook pages. Consequently, the extracted data can be saved and analyzed. RUM Extractor is easy to set up and use, and it gives flexible options to users to specify the type and amount of content and structure they want to retrieve. The paper also demonstrates how RUM can be exploited by collecting and further analyzing data collected from two popular Arabic news pages.

Online social and news media generate rich and timely information about real-world events of all kinds. However, the huge amount of data available, along with the breadth of the user base, requires a substantial effort of information filtering to successfully drill down to relevant topics and events. Trending topic detection is therefore a fundamental building block to monitor and summarize information originating from social sources. There are a wide variety of methods and variables and they greatly affect the quality of results. We compare six topic detection methods on three Twitter datasets related to major events, which differ in their time scale and topic churn rate. We observe how the nature of the event considered, the volume of activity over time, the sampling procedure and the pre-processing of the data all greatly affect the quality of detected topics, which also depends on the type of detection method used. We find that standard natural language processing techniques can perform well for social streams on very focused topics, but novel techniques designed to mine the temporal distribution of concepts are needed to handle more heterogeneous streams containing multiple stories evolving in parallel. One of the novel topic detection methods we propose, based on -grams cooccurrence and topic ranking, consistently achieves the best performance across all these conditions, thus being more reliable than other state-of-the-art techniques.

Most existing approaches to Twitter sentiment analysis assume that sentiment is explicitly expressed through affective words. Nevertheless, sentiment is often implicitly expressed via latent semantic relations, patterns and dependencies among words in tweets. In this paper, we propose a novel approach that automatically captures patterns of words of similar contextual semantics and sentiment in tweets. Unlike previous work on sentiment pattern extraction, our proposed approach does not rely on external and fixed sets of syntactical templates/patterns, nor requires deep analyses of the syntactic structure of sentences in tweets. We evaluate our approach with tweetand entity-level sentiment analysis tasks by using the extracted semantic patterns as classification features in both tasks. We use 9 Twitter datasets in our evaluation and compare the performance of our patterns against 6 state-of-the-art baselines. Results show that our patterns consistently outperform all other baselines on all datasets by 2.19% at the tweet-level and 7.5% at the entity-level in average F-measure.

In the past few years, sentiment analysis and opinion mining becomes a popular and important task. These studies all assume that their opinion resources are real and trustful. However, they may encounter the faked opinion or opinion spam problem. In this paper, we study this issue in the context of our product review mining system. On product review site, people may write faked reviews, called review spam, to promote their products, or defame their competitors’ products. It is important to identify and filter out the review spam. Previous work only focuses on some heuristic rules, such as helpfulness voting, or rating deviation, which limits the performance of this task. In this paper, we exploit machine learning methods to identify review spam. Toward the end, we manually build a spam collection from our crawled reviews. We first analyze the effect of various features in spam identification. We also observe that the review spammer consistently writes spam. This provides us another view to identify review spam: we can identify if the author of the review is spammer. Based on this observation, we provide a twoview semi-supervised method, co-training, to exploit the large amount of unlabeled data. The experiment results show that our proposed method is effective. Our designed machine learning methods achieve significant improvements in comparison to the heuristic baselines.

As the size of Twitter© data is increasing, so are undesirable behaviors of its users. One of such undesirable behavior is cyberbullying, which may even lead to catastrophic consequences. Hence, it is critical to efficiently detect cyberbullying behavior by analyzing tweets, if possible in realtime. Prevalent approaches to identify cyberbullying are mainly stand-alone and thus, are time-consuming. This research improves detection task using the principles of collaborative computing. Different collaborative paradigms are suggested and discussed in this paper. Preliminary results indicate an improvement in time and accuracy of the detection mechanism over the stand-alone paradigm.

Our society is increasingly digitalized. Every day, a tremendous amount of information is being created, shared, and digested through all kinds of cyber channels. Although people can easily acquire information from various sources (social media, news articles, etc.), the truthfulness of most received information remains unverified. In many real-life scenarios, false information has become the de facto cause that leads to detrimental decision makings, and techniques that can automatically filter false information are highly demanded. However, verifying whether a piece of information is trustworthy is difficult because: (1) selecting candidate snippets for fact checking is nontrivial; and (2) detecting supporting evidences, i.e. stances, suffers from the difficulty of measuring the similarity between claims and related evidences. We build ClaimVerif, a claim verification system that not only provides credibility assessment for any user-given query claim, but also rationales the assessment results with supporting evidences. ClaimVerif can automatically select the stances from millions of documents and employs two-step training to justify the opinions of the stances. Furthermore, combined with the credibility of stances sources, ClaimVerif degrades the score of stances from untrustworthy sources and alleviates the negative effects from rumor spreaders. Our empirical evaluations show that ClaimVerif achieves both high accuracy and efficiency in different claim verification tasks. It can be highly useful in practical applications by providing multi-dimension analysis for the suspicious statements, including the stances, opinions, source credibility and estimated judgements.


Question: Rumor identification and categorization for Twitter information.

This research introduces a straightforward unsupervised learning method for categorizing reviews as either positive (thumbs up) or negative (thumbs down). The classification of a review is determined by the mean semantic orientation of the phrases within it that include adjectives or adverbs. A phrase's semantic orientation is deemed positive when it carries favorable connotations (for instance, "subtle nuances") and negative when it bears unfavorable implications (such as "very cavalier"). In this study, the semantic orientation of a phrase is computed as the mutual information between the phrase in question and the term "excellent" minus the mutual information between the phrase and the term "poor". A review is deemed positive if the average semantic orientation of its constituent phrases is positive. When tested on 410 reviews from Epinions, which were randomly selected from four distinct categories (automobiles, banks, movies, and travel destinations), the algorithm achieves an average accuracy of 74%. The accuracy varies from 84% for automobile reviews to 66% for movie reviews.

**Question**: A Comparative Study of Recommender Systems for Personalized Medicine

**Documents**: Collaborative Filtering on Ordinal User Feedback
Collaborative filtering (CF) is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences from many users (collaborating). The underlying assumption of the CF approach is that if a person A has the same opinion as a person B on an issue, A is more likely to have B's opinion on a different issue. This paper presents a novel CF method for ordinal feedback, where users rate items on an ordinal scale. The proposed method, called Collaborative Ordinal Regression (COR), is based on the assumption that the ordinal ratings reflect the user's true utility, which is a continuous and monotonically increasing function of the rating. The model is trained using a maximum likelihood estimation approach, where the likelihood function is derived from the assumption that the ratings are generated from a latent utility function with a logistic distribution. The proposed method is evaluated on a number of datasets, including movie ratings, product ratings, and music ratings. The results show that the proposed method outperforms the state-of-the-art CF methods in terms of prediction accuracy.

**Similar Documents**: This article introduces a fresh collaborative filtering (CF) technique designed for ordinal user feedback, where users rate items on an ordinal scale. The proposed method, referred to as Collaborative Ordinal Regression (COR), operates under the assumption that ordinal ratings mirror a user

We present a machine learning framework that leverages a mixture of metadata, network, and temporal features to detect extremist users, and predict content adopters and interaction reciprocity in social media. We exploit a unique dataset containing millions of tweets generated by more than 25 thousand users who have been manually identified, reported, and suspended by Twitter due to their involvement with extremist campaigns. We also leverage millions of tweets generated by a random sample of 25 thousand regular users who were exposed to, or consumed, extremist content. We carry out three forecasting tasks, (i) to detect extremist users, (ii) to estimate whether regular users will adopt extremist content, and finally (iii) to predict whether users will reciprocate contacts initiated by extremists. All forecasting tasks are set up in two scenarios: a post hoc (time independent) prediction task on aggregated data, and a simulated real-time prediction task. The performance of our framework is extremely promising, yielding in the different forecasting scenarios up to 93% AUC for extremist user detection, up to 80% AUC for content adoption prediction, and finally up to 72% AUC for interaction reciprocity forecasting. We conclude by providing a thorough feature analysis that helps determine which are the emerging signals that provide predictive power in different scenarios.

Abstract – Now-a-days social networking sites are at the boom, so large amount of data is generated. Millions of people are sharing their views daily on micro blogging sites, since it contains short and simple expressions. In this paper, we will discuss about a paradigm to extract the sentiment from a famous micro blogging service, Twitter, where users post their opinions for everything. In this paper, we will discuss the existing analysis of twitter dataset with data mining approach such as use of Sentiment analysis algorithm using machine learning algorithms. An approach is introduced that automatically classifies the sentiments of Tweets taken from Twitter dataset as in [1]. These messages or tweets are classified as positive, negative or neutral with respect to a query term. This is very useful for the companies who want to know the feedback about their product brands or the customers who want to search the opinion from others about product before purchase. We will use machine learning algorithms for classifying the sentiment of Twitter messages using distant supervision which is discussed in [8]. The training data consists of Twitter messages with emoticons, acronyms which are used as noisy labels discussed in [4]. We examine sentiment analysis on Twitter data. The contributions of this survey paper are: (1) we use Parts Of Speech (POS)-specific prior polarity features. (2) We also use a tree kernel to prevent the need for monotonous feature engineering.

Sentiment Analysis of tweets is a complex task, because these short messages employ unconventional language to increase the expressiveness. This task becomes even more difficult when people use figurative language (e.g. irony, sarcasm and metaphors) because it causes a mismatch between the literal meaning and the actual expressed sentiment. In this paper, we describe a sentiment analysis system designed for handling ironic and sarcastic tweets. Features grounded on several linguistic levels are proposed and used to classify the tweets in a 11-scale range, using a decision tree. The system is evaluated on the dataset released by the organizers of the SemEval 2015, task 11. The results show that our method largely outperforms the systems proposed by the participants of the task on ironic and sarcastic tweets.

Twitter presents an unparalleled opportunity for researchers from various ï¬elds to gather valuable and genuine textual data from millions of people. However, the collection pro-cess, as well as the analysis of these data require different kinds of skills (e.g. programing, data mining) which can be an obstacle for people who do not have this background. In this paper we present CATS, an open source, scalable, Web application designed to support researchers who want to carry out studies based on tweets. The purpose of CATS is twofold: (i) allow people to collect tweets (ii) enable them to analyze these tweets thanks to efï¬cient tools (e.g. event detection, named-entity recognition, topic modeling, word-clouds). What is more, CATS relies on a distributed imple-mentation which can deal with massive data streams.

User generated content on the microblogging social network Twitter continues to grow with significant amount of information. The semantic analysis offers the opportunity to discover and model latent interests’ in the users’ publications. This article focuses on the problem of uncertainty in the users’ publications that has not been previously treated. It proposes a new approach for users’ interest discovery from uncertain information that augments traditional methods using possibilistic logic. The possibility theory provides a solid theoretical base for the treatment of incomplete and imprecise information and inferring the reliable expressions from a knowledge base. More precisely, this approach used the product-based possibilistic network to model knowledge base and discovering possibilistic interests. DBpedia ontology is integrated into the interests’ discovery process for selecting the significant topics. The empirical analysis and the comparison with the most known methods proves the significance of this approach.

Sarcasm is a form of speech act in which the speakers convey their message in an implicit way. The inherently ambiguous nature of sarcasm sometimes makes it hard even for humans to decide whether an utterance is sarcastic or not. Recognition of sarcasm can benefit many sentiment analysis NLP applications, such as review summarization, dialogue systems and review ranking systems. In this paper we experiment with semisupervised sarcasm identification on two very different data sets: a collection of 5.9 million tweets collected from Twitter, and a collection of 66000 product reviews from Amazon. Using the Mechanical Turk we created a gold standard sample in which each sentence was tagged by 3 annotators, obtaining F-scores of 0.78 on the product reviews dataset and 0.83 on the Twitter dataset. We discuss the differences between the datasets and how the algorithm uses them (e.g., for the Amazon dataset the algorithm makes use of structured information). We also discuss the utility of Twitter #sarcasm hashtags for the task.

We propose an analytical framework able to investigate discussions about polarized topics in online social networks from many different angles. The framework supports the analysis of social networks along several dimensions: time, space and sentiment. We show that the proposed analytical framework and the methodology can be used to mine knowledge about the perception of complex social phenomena. We selected the refugee crisis discussions over Twitter as a case study. This difficult and controversial topic is an increasingly important issue for the EU. The raw stream of tweets is enriched with space information (user and mentioned locations), and sentiment (positive vs. negative) w.r.t. refugees. Our study shows differences in positive and negative sentiment in EU countries, in particular in UK, and by matching events, locations and perception, it underlines opinion dynamics and common prejudices regarding the refugees.

BACKGROUND
Twitter has shown some usefulness in predicting influenza cases on a weekly basis in multiple countries and on different geographic scales. Recently, Broniatowski and colleagues suggested Twitter's relevance at the city-level for New York City. Here, we look to dive deeper into the case of New York City by analyzing daily Twitter data from temporal and spatiotemporal perspectives. Also, through manual coding of all tweets, we look to gain qualitative insights that can help direct future automated searches.

OBJECTIVE
The intent of the study was first to validate the temporal predictive strength of daily Twitter data for influenza-like illness emergency department (ILI-ED) visits during the New York City 2012-2013 influenza season against other available and established datasets (Google search query, or GSQ), and second, to examine the spatial distribution and the spread of geocoded tweets as proxies for potential cases.

METHODS
From the Twitter Streaming API, 2972 tweets were collected in the New York City region matching the keywords "flu", "influenza", "gripe", and "high fever". The tweets were categorized according to the scheme developed by Lamb et al. A new fourth category was added as an evaluator guess for the probability of the subject(s) being sick to account for strength of confidence in the validity of the statement. Temporal correlations were made for tweets against daily ILI-ED visits and daily GSQ volume. The best models were used for linear regression for forecasting ILI visits. A weighted, retrospective Poisson model with SaTScan software (n=1484), and vector map were used for spatiotemporal analysis.

RESULTS
Infection-related tweets (R=.763) correlated better than GSQ time series (R=.683) for the same keywords and had a lower mean average percent error (8.4 vs 11.8) for ILI-ED visit prediction in January, the most volatile month of flu. SaTScan identified primary outbreak cluster of high-probability infection tweets with a 2.74 relative risk ratio compared to medium-probability infection tweets at P=.001 in Northern Brooklyn, in a radius that includes Barclay's Center and the Atlantic Avenue Terminal.

CONCLUSIONS
While others have looked at weekly regional tweets, this study is the first to stress test Twitter for daily city-level data for New York City. Extraction of personal testimonies of infection-related tweets suggests Twitter's strength both qualitatively and quantitatively for ILI-ED prediction compared to alternative daily datasets mixed with awareness-based data such as GSQ. Additionally, granular Twitter data provide important spatiotemporal insights. A tweet vector-map may be useful for visualization of city-level spread when local gold standard data are otherwise unavailable.

Crime analysis has become a critical area for helping law enforcement agencies to protect civilians. As a result of a rapidly increasing population, crime rates have increased dramatically, and appropriate analysis has become a timeconsuming effort. Text mining is an effective tool that may help to solve this problem to classify crimes in effective manner. The proposed system aims to detect and classify crimes in Twitter posts that written in the Arabic language, one of the most widespread languages today. In this paper, classification techniques are used to detect crimes and identify their nature by different classification algorithms. The experiments evaluate different algorithms, such as SVM, DT, CNB, and KNN, in terms of accuracy and speed in the crime domain. Also, different features extraction techniques are evaluated, including rootbased stemming, light stemming, n-gram. The experiments revealed the superiority of n-gram over other techniques. Specifically, the results indicate the superiority of SVM with trigram over other classifiers, with a 91.55% accuracy. Keywords—Crimes; text mining; classification; features extraction techniques; arabic posts; twitter

The open nature of the World Wide Web makes evaluating webpage credibility challenging for users. In this paper, we aim to automatically assess web credibility by investigating various characteristics of webpages. Specifically, we first identify features from textual content, link structure, webpages design, as well as their social popularity learned from popular social media sites (e.g., Facebook, Twitter). A set of statistical analyses methods are applied to select the most informative features, which are then used to infer webpages credibility by employing supervised learning algorithms. Real dataset-based experiments under two application settings show that we attain an accuracy of 75% for classification, and an improvement of 53% for the mean absolute error (MAE), with respect to the random baseline approach, for regression.


Question: Rumor identification and categorization for Twitter information.

This paper tackles the critical issue of misinformation containment within online social networks (OSNs), focusing on platforms like Twitter, Facebook, and Bebo. These networks, with their rapid growth and extensive user base, have transformed into significant news sources and powerful tools for viral marketing. However, they also present a significant risk due to the potential for misinformation to spread rapidly, leading to negative consequences such as widespread panic, as evidenced by the swine flu tweets on Twitter in 2009. The sheer size of OSNs and their characteristic clustered structures make it challenging to effectively control the viral spread of false information.
The primary objective of this study is to develop strategies to limit the dissemination of misinformation in OSNs. Specifically, we address a series of problems under the umbrella of the β1T -- Node Protectors, which involves identifying the smallest set of highly influential nodes that, when provided with accurate information, can effectively contain the spread of misinformation to a desired ratio (1 − β) within T time steps. We explore this problem set through inapproximability analysis, the presentation of greedy algorithms that offer improved lower bounds on the number of nodes to be protected, and a community-based heuristic approach. To validate the efficacy of our proposed solutions, we perform experiments using real-world network traces, including NetHEPT, NetHEPT_WC, and Facebook networks. Our empirical findings demonstrate that our methods outperform existing techniques in identifying key nodes crucial for misinformation containment.

The dissemination of medical misinformation online presents a challenge to human health. Machine learning techniques provide a unique opportunity for decreasing the cognitive load associated with deciding upon whether any given user comment is likely to contain misinformation, but a paucity of labeled data of medical misinformation makes supervised approaches a challenge. In order to ameliorate this condition, we present a new labeled dataset of misinformative and non-misinformative comments developed over posted questions and comments on a health discussion forum. This required extraction of candidate misinformative entries from the corpus using information retrieval techniques, development of a codex and labeling strategy for the dataset, and the creation of features for use in machine learning tasks. By identifying the nine most descriptive features with regard to classification as misinformative or non-misinformative through the use of Recursive Feature Elimination, we achieved a classification accuracy of 90.1%, where the dataset is comprised 85.8% of non-misinformative comments. In our opinion, this dataset and analysis will aid the machine learning community in the development of an online misinformation classification system over user-generated content such as medical forum posts.

Social media outlets such as Twitter have become an important forum for peer interaction. Thus the ability to classify latent user attributes, including gender, age, regional origin, and political orientation solely from Twitter user language or similar highly informal content has important applications in advertising, personalization, and recommendation. This paper includes a novel investigation of stacked-SVM-based classification algorithms over a rich set of original features, applied to classifying these four user attributes. It also includes extensive analysis of features and approaches that are effective and not effective in classifying user attributes in Twitter-style informal written genres as distinct from the other primarily spoken genres previously studied in the user-property classification literature. Our models, singly and in ensemble, significantly outperform baseline models in all cases. A detailed analysis of model components and features provides an often entertaining insight into distinctive language-usage variation across gender, age, regional origin and political orientation in modern informal communication.

In recent years, microblogs have become an important source for reporting real-world events. A real-world occurrence reported in microblogs is also called a social event. Social events may hold critical materials that describe the situations during a crisis. In real applications, such as crisis management and decision making, monitoring the critical events over social streams will enable watch officers to analyze a whole situation that is a composite event, and make the right decision based on the detailed contexts such as what is happening, where an event is happening, and who are involved. Although there has been significant research effort on detecting a target event in social networks based on a single source, in crisis, we often want to analyze the composite events contributed by different social users. So far, the problem of integrating ambiguous views from different users is not well investigated. To address this issue, we propose a novel framework to detect composite social events over streams, which fully exploits the information of social data over multiple dimensions. Specifically, we first propose a graphical model called location-time constrained topic (LTT) to capture the content, time, and location of social messages. Using LTT, a social message is represented as a probability distribution over a set of topics by inference, and the similarity between two messages is measured by the distance between their distributions. Then, the events are identified by conducting efficient similarity joins over social media streams. To accelerate the similarity join, we also propose a variable dimensional extendible hash over social streams. We have conducted extensive experiments to prove the high effectiveness and efficiency of the proposed approach.

Twitter presents an unparalleled opportunity for researchers from various ï¬elds to gather valuable and genuine textual data from millions of people. However, the collection pro-cess, as well as the analysis of these data require different kinds of skills (e.g. programing, data mining) which can be an obstacle for people who do not have this background. In this paper we present CATS, an open source, scalable, Web application designed to support researchers who want to carry out studies based on tweets. The purpose of CATS is twofold: (i) allow people to collect tweets (ii) enable them to analyze these tweets thanks to efï¬cient tools (e.g. event detection, named-entity recognition, topic modeling, word-clouds). What is more, CATS relies on a distributed imple-mentation which can deal with massive data streams.

The proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers have made it challenging to identify trustworthy news sources, thus increasing the need for computational tools able to provide insights into the reliability of online content. In this paper, we focus on the automatic identification of fake content in online news. Our contribution is twofold. First, we introduce two novel datasets for the task of fake news detection, covering seven different news domains. We describe the collection, annotation, and validation process in detail and present several exploratory analyses on the identification of linguistic differences in fake and legitimate news content. Second, we conduct a set of learning experiments to build accurate fake news detectors, and show that we can achieve accuracies of up to 76%. In addition, we provide comparative analyses of the automatic and manual identification of fake news.

Sentiment analysis is considered to be a category of machine learning and natural language processing. It is used to extricate, recognize, or portray opinions from different content structures, including news, audits and articles and categorizes them as positive, neutral and negative. It is difficult to predict election results from tweets in different Indian languages. We used Twitter Archiver tool to get tweets in Hindi language. We performed data (text) mining on 42,235 tweets collected over a period of a month that referenced five national political parties in India, during the campaigning period for general state elections in 2016. We made use of both supervised and unsupervised approaches. We utilized Dictionary Based, Naive Bayes and SVM algorithm to build our classifier and classified the test data as positive, negative and neutral. We identified the sentiment of Twitter users towards each of the considered Indian political parties. The results of the analysis for Naive Bayes was the BJP (Bhartiya Janta Party), for SVM it was the BJP (Bhartiya Janta Party) and for the Dictionary Approach it was the Indian Nathional Congress. SVM predicted a 78.4% chance that the BJP would win more elections in the general election due to the positive sentiment they received in tweets. As it turned out, BJP won 60 out of 126 constituencies in the 2016 general election, far more than any other political party as the next party (the Indian National Congress) only won 26 out of 126 constituencies.

Cyberbullying has emerged as a serious societal and public health problem that demands accurate methods for the detection of cyber-bullying instances in an effort to mitigate the consequences. While techniques to automatically detect cyberbullying incidents have been developed, the scalability and timeliness of existing cyberbullying detection approaches have largely been ignored. We address this gap by formulating cyberbullying detection as a sequential hypothesis testing problem. Based on this formulation, we propose a novel algorithm designed to reduce the time to raise a cyberbullying alert by drastically reducing the number of feature evaluations necessary for a decision to be made. We demonstrate the effectiveness of our approach using a real-world dataset from Twitter, one of the top five networks with the highest percentage of users reporting cyberbullying instances. We show that our approach is highly scalable while not sacrificing accuracy for scalability.

Crime analysis has become a critical area for helping law enforcement agencies to protect civilians. As a result of a rapidly increasing population, crime rates have increased dramatically, and appropriate analysis has become a timeconsuming effort. Text mining is an effective tool that may help to solve this problem to classify crimes in effective manner. The proposed system aims to detect and classify crimes in Twitter posts that written in the Arabic language, one of the most widespread languages today. In this paper, classification techniques are used to detect crimes and identify their nature by different classification algorithms. The experiments evaluate different algorithms, such as SVM, DT, CNB, and KNN, in terms of accuracy and speed in the crime domain. Also, different features extraction techniques are evaluated, including rootbased stemming, light stemming, n-gram. The experiments revealed the superiority of n-gram over other techniques. Specifically, the results indicate the superiority of SVM with trigram over other classifiers, with a 91.55% accuracy. Keywords—Crimes; text mining; classification; features extraction techniques; arabic posts; twitter

Presence of hyperlink in a tweet is a strong indication of tweet being more informative. In this paper, we study the problem of hashtag recommendation for hyperlinked tweets (i.e., tweets containing links to Web pages). By recommending hashtags to hyperlinked tweets, we argue that the functions of hashtags such as providing the right context to interpret the tweets, tweet categorization, and tweet promotion, can be extended to the linked documents. The proposed solution for hashtag recommendation consists of two phases. In the first phase, we select candidate hashtags through five schemes by considering the similar tweets, the similar documents, the named entities contained in the document, and the domain of the link. In the second phase, we formulate the hashtag recommendation problem as a learning to rank problem and adopt RankSVM to aggregate and rank the candidate hashtags. Our experiments on a collection of 24 million tweets show that the proposed solution achieves promising results.

Social Network Analysis (SNA) is a field of study that focuses on analyzing user profiles and participations on social network channels in order to model relationships between people and to predict certain behaviors or knowledge. To achieve their goals, researchers, interested in SNA, have to extract content and structure from the numerous social networks available today. Existing tools, which help in this task, often require substantial pre-processing or good programming skills which may not be available for all SNA researchers. This paper describes RUM, a data extraction tool which allows researchers to easily extract several types of content and structure that are available on Facebook pages. Consequently, the extracted data can be saved and analyzed. RUM Extractor is easy to set up and use, and it gives flexible options to users to specify the type and amount of content and structure they want to retrieve. The paper also demonstrates how RUM can be exploited by collecting and further analyzing data collected from two popular Arabic news pages.


Question: Rumor identification and categorization in Twitter data analysis.

This paper explores the enhanced accuracy achievable by an unlexicalized Probabilistic Context-Free Grammar (PCFG) in parsing tasks, utilizing straightforward, linguistically-driven state splits to mitigate false independence assumptions inherent in vanilla treebank grammars. The achieved performance of 86.36% (LP/LR F1) surpasses that of early lexicalized PCFG models and closely approaches the current state-of-the-art, offering potential beyond setting a robust lower bound for unlexicalized models' accuracy. An unlexicalized PCFG is more compact, easier to replicate, and interpret compared to complex lexical models. Its parsing algorithms are simpler, widely understood, with lower complexity and easier to optimize. In the early 1990s, as probabilistic techniques gained prominence in NLP, parsing research revisited PCFGs. However, initial outcomes on PCFGs' utility for parse disambiguation and language modeling were underwhelming, leading to the belief that lexicalized PCFGs (with head words annotating phrasal nodes) were crucial for high-performance parsing. This aligned with the success of word n-gram models in speech recognition and the interest in lexicalized grammars. Over the following decade, lexicalized PCFG models achieved significant success in parse disambiguation and language modeling. Yet, the role of lexicalization in these parsers' effectiveness has been questioned. Johnson (1998) demonstrated that the performance of an unlexicalized PCFG on the Penn treebank could be dramatically improved by simply annotating each node with its parent category. The Penn treebank covering PCFG is inadequate for parsing due to its overly strong context-freedom assumptions, which are weakened by this modification, improving the model significantly. More recently, Gildea (2001) found that removing bilexical probabilities from a high-quality lexicalized PCFG parser barely affects performance, with a maximum 0.5% decrease for test text from the same domain as the training data, and no effect for different domain test text. This challenges the intuition that lexicalized PCFGs should be highly effective, as demonstrated in resolving ambiguities like prepositional phrase attachments. We view this as a reflection of the fundamental sparseness of lexical dependency information in the Penn Treebank, as one million words of training data are insufficient. Even for central topics like stocks in the treebank's Wall Street Journal text, many plausible dependencies occur infrequently or not at all. The best-performing lexicalized PCFGs have increasingly

Lately, Twitter has grown to be one of the most favored ways of disseminating information to people around the globe. However, the main challenge faced by the users is how to assess the credibility of information posted through this social network in real time. In this paper, we present a real-time content credibility assessment system named CredFinder, which is capable of measuring the trustworthiness of information through user analysis and content analysis. The proposed system is capable of providing a credibility score for each user's tweets. Hence, it provides users with the opportunity to judge the credibility of information faster. CredFinder consists of two parts: a frontend in the form of an extension to the Chrome browser that collects tweets in real time from a Twitter search or a user-timeline page and a backend that analyzes the collected tweets and assesses their credibility.

A FUZZY LOGIC-BASED TEXT CLASSIFICATION METHOD FOR SOCIAL MEDIA

The proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers have made it challenging to identify trustworthy news sources, thus increasing the need for computational tools able to provide insights into the reliability of online content. In this paper, we focus on the automatic identification of fake content in online news. Our contribution is twofold. First, we introduce two novel datasets for the task of fake news detection, covering seven different news domains. We describe the collection, annotation, and validation process in detail and present several exploratory analyses on the identification of linguistic differences in fake and legitimate news content. Second, we conduct a set of learning experiments to build accurate fake news detectors, and show that we can achieve accuracies of up to 76%. In addition, we provide comparative analyses of the automatic and manual identification of fake news.

Our personal social networks are big and cluttered, and currently there is no good way to organize them. Social networking sites allow users to manually categorize their friends into social circles (e.g. ‘circles’ on Google+, and ‘lists’ on Facebook and Twitter), however they are laborious to construct and must be updated whenever a user’s network grows. We define a novel machine learning task of identifying users’ social circles. We pose the problem as a node clustering problem on a user’s ego-network, a network of connections between her friends. We develop a model for detecting circles that combines network structure as well as user profile information. For each circle we learn its members and the circle-specific user profile similarity metric. Modeling node membership to multiple circles allows us to detect overlapping as well as hierarchically nested circles. Experiments show that our model accurately identifies circles on a diverse set of data from Facebook, Google+, and Twitter for all of which we obtain hand-labeled ground-truth.

We developed a social media analytics and research testbed (SMART) dashboard for monitoring Twitter messages and tracking the diffusion of information in different cities. SMART dashboard is an online geo-targeted search and analytics tool, including an automatic data processing procedure to help researchers to 1) search tweets in different cities; 2) filter noise (such as removing redundant retweets and using machine learning methods to improve precision); 3) analyze social media data from a spatiotemporal perspective, and 4) visualize social media data in various ways (such as weekly and monthly trends, top URLs, top retweets, top mentions, or top hashtags). By monitoring social messages in geo-targeted cities, we hope that SMART dashboard can assist researchers investigate and monitor various topics, such as flu outbreaks, drug abuse, and Ebola epidemics at the municipal level.

The online social networks (OSNs) have become an important platform for detecting real-world event in recent years. These real-world events are detected by analyzing huge social-stream data available on different OSN platforms. Event detection has become significant because it contains substantial information which describes different scenarios during events or crisis. This information further helps to enable contextual decision making, regarding the event location, content and the temporal specifications. Several studies exist, which offers plethora of frameworks and tools for detecting and analyzing events used for applications like crisis management, monitoring and predicting events in different OSN platforms. In this paper, a survey is done for event detection techniques in OSN based on social text streams—newswire, web forums, emails, blogs and microblogs, for natural disasters, trending or emerging topics and public opinion-based events. The work done and the open problems are explicitly mentioned for each social stream. Further, this paper elucidates the list of event detection tools available for the researchers.

Abstract—Nowadays social media information, such as news, links, images, or VDOs, is shared extensively. However, the effectiveness of disseminating information through social media lacks in quality: less fact checking, more biases, and several rumors. Many researchers have investigated about credibility on Twitter, but there is no the research report about credibility information on Facebook. This paper proposes features for measuring credibility on Facebook information. We developed the system for credibility on Facebook. First, we have developed FB credibility evaluator for measuring credibility of each post by manual human’s labelling. We then collected the training data for creating a model using Support Vector Machine (SVM). Secondly, we developed a chrome extension of FB credibility for Facebook users to evaluate the credibility of each post. Based on the usage analysis of our FB credibility chrome extension, about 81% of users’ responses agree with suggested credibility automatically computed by the proposed system.

Use of data generated through social media for health studies is gradually increasing. Twitter is a short-text message system developed 6 years ago, now with more than 100 million users generating over 300 million Tweets every day. Twitter may be used to gain real-world insights to promote healthy behaviors. The purposes of this paper are to describe a practical approach to analyzing Tweet contents and to illustrate an application of the approach to the topic of physical activity. The approach includes five steps: (1) selecting keywords to gather an initial set of Tweets to analyze; (2) importing data; (3) preparing data; (4) analyzing data (topic, sentiment, and ecologic context); and (5) interpreting data. The steps are implemented using tools that are publically available and free of charge and designed for use by researchers with limited programming skills. Content mining of Tweets can contribute to addressing challenges in health behavior research.

Twitter and other microblogging services have become indispensable sources of information in today's web. Understanding the main factors that make certain pieces of information spread quickly in these platforms can be decisive for the analysis of opinion formation and many other opinion mining tasks.
 This paper addresses important questions concerning the spread of information on Twitter. What makes Twitter users retweet a tweet? Is it possible to predict whether a tweet will become "viral", i.e., will be frequently retweeted? To answer these questions we provide an extensive analysis of a wide range of tweet and user features regarding their influence on the spread of tweets. The most impactful features are chosen to build a learning model that predicts viral tweets with high accuracy. All experiments are performed on a real-world dataset, extracted through a public Twitter API based on user IDs from the TREC 2011 microblog corpus.

The growing number of Twitter users makes it a valuable source of information to study what is happening right now. Users often use Twitter to report real-life events. Here we are only interested in following the financial community. This paper focuses on detecting events popularity through sentiment analysis of tweets published by the financial community on the Twitter universe. The detection of events popularity on Twitter makes this a non-trivial task due to noisy content that often are the tweets. This work aims to filter out all the noisy tweets in order to analyze only the tweets that influence the financial market, more specifically the thirty companies that compose the Dow Jones Average. To perform these tasks, in this paper it is proposed a methodology that starts from the financial community of Twitter and then filters the collected tweets, makes the sentiment analysis of the tweets and finally detects the important events in the life of companies. © 2016 Elsevier Ltd. All rights reserved.


Question: Creation and Verification of the Transgender Attitudes and Beliefs Scale.

This paper offers a comprehensive perspective on determining the optimal sample size in factor analysis, addressing a common misunderstanding that a fixed minimum sample size or ratio applies universally across studies. The authors clarify that the requisite sample size is contingent on various study-specific factors, such as the variables' communality and the degree of overdetermination of the factors. They propose a theoretical and mathematical framework to elucidate and forecast these relationships. The validity of their hypotheses is substantiated through a simulation study employing synthetic data. The findings debunk conventional wisdom and provide a foundation for developing guidelines for sample size selection in factor analysis, emphasizing the importance of context-specific considerations in achieving stable and accurate factor solutions.

**Question**: The Role of Social Media in the Development of Mental Health Stigma

**Documents**: The impact of social media on mental health: A systematic review
Social media has become an integral part of modern life, influencing various aspects of human behavior and social interactions. This systematic review examines the impact of social media on mental health, with a focus on the development of mental health stigma. The review includes 25 studies published between 2010 and 2020, which collectively suggest that social media can both exacerbate and mitigate mental health stigma. On one hand, social media platforms can amplify negative stereotypes and misinformation about mental illness, contributing to increased stigma. On the other hand, they can also serve as a tool for education and awareness, potentially reducing stigma by providing accurate information and promoting positive narratives about mental health. The review highlights the need for more research to understand the complex relationship between social media and mental health stigma, including the role of specific features and user demographics. Recommendations for future studies include exploring the impact of social media on different subgroups of the population and examining the effectiveness of stigma-reduction interventions delivered through social media.

**Similar Documents**: This systematic review explores the multifaceted influence of social media on mental health, particularly focusing on its role in the evolution of mental health stigma. Drawing from 25 studies conducted between 2010 and 2020, it reveals that social media platforms can both intensify and alleviate mental health stigma. They can propagate negative stereotypes and misinformation, thereby exacerbating stigma, but also offer a platform for education and positive discourse, potentially diminishing stigma by disseminating accurate information and narratives about mental health. The review underscores the necessity for further research to decipher the intricate interplay between social media and mental health stigma, including the effects of distinct platform features and user characteristics. Suggestions for future investigations include scrutinizing the

Several studies that measured basic human values across countries with the Portrait Values Questionnaire (PVQ-21) reported violations of measurement invariance. Such violations may hinder meaningful cross-cultural research on human values because value scores may not be comparable. Schwartz et al. (2012) proposed a refined value theory and a new instrument (PVQ-5X) to measure 19 more narrowly defined values. We tested the measurement invariance of this instrument across eight countries. Configural and metric invariance were established for all values across almost all countries. Scalar invariance was supported across nearly all countries for 10 values. The analyses revealed that the cross-country invariance properties of the values measured with the PVQ-5X are substantially better than those measured with the PVQ-21. DOI: https://doi.org/10.1177/0022022114527348 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-94906 Accepted Version Originally published at: Cieciuch, Jan; Davidov, Eldad; Vecchione, Michele; Beierlein, Constanze; Schwartz, Shalom H (2014). The Cross-National Invariance Properties of a New Scale to Measure 19 Basic Human Values: A Test Across Eight Countries. Journal of Cross-Cultural Psychology, 45(5):764-776. DOI: https://doi.org/10.1177/0022022114527348 MEASUREMENT INVARIANCE OF A REFINED VALUES SCALE Jan Cieciuch Institute of Sociology, University of Zurich, Switzerland and University of Finance and Management, Warsaw, Poland Eldad Davidov Institute of Sociology, University of Zurich, Switzerland Michele Vecchione “Sapienza” University of Rome, Italy Constanze Beierlein Leibniz-Institute for the Social Sciences, GESIS, Mannheim, Germany Shalom H. Schwartz The Hebrew University of Jerusalem, Israel and National Research University-Higher School of Economics, Moscow This is a pre-copy-editing, author-produced PDF of an article accepted for publication in the Journal of Cross-Cultural Psychology following peer review. It was first published online in this journal on March 19, 2014. The definitive publisher-authenticated version is available online at: http://jcc.sagepub.com/content/early/2014/03/14/0022022114527348 or under doi: 10.1177/0022022114527348 MEASUREMENT INVARIANCE OF A REFINED VALUES SCALE 1 Running Head: MEASUREMENT INVARIANCE OF A REFINED VALUES SCALE The Cross-National Invariance Properties of a New Scale to Measure 19 Basic Human Values. A Test across Eight Countries Jan Cieciuch jancieciuch@gmail.com Institute of Sociology, University of Zurich, Switzerland and University of Finance and Management, Warsaw, Poland Contact address: Andreasstrasse 15, CH-8050 Zurich, Switzerland Tel: +41 44 635 23 22, Fax: +41 44 635 23 99 Eldad Davidov Institute of Sociology, University of Zurich, Switzerland Michele Vecchione “Sapienza” University of Rome, Italy Constanze Beierlein Leibniz-Institute for the Social Sciences, GESIS, Mannheim, Germany Shalom H. Schwartz The Hebrew University of Jerusalem, Israel and National Research University-Higher School of Economics, Moscow MEASUREMENT INVARIANCE OF A REFINED VALUES SCALE 2 Author Note The work of the first and second authors on this paper was supported by the Scientific Exchange Program (Switzerland) and the Research Priority Program ‘Social Networks’, University of Zurich. The work of the first author was partially supported by Grant DEC2011/01/D/HS6/04077 from the Polish National Science Centre. The work of the fifth author on this paper was partly supported by the Higher School of Economics (HSE) Basic Research Program (International Laboratory of Socio-Cultural Research). The second author would like to thank the EUROLAB, GESIS, Cologne, for their hospitality during work on this paper. The authors would like to thank Lisa Trierweiler for the English proof of the manuscript. MEASUREMENT INVARIANCE OF A REFINED VALUES SCALE 3 Abstract Several studies that measured basic human values across countries with the Portrait Values Questionnaire (PVQ-21) reported violations of measurement invariance. Such violations may hinder meaningful cross-cultural research on human values because value scores may not be comparable. Schwartz et al. (2012) proposed a refined value theory and a new instrument (PVQ-5X) to measure 19 more narrowly defined values. We tested the measurement invariance of this instrument across eight countries. Configural and metric invariance were established for all values across almost all countries. Scalar invariance was supported across nearly all countries for 10 values. The analyses revealed that the cross-country invariance properties of the values measured with the PVQ-5X are substantially better than those measured with the PVQ-21.Several studies that measured basic human values across countries with the Portrait Values Questionnaire (PVQ-21) reported violations of measurement invariance. Such violations may hinder meaningful cross-cultural research on human values because value scores may not be comparable. Schwartz et al. (2012) proposed a refined value theory and a new instrument (PVQ-5X) to measure 19 more narrowly defined values. We tested the measurement invariance of this instrument across eight countries. Configural and metric invariance were established for all values across almost all countries. Scalar invariance was supported across nearly all countries for 10 values. The analyses revealed that the cross-country invariance properties of the values measured with the PVQ-5X are substantially better than those measured with the PVQ-21.

Many of the instruments to measure information and system quality were developed in the context of mainframe and PC-based technologies of yesteryears. With the proliferation of the Internet and World Wide Web applications, users are increasingly interfacing and interacting with web-based applications. It is, therefore, important to develop new instruments and scales, which are directly targeted to these new interfaces and applications. In this article, we report on the development of an instrument that captures key characteristics of web site quality from the userâ€TMs perspective. The 25item instrument measures four dimensions of web quality: specific content, content quality, appearance and technical adequacy. While improvements are possible, the instrument exhibits excellent psychometric properties. The instrument would be useful to organizations and web designers as it provides an aggregate measure of web quality, and to researchers in related web research. a 1, b Purchase Export Previous article Next article Check if you have access through your login credentials or your institution.

Vegetarianism within the U.K. is growing in popularity, with the current estimate of 7% of the population eating a vegetarian diet. This study examined differences between the attitudes and beliefs of four dietary groups (meat eaters, meat avoiders, vegetarians and vegans) and the extent to which attitudes influenced intentions to follow each diet. In addition, the role of attitudinal ambivalence as a moderator variable was examined. Completed questionnaires were obtained from 111 respondents (25 meat eaters, 26 meat avoiders, 34 vegetarians, 26 vegans). In general, predictions were supported, in that respondents displayed most positive attitudes and beliefs towards their own diets, and most negative attitudes and beliefs towards the diet most different form their own. Regression analyses showed that, as predicted by the Theory of Planned Behaviour, attitudes, subjective norm and perceived behavioural control were significant predictors of intention to follow each diet (apart from the vegetarian diet, where subjective norm was non-significant). In each case, attitudinal ambivalence was found to moderate the attitude-intention relationship, such that attitudes were found to be stronger predictors at lower levels of ambivalence. The results not only highlight the extent to which such alternative diets are an interesting focus for psychological research, but also lend further support to the argument that ambivalence in an important influence on attitude strength.

The aim of this study is to describe the factor structure and psychometric properties of an extended version of the Children’s Automatic Thoughts Scale (CATS), the CATS-Negative/Positive (CATS-N/P). The CATS was originally designed to assess negative self-statements in children and adolescents. However, positive thoughts also play a major role in childhood disorders such as anxiety and depression. Therefore, positive self-statements were added to the CATS. The CATS-N/P was administered to a community sample of 554 children aged 8–18 years. The results of a confirmatory factor analysis revealed that the positive self-statements formed a separate and psychometrically sound factor. Internal and short-term test–retest reliability was good. Boys reported more hostile and positive thoughts than girls; and younger children reported more negative thoughts concerning physical threat, social threat, and failure than older children. In conclusion, the results of the current study support the use of the CATS-N/P for the measurement of positive and negative thoughts in children. The application of the CATS-N/P can facilitate further research on cognitive factors in different childhood disorders.


Question: Creation and Testing of the Transgender Attitudes and Beliefs Scale.

In the realm of data collection, particularly through anonymous online surveys under obligatory participation scenarios (common with student samples), ensuring data quality is paramount. Yet, the literature offers limited guidance on identifying careless responses, a critical issue for maintaining the integrity of survey results. Previous research has proposed various methods to flag careless responders, utilizing specific indices derived from the data. However, there has been scarce examination of the interrelationships among these indicators or the distinct data patterns they can identify. Across two studies, we scrutinized several techniques for pinpointing careless responses: (a) special items crafted to detect such responses, (b) consistency indices derived from standard survey items, (c) multivariate outlier analysis, (d) response timing, and (e) self-reported conscientiousness. Our findings revealed two discernible patterns of careless response—random and nonrandom—requiring different indicators for accurate identification. We also discovered that roughly 10%-12% of undergraduate students participating in lengthy surveys for academic credit exhibited careless response behavior. In the second study, we created data with predetermined random response patterns to evaluate the effectiveness of several careless response indicators. Our analysis showed that the data's nature significantly impacts the indicators' ability to pinpoint careless responses. To ensure high-quality data, we recommend utilizing identified rather than anonymous responses, integrating instructed response items before data collection, and applying consistency indices and multivariate outlier analysis. These measures collectively contribute to safeguarding the validity of survey outcomes.

In the past, most studies used the technology acceptance model (TAM) to survey the subjective perception of users in using information technology. The usability test was also used to assess the ease of use of user interfaces. This study introduces a conceptual framework to explore the relationship between user’s beliefs of TAM and usability testing attributes. Usability testing was conducted on an eCampus learning system with a mobile device. TAM data was collected from the participants for analyzing a possible relationship. The findings of this study reveal that TAM results contradict the usability test results in certain areas. The focus of our proposed research model is supported from the causality between perceived ease of use and usability; however, the correlation between perceived usefulness and usability remains unclear.

Two studies investigated relationships among individual differences in implicit and explicit prejudice, right-wing ideology, and rigidity in thinking. The first study examined these relationships focusing on White Americans' prejudice toward Black Americans. The second study provided the first test of implicit ethnocentrism and its relationship to explicit ethnocentrism by studying the relationship between attitudes toward five social groups. Factor analyses found support for both implicit and explicit ethnocentrism. In both studies, mean explicit attitudes toward out groups were positive, whereas implicit attitudes were negative, suggesting that implicit and explicit prejudices are distinct; however, in both studies, implicit and explicit attitudes were related (r = .37, .47). Latent variable modeling indicates a simple structure within this ethnocentric system, with variables organized in order of specificity. These results lead to the conclusion that (a) implicit ethnocentrism exists and (b) it is related to and distinct from explicit ethnocentrism.