Question: Ten straightforward guidelines for creating user-friendly software in computational biology.

This paper explores the impact of social coding platforms, specifically GitHub, on transparency and collaboration within the open-source software community. Social applications on the web, like GitHub, enable users to track and follow the activities of others, regardless of their location or organizational ties, fostering a level of transparency that can significantly enhance collaboration and knowledge sharing in complex, knowledge-intensive tasks. Through detailed interviews with both core and peripheral GitHub users, the study investigates how transparency facilitates large-scale, distributed collaborations and the formation of communities of practice. The findings reveal that users derive a wealth of social insights from the networked activity data available on GitHub. For instance, they can deduce another user's technical objectives and vision from their code edits, or predict which among several comparable projects is most likely to succeed in the long run. These insights are then integrated into effective strategies for coordinating efforts, advancing technical proficiency, and managing one's reputation within the community. This demonstrates how social coding platforms, by enabling transparency, not only support but also enrich collaborative software development and learning experiences.

OBJECTIVE
Ontologies are widely used in the biomedical domain. While many tools exist for the edition, alignment or evaluation of ontologies, few solutions have been proposed for ontology programming interface, i.e. for accessing and modifying an ontology within a programming language. Existing query languages (such as SPARQL) and APIs (such as OWLAPI) are not as easy-to-use as object programming languages are. Moreover, they provide few solutions to difficulties encountered with biomedical ontologies. Our objective was to design a tool for accessing easily the entities of an OWL ontology, with high-level constructs helping with biomedical ontologies.

METHODS
From our experience on medical ontologies, we identified two difficulties: (1) many entities are represented by classes (rather than individuals), but the existing tools do not permit manipulating classes as easily as individuals, (2) ontologies rely on the open-world assumption, whereas the medical reasoning must consider only evidence-based medical knowledge as true. We designed a Python module for ontology-oriented programming. It allows access to the entities of an OWL ontology as if they were objects in the programming language. We propose a simple high-level syntax for managing classes and the associated "role-filler" constraints. We also propose an algorithm for performing local closed world reasoning in simple situations.

RESULTS
We developed Owlready, a Python module for a high-level access to OWL ontologies. The paper describes the architecture and the syntax of the module version 2. It details how we integrated the OWL ontology model with the Python object model. The paper provides examples based on Gene Ontology (GO). We also demonstrate the interest of Owlready in a use case focused on the automatic comparison of the contraindications of several drugs. This use case illustrates the use of the specific syntax proposed for manipulating classes and for performing local closed world reasoning.

CONCLUSION
Owlready has been successfully used in a medical research project. It has been published as Open-Source software and then used by many other researchers. Future developments will focus on the support of vagueness and additional non-monotonic reasoning feature, and automatic dialog box generation.

Mobile computing presents formidable challenges not only to the design of applications but also to each and every phase of the systems lifecycle. In particular, the HCI community is still struggling with the challenges that mobile computing poses to evaluation. Expert-based evaluation techniques are well known and they do enable a relatively quick and easy evaluation. Heuristic evaluation, in particular, has been widely applied and investigated, most likely due to its efficiency in detecting most of usability flaws at front of a rather limited investment of time and human resources in the evaluation. However, the capacity of expert-based techniques to capture contextual factors in mobile computing is a major concern. In this paper, we report an effort for realizing usability heuristics appropriate for mobile computing. The effort intends to capture contextual requirements while still drawing from the inexpensive and flexible nature of heuristic-based techniques. This work has been carried out in the context of a research project task geared toward developing a heuristic-based evaluation methodology for mobile computing. This paper describes the methodology that we adopted toward realizing mobile heuristics. It also reports a study that we carried out in order to assess the relevance of the realized mobile heuristics by comparing their performance with that of the standard/traditional usability heuristics. The study yielded positive results in terms of the number of usability flaws identified and the severity ranking assigned.

Proponents of open source style software development claim that better software is produced using this model compared with the traditional closed model. However, there is little empirical evidence in support of these claims. In this paper, we present the results of a pilot case study aiming: (a) to understand the implications of structural quality; and (b) to figure out the benefits of structural quality analysis of the code delivered by open source style development. To this end, we have measured quality characteristics of 100 applications written for Linux, using a software measurement tool, and compared the results with the industrial standard that is proposed by the tool. Another target of this case study was to investigate the issue of modularity in open source as this characteristic is being considered crucial by the proponents of open source for this type of software development. We have empirically assessed the relationship between the size of the application components and the delivered quality measured through user satisfaction. We have determined that, up to a certain extent, the average component size of an application is negatively related to the user satisfaction for this application.

Recent studies in the literature have shown that syntax remains a significant barrier to novice computer science students in the field. While this syntax barrier is known to exist, whether and how it varies across programming languages has not been carefully investigated. For this article, we conducted four empirical studies on programming language syntax as part of a larger analysis into the, so called, programming language wars. We first present two surveys conducted with students on the intuitiveness of syntax, which we used to garner formative clues on what words and symbols might be easy for novices to understand. We followed up with two studies on the accuracy rates of novices using a total of six programming languages: Ruby, Java, Perl, Python, Randomo, and Quorum. Randomo was designed by randomly choosing some keywords from the ASCII table (a metaphorical placebo). To our surprise, we found that languages using a more traditional C-style syntax (both Perl and Java) did not afford accuracy rates significantly higher than a language with randomly generated keywords, but that languages which deviate (Quorum, Python, and Ruby) did. These results, including the specifics of syntax that are particularly problematic for novices, may help teachers of introductory programming courses in choosing appropriate first languages and in helping students to overcome the challenges they face with syntax.

To be useful, software must respond to events in a predictable manner. The results can then be used as a window to the interior workings of the code, revealing some of the mechanisms of operations, which may be used to find ways to make it fail in a dangerous way. To some, the window is as clear as a six inch thick pane of lead, but to those with a high level of understanding it can be clear, or at the very least serve as a keyhole. This is an allusion to the old detective stories where someone looks through the keyhole to see what is behind the door. For these reasons, no software that interacts with humans can ever be considered completely secure, and human error in the development of the software can leave the equivalent of keyholes throughout the code.

Automatic recognition of biomedical names is an essential task in biomedical information extraction, presenting several complex and unsolved challenges. In recent years, various solutions have been implemented to tackle this problem. However, limitations regarding system characteristics, customization and usability still hinder their wider application outside text mining research. We present Gimli, an open-source, state-of-the-art tool for automatic recognition of biomedical names. Gimli includes an extended set of implemented and user-selectable features, such as orthographic, morphological, linguistic-based, conjunctions and dictionary-based. A simple and fast method to combine different trained models is also provided. Gimli achieves an F-measure of 87.17% on GENETAG and 72.23% on JNLPBA corpus, significantly outperforming existing open-source solutions. Gimli is an off-the-shelf, ready to use tool for named-entity recognition, providing trained and optimized models for recognition of biomedical entities from scientific text. It can be used as a command line tool, offering full functionality, including training of new models and customization of the feature set and model parameters through a configuration file. Advanced users can integrate Gimli in their text mining workflows through the provided library, and extend or adapt its functionalities. Based on the underlying system characteristics and functionality, both for final users and developers, and on the reported performance results, we believe that Gimli is a state-of-the-art solution for biomedical NER, contributing to faster and better research in the field. Gimli is freely available at http://bioinformatics.ua.pt/gimli .

Considers how dynamic queries allow users to "fly through" databases by adjusting widgets and viewing the animated results. In studies, users reacted to this approach with an enthusiasm more commonly associated with video games. Adoption requires research into retrieval and display algorithms and user-interface design. The author discusses how experts may benefit from visual interfaces because they will be able to formulate more complex queries and interpret intricate results.<<ETX>>


Question: Ten straightforward guidelines for creating user-friendly software in computational biology.

Navigating the Landscape of Technical Debt: Insights from Google's Build System

In the dynamic environment of Google's extensive and swiftly evolving codebase, software engineers continually confront and manage various types of technical debt. This debt accumulates through compromises made during development, often prioritizing immediate functionality over optimal code quality. Google's engineers are proactive in addressing this debt, engaging in specific initiatives like Fixit days, where they focus on code cleanup, or working within specialized teams known as janitors, cultivators, or demolition experts, each dedicated to tackling different aspects of technical debt.

This paper explores several initiatives aimed at quantifying and mitigating technical debt within Google's BUILD files and associated redundant code. The focus is on three main areas: dependency specifications, unbuildable targets, and superfluous command line flags. By addressing these specific forms of debt, the efforts often reveal additional layers of technical debt that require strategic management.

For instance, managing dependency specifications involves ensuring that each module or component of the codebase is properly linked to its dependencies, avoiding the need for manual intervention or ad-hoc solutions. Unbuildable targets refer to code that is no longer necessary or functional but remains in the codebase, contributing to unnecessary complexity and maintenance overhead. Finally, unnecessary command line flags are options in the build process that serve no purpose or have been replaced by newer, more efficient methods.

These efforts not only streamline the codebase but also improve overall code quality and maintainability. They highlight the importance of continuous code review and maintenance in managing technical debt, a critical aspect of software engineering in a fast-paced, high-scale environment like Google's. By addressing technical debt, engineers can ensure that the codebase remains robust, efficient, and adaptable to future changes and challenges.

Although quantitative PCR (qPCR) is becoming the method of choice for expression profiling of selected genes, accurate and straightforward processing of the raw measurements remains a major hurdle. Here we outline advanced and universally applicable models for relative quantification and inter-run calibration with proper error propagation along the entire calculation track. These models and algorithms are implemented in qBase, a free program for the management and automated analysis of qPCR data.

Permission is granted to copy, distribute and/or modify this document under the terms of the Open Publication License, version 2.0. $Date: 2002/08/02 09:02:14 $ Revision History Revision 1.57 11 September 2000 esr New major section “How Many Eyeballs Tame Complexity”. Revision 1.52 28 August 2000 esr MATLAB is a reinforcing parallel to Emacs. Corbatoó & Vyssotsky got it in 1965. Revision 1.51 24 August 2000 esr First DocBook version. Minor updates to Fall 2000 on the time-sensitive material. Revision 1.49 5 May 2000 esr Added the HBS note on deadlines and scheduling. Revision 1.51 31 August 1999 esr This the version that O’Reilly printed in the first edition of the book. Revision 1.45 8 August 1999 esr Added the endnotes on the Snafu Principle, (pre)historical examples of bazaar development, and originality in the bazaar. Revision 1.44 29 July 1999 esr Added the “On Management and the Maginot Line” section, some insights about the usefulness of bazaars for exploring design space, and substantially improved the Epilog. Revision 1.40 20 Nov 1998 esr Added a correction of Brooks based on the Halloween Documents. Revision 1.39 28 July 1998 esr I removed Paul Eggert’s ’graph on GPL vs. bazaar in response to cogent aguments from RMS on Revision 1.31 February 1

This paper describes persuasive design methods and compares this to an application currently under development for diabetes mellitus patients. Various elements of persuasion and a categorization of persuasion types are mentioned. Also discussed are principles of how successful persuasion should be designed, as well as the practical applications and ethics of persuasive design. This paper is not striving for completeness of theories on the topic, but uses the theories to compare it to an application intended for diabetes mellitus patients. The results of this comparison can be used for improvements of the application.

Context : Exploration of user interfaces has attracted a considerable attention in Semantic Web research in the last several years. However, the overall impact of these research efforts and the level of the maturity of their results are still unclear. Objective: This study aims at surveying existing research on Semantic Web user interfaces in order to synthesise their results, assess the level of the maturity of the research results, and identify needs for future research. Method : This paper reports the results of a systematic literature review and mapping study on Semantic Web user interfaces published between 2003 and 2011. After collecting a corpus of 87 papers, the collected papers are analysed through four main dimensions: general information such as sources, types and time of publication; covered research topics; quality of evaluations; and finally, used research methods and reported rigour of their use. Results: Based on only the obtained data, the paper provides a high level overview of the works reviewed, a detailed quality assessment of papers involving human subjects and a discussion of the identified gaps in the analysed papers and draws promising venues for future research. Conclusion: We arrive at two main conclusions: important gaps exist which hinder a wide adoption of Semantic Web technologies (e.g. lack user interfaces based on semantic technologies for social and mobile applications) and experimental research hardly used any proven theories and instruments specifically designed for investigating technology acceptance.

Purpose: E-books are not as popular as other types of e-publications, such as e-journals and e-newspapers. The possible reasons for this could be because the technology for creating/accessing e-books (both hardware and software) is not yet matured, users’ perspectives about e-books need to be changed and the awareness of e-books needs to be raised. The purpose of this study is to investigate the use and usability of e-books from users’ perspectives in an academic and research environment.

Biological knowledge is inherently complex and so cannot readily be integrated into existing databases of molecular (for example, sequence) data. An ontology is a formal way of representing knowledge in which concepts are described both by their meaning and their relationship to each other. Unique identifiers that are associated with each concept in biological ontologies (bio-ontologies) can be used for linking to and querying molecular databases. This article reviews the principal bio-ontologies and the current issues in their design and development: these include the ability to query across databases and the problems of constructing ontologies that describe complex knowledge, such as phenotypes.

School of Computing and Mathematics, Faculty of Engineering, University of Ulster at Jordanstown, Shore Road, Newtownabbey, County Antrim BT37 0QB, Northern Ireland, UK The Applied Software Systems Laboratory (TASSL), Electrical and Computer Engineering, Rutgers, The State University of New Jersey, 94 Brett Road, Piscataway, NJ 08854-8058, USA School of Computing and Mathematical Sciences, The SRIF/SHEFC Centre for Virtual Organization Technology Enabling Research (VOTER), Glasgow Caledonian University, 70 Cowcaddens Road, Glasgow G4 0BA, UK Institute for Computer Science and Business Information Systems (ICB), University of Duisburg-Essen, Schützenbahn 70, 45117 Essen, Germany


Question: Ten straightforward guidelines for creating user-friendly software in computational biology.

This paper demonstrates the application of causal inference in deciphering the actions of intricate learning systems that engage with their surroundings and forecasting the outcomes of system modifications. These forecasts empower both human decision-makers and algorithms to identify alterations that would enhance system efficacy. The paper substantiates this approach through experiments conducted on the advertisement placement mechanism linked to the Bing search engine, showcasing how counterfactual reasoning can be employed to optimize computational advertising systems.

Context : Exploration of user interfaces has attracted a considerable attention in Semantic Web research in the last several years. However, the overall impact of these research efforts and the level of the maturity of their results are still unclear. Objective: This study aims at surveying existing research on Semantic Web user interfaces in order to synthesise their results, assess the level of the maturity of the research results, and identify needs for future research. Method : This paper reports the results of a systematic literature review and mapping study on Semantic Web user interfaces published between 2003 and 2011. After collecting a corpus of 87 papers, the collected papers are analysed through four main dimensions: general information such as sources, types and time of publication; covered research topics; quality of evaluations; and finally, used research methods and reported rigour of their use. Results: Based on only the obtained data, the paper provides a high level overview of the works reviewed, a detailed quality assessment of papers involving human subjects and a discussion of the identified gaps in the analysed papers and draws promising venues for future research. Conclusion: We arrive at two main conclusions: important gaps exist which hinder a wide adoption of Semantic Web technologies (e.g. lack user interfaces based on semantic technologies for social and mobile applications) and experimental research hardly used any proven theories and instruments specifically designed for investigating technology acceptance.

0950-5849/$ see front matter 2009 Elsevier B.V. A doi:10.1016/j.infsof.2009.11.005 * Corresponding author. Tel.: +44 (0) 1782 734090; E-mail address: m.turner@cs.keele.ac.uk (M. Turne Context: The technology acceptance model (TAM) was proposed in 1989 as a means of predicting technology usage. However, it is usually validated by using a measure of behavioural intention to use (BI) rather than actual usage. Objective: This review examines the evidence that the TAM predicts actual usage using both subjective and objective measures of actual usage. Method: We performed a systematic literature review based on a search of six digital libraries, along with vote-counting meta-analysis to analyse the overall results. Results: The search identified 79 relevant empirical studies in 73 articles. The results show that BI is likely to be correlated with actual usage. However, the TAM variables perceived ease of use (PEU) and perceived usefulness (PU) are less likely to be correlated with actual usage. Conclusion: Care should be taken using the TAM outside the context in which it has been validated. 2009 Elsevier B.V. All rights reserved.

Current user interfaces for automated patient and consumer health care systems can be improved by leveraging the results of several decades of research into effective patient-provider communication skills. A research project is presented in which several such "relational" skills - including empathy, social dialogue, nonverbal immediacy behaviors, and other behaviors to build and maintain good working relationships over multiple interactions - are explicitly designed into a computer interface within the context of a longitudinal health behavior change intervention for physical activity adoption. Results of a comparison among 33 subjects interacting near-daily with the relational system and 27 interacting near-daily with an identical system with the relational behaviors ablated, each for 30 days indicate, that the use of relational behaviors by the system significantly increases working alliance and desire to continue working with the system. Comparison of the above groups to another group of 31 subjects interacting with a control system near-daily for 30 days also indicated a significant increase in proactive viewing of health information.

This paper describes persuasive design methods and compares this to an application currently under development for diabetes mellitus patients. Various elements of persuasion and a categorization of persuasion types are mentioned. Also discussed are principles of how successful persuasion should be designed, as well as the practical applications and ethics of persuasive design. This paper is not striving for completeness of theories on the topic, but uses the theories to compare it to an application intended for diabetes mellitus patients. The results of this comparison can be used for improvements of the application.

We present a new approach to the effective development of menu construction systems that allow to automatically construct a menu that is strongly tailored to the individual requirements and food preferences of a client. In hospitals and other health care institutions dietitians develop diets for clients which need to change their eating habits. Many clients have special needs in regards to their medical conditions, cultural backgrounds, or special levels of nutrient requirements for better recovery from diseases or surgery, etc. Existing computer support for this task is insufficient-many diets are not specifically tailored for the client's needs or require substantial time of a dietitian to be manually developed. Our approach is based on case-based reasoning, an artificial intelligence technique that finds increasing entry into industrial practice. Our approach goes beyond the traditional case-based reasoning (CBR) approach by allowing an incremental improvement of the system's competency during routine use of the system. The improvement of the system takes place through a direct expert user-system interaction while the expert is accomplishing their tasks of constructing a diet for a given client. Whenever the system performs unsatisfactorily, the expert will need to modify the system-produced diet 'manually', i.e. by entering the desired modifications into the system. Our implemented system, menu construction using an incremental knowledge acquisition system (MIKAS), asks the expert for simple explanations for each of the manual actions he/she takes and incorporates the explanations automatically into its knowledge base (KB) so that the system will perform these manually conducted actions automatically at the next occasion. We present MIKAS and discuss the results of our case study. While still being a prototype, the senior clinical dietitian involved in our evaluation studies judges the approach to have considerable potential to improve the daily routine of hospital dietitians as well as to improve the average quality of the dietary advice given to patients within the limited available time for dietary consultations. Our approach opens up a new avenue towards building highly specialised CBR systems in a more cost-effective way. Hence, our approach promises to allow a significantly more widespread development and practical deployment of CBR systems in a large variety of application domains including many medical applications.

School of Computing and Mathematics, Faculty of Engineering, University of Ulster at Jordanstown, Shore Road, Newtownabbey, County Antrim BT37 0QB, Northern Ireland, UK The Applied Software Systems Laboratory (TASSL), Electrical and Computer Engineering, Rutgers, The State University of New Jersey, 94 Brett Road, Piscataway, NJ 08854-8058, USA School of Computing and Mathematical Sciences, The SRIF/SHEFC Centre for Virtual Organization Technology Enabling Research (VOTER), Glasgow Caledonian University, 70 Cowcaddens Road, Glasgow G4 0BA, UK Institute for Computer Science and Business Information Systems (ICB), University of Duisburg-Essen, Schützenbahn 70, 45117 Essen, Germany


Question: A dual-layer message grouping technique for identifying topics on Twitter.

Introducing the Latent Dirichlet Allocation (LDA) Model for Discrete Data Analysis

This paper presents a generative model, Latent Dirichlet Allocation (LDA), designed for text and other forms of discrete data, offering a comprehensive framework that enhances or extends previous models such as the naive Bayes/unigram model, mixture of unigrams, and Hofmann's aspect model, also known as probabilistic latent semantic indexing (pLSI). In the context of text analysis, the LDA model assumes that each document is created through a mixture of topics, where the proportions of these topics are continuous values and are distributed according to a latent Dirichlet random variable. This innovative approach enables efficient inference and learning through the use of variational algorithms. The effectiveness of the LDA model is demonstrated through empirical studies applied to various problems, including text modeling, collaborative filtering, and text classification, showcasing its versatility and superiority over existing models in these domains.

Being a prevalent form of social communications on the Internet, billions of short texts are generated everyday. Discovering knowledge from them has gained a lot of interest from both industry and academia. The short texts have a limited contextual information, and they are sparse, noisy and ambiguous, and hence, automatically learning topics from them remains an important challenge. To tackle this problem, in this paper, we propose a semantics-assisted non-negative matrix factorization (SeaNMF) model to discover topics for the short texts. It effectively incorporates the word-context semantic correlations into the model, where the semantic relationships between the words and their contexts are learned from the skip-gram view of the corpus. The SeaNMF model is solved using a block coordinate descent algorithm. We also develop a sparse variant of the SeaNMF model which can achieve a better model interpretability. Extensive quantitative evaluations on various realworld short text datasets demonstrate the superior performance of the proposed models over several other state-of-the-art methods in terms of topic coherence and classification accuracy. The qualitative semantic analysis demonstrates the interpretability of our models by discovering meaningful and consistent topics. With a simple formulation and the superior performance, SeaNMF can be an effective standard topic model for short texts.

User generated contents (UGCs) carry a huge amount of high quality information. However, the information overload and diversity of UGC sources limit their potential uses. In this research, we propose a framework to organize information from multiple UGC sources by a topic hierarchy which is automatically generated and updated using the UGCs. We explore the unique characteristics of UGCs like blogs, cQAs, microblogs, etc., and introduce a novel scheme to combine them. We also propose a graph-based method to enable incremental update of the generated topic hierarchy. Using the hierarchy, users can easily obtain a comprehensive, in-depth and up-to-date picture of their topics of interests. The experiment results demonstrate how information from multiple heterogeneous sources improves the resultant topic hierarchies. It also shows that the proposed method achieves better F1 scores in hierarchy generation as compared to the state-of-the-art methods.

Much research has been devoted to topic detection from text, but one major challenge has not been addressed: revealing the rich relationships that exist among the detected topics. Finding such relationships is important since many applications are interested in how topics come into being, how they develop, grow, disintegrate, and finally disappear. In this paper, we present a novel method that reveals the connections between topics discovered from the text data. Specifically, our method focuses on how one topic splits into multiple topics, and how multiple topics merge into one topic. We adopt the hierarchical Dirichlet process (HDP) model, and propose an incremental Gibbs sampling algorithm to incrementally derive and refine the labels of clusters. We then characterize the splitting and merging patterns among clusters based on how labels change. We propose a global analysis process that focuses on cluster splitting and merging, and a finer granularity analysis process that helps users to better understand the content of the clusters and the evolution patterns. We also develop a visualization process to present the results.

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.

Topic model building is the basis and the most critical module of cross-language topic detection and tracking. Topic model also can be applied to cross-language text similarity calculation. It can improve the efficiency and the speed of calculation by reducing the texts’ dimensionality. In this paper, we use the LDA model in cross-language text similarity computation to obtain Tibetan-Chinese comparable corpora: (1) Extending Tibetan-Chinese dictionary by extracting Tibetan-Chinese entities from Wikipedia. (2) Using topic model to make the texts mapped to the feature space of topics. (3) Calculating the similarity of two texts in different language according to the characteristics of the news text. The method for text similarity calculation based on LDA model reduces the dimensions of text space vector, and enhances the understanding of the text’s semantics. It also improves the speed and efficiency of calculation.

Web services have attracted much attention from distributed application designers and developers because of their roles in abstraction and interoperability among heterogeneous software systems, and a growing number of distributed software applications have been published as Web services on the Internet. Faced with the increasing numbers of Web services and service users, researchers in the services computing field have attempted to address a challenging issue, i.e., how to quickly find the suitable ones according to user queries. Many previous studies have been reported towards this direction. In this paper, a novel Web service discovery approach based on topic models is presented. The proposed approach mines common topic groups from the service-topic distribution matrix generated by topic modeling, and the extracted common topic groups can then be leveraged to match user queries to relevant Web services, so as to make a better trade-off between the accuracy of service discovery and the number of candidate Web services. Experiment results conducted on two publicly-available data sets demonstrate that, compared with several widely used approaches, the proposed approach can maintain the performance of service discovery at an elevated level by greatly decreasing the number of candidate Web services, thus leading to faster response time.

Traditional topic models are insufficient for topic extraction in social media. The existing methods only consider text information or simultaneously model the posts and the static characteristics of social media. They ignore that one discusses diverse topics when dynamically interacting with different people. Moreover, people who talk about the same topic have different effects on the topic. In this paper, we propose an Interaction-Aware Topic Model (IATM) for microblog conversations by integrating network embedding and user attention. A conversation network linking users based on reposting and replying relationship is constructed to mine the dynamic user behaviours. We model dynamic interactions and user attention so as to learn interaction-aware edge embeddings with social context. Then they are incorporated into neural variational inference for generating the more consistent topics. The experiments on three real-world datasets show that our proposed model is effective.

Social media constitute a challenging new source of information for intelligence gathering and decision making. Twitter is one of the most popular social media sites and often becomes the primary source of information. Twitter messages are short and well suited for knowledge discovery. Twitter provides both researchers and practitioners a free Application Programming Interface (API) which allows them to gather and analyse large data sets of tweets. Twitter information aren't solely tweet texts, as Twitter’s API provides a lot of info to perform attention-grabbing analysis studies. The paper concisely describes method of knowledge gathering and therefore the main areas of knowledge mining, information discovery and information visual image from Twitter information. In this paper we can create a twitter app from which we can fetch the real time twitter tweets on a particular topic and stored it into R and then we can apply several text mining steps on the tweets to pre-process the tweets text and than we

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.

Twitter is a microblogging website where users read and write millions of short messages on a variety of topics every day. This study uses the context of the German federal election to investigate whether Twitter is used as a forum for political deliberation and whether online messages on Twitter validly mirror offline political sentiment. Using LIWC text analysis software, we conducted a content analysis of over 100,000 messages containing a reference to either a political party or a politician. Our results show that Twitter is indeed used extensively for political deliberation. We find that the mere number of messages mentioning a party reflects the election result. Moreover, joint mentions of two parties are in line with real world political ties and coalitions. An analysis of the tweets’ political sentiment demonstrates close correspondence to the parties' and politicians’ political positions indicating that the content of Twitter messages plausibly reflects the offline political landscape. We discuss the use of microblogging message content as a valid indicator of political sentiment and derive suggestions for further research.


Question: A two-level clustering method for identifying topics in Twitter messages.

This paper delves into the challenge of pinpointing influential users on micro-blogging platforms, with Twitter serving as a prime example. Twitter's unique social networking feature, known as "following," allows users to subscribe to the tweets of others without mutual consent. Our analysis of a dataset reveals that a significant portion (72.4%) of Twitter users follow over 80% of their followers, and an even larger percentage (80.5%) have 80% of the users they follow reciprocating. This "reciprocity" can be attributed to the phenomenon of homophily, where people tend to associate with similar individuals. Inspired by these insights, we introduce TwitterRank, an adaptation of the PageRank algorithm, designed to assess user influence in the Twitter ecosystem. TwitterRank incorporates both the topical affinity between users and the link structure to evaluate influence comprehensively. Our empirical evaluation demonstrates that TwitterRank surpasses the current Twitter algorithm and alternative methods, such as the original PageRank and Topic-sensitive PageRank, in identifying influential users.

The ever-growing number of people using Twitter makes it a valuable source of timely information. However, detecting events in Twitter is a difficult task, because tweets that report interesting events are overwhelmed by a large volume of tweets on unrelated topics. Existing methods focus on the textual content of tweets and ignore the social aspect of Twitter. In this paper we propose MABED (Mention-Anomaly-Based Event Detection), a novel method that leverages the creation frequency of dynamic links (i.e. mentions) that users insert in tweets to detect important events and estimate the magnitude of their impact over the crowd. The main advantages of MABED over prior works are that (i) it relies solely on tweets, meaning no external knowledge is required, and that (ii) it dynamically estimates the period of time during which each event is discussed rather than assuming a predefined fixed duration. The experiments we conducted on both English and French Twitter data show that the mention-anomaly-based approach leads to more accurate event detection and improved robustness in presence of noisy Twitter content. Last, we show that MABED helps with the interpretation of detected events by providing clear and precise descriptions.

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).

The popularity of social media in recent years has increased a lot and created new opportunities to study the interactions of different groups of people. Because of this it comes to possible to study the user‟s opinion. Two popular topics in the study of social networks are community detection and finding trends. Trend Analysis is an ongoing field of research in Data mining field, May referred as an “Opinion Mining” by some researchers. Trend Analysis is the computational treatment of opinions, sentiments and subjectivity of text. The related field to Trend Analysis contains finding emotions in a statement which attracted researchers recently are discussed. The main target of this survey is to give nearly full image of Trend Analysis along with the community detection and their related fields with brief details. The main contributions of this paper include the overall analysis to bridge the concept between trend analyses with community detection. To find out the anomaly detection in a community with using the concept of trend analysis is a difficult task.

Document clustering aims to automatically group related document into clusters. If two documents are close to each other in the original document space, they are grouped into the same cluster. If the two documents are far away from each other in the original document space, they tend to be grouped into different cluster. The classical clustering algorithms assign each data to exactly one cluster, but fuzzy c-means allow data belong to different clusters. Fuzzy clustering is a powerful unsupervised method for the analysis of data. Cluster validity measure is useful in estimating the optimal number of clusters. Purity and Entropy are the validity measures used in this

The status updates posted to social networks, such as Twitter and Facebook, contain a myriad of information about what people are doing and watching. During events, such as sports games, many updates are sent describing and expressing opinions about the event. In this paper, we describe an algorithm that generates a journalistic summary of an event using only status updates from Twitter as a source. Temporal cues, such as spikes in the volume of status updates, are used to identify the important moments within an event, and a sentence ranking method is used to extract relevant sentences from the corpus of status updates describing each important moment within an event. We evaluate our algorithm compared to human-generated summaries and the previous best summarization algorithm, and find that the results of our method are superior to the previous algorithm and approach the readability and grammaticality of the human-generated summaries.

Timeline generation is an important research task which can help users to have a quick understanding of the overall evolution of one given topic. Previous methods simply split the time span into fixed, equal time intervals without studying the role of the evolutionary patterns of the underlying topic in timeline generation. In addition, few of these methods take users’ collective interests into considerations to generate timelines. We consider utilizing social media attention to address these two problems due to the facts: 1) social media is an important pool of real users’ collective interests; 2) the information cascades generated in it might be good indicators for boundaries of topic phases. Employing Twitter as a basis, we propose to incorporate topic phases and user’s collective interests which are learnt from social media into a unified timeline generation algorithm.We construct both one informativeness-oriented and three interestingness-oriented evaluation sets over five topics.We demonstrate that it is very effective to generate both informative and interesting timelines. In addition, our idea naturally leads to a novel presentation of timelines, i.e., phase based timelines, which can potentially improve user experience.

A FUZZY LOGIC-BASED TEXT CLASSIFICATION METHOD FOR SOCIAL MEDIA

The fast information sharing on social network services generates more than thousands of topics every day. It is extremely important for business organizations and administrative decision makers to learn the popularity of these topics as quickly as possible. In this paper, we propose a prediction mode based on SVM with features of three subsets: quantity specific features, quality and user specific features which supplement each other. Furthermore, we divide topic data into time slices which is used as a unit of feature construction. Our findings suggest that the capability of our prediction model outperforms previous methods and also reveals that subsets of features play different role in the prediction of trending topics.

In this paper, we define and study a novel text mining problem, which we refer to as Comparative Text Mining (CTM). Given a set of comparable text collections, the task of comparative text mining is to discover any latent common themes across all collections as well as summarize the similarity and differences of these collections along each common theme. This general problem subsumes many interesting applications, including business intelligence and opinion summarization. We propose a generative probabilistic mixture model for comparative text mining. The model simultaneously performs cross-collection clustering and within-collection clustering, and can be applied to an arbitrary set of comparable text collections. The model can be estimated efficiently using the Expectation-Maximization (EM) algorithm. We evaluate the model on two different text data sets (i.e., a news article data set and a laptop review data set), and compare it with a baseline clustering method also based on a mixture model. Experiment results show that the model is quite effective in discovering the latent common themes across collections and performs significantly better than our baseline mixture model.

Today, a huge amount of text is being generated for social purposes on social networking services on the Web. Unlike traditional documents, such text is usually extremely short and tends to be informal. Analysis of such text benefit many applications such as advertising, search, and content filtering. In this work, we study one traditional text mining task on such new form of text, that is extraction of meaningful keywords. We propose several intuitive yet useful features and experiment with various classification models. Evaluation is conducted on Facebook data. Performances of various features and models are reported and compared.


Question: A dual-layer message grouping technique for identifying topics in Twitter.

This paper explores the realm of probabilistic topic models, presenting an array of algorithms designed to tackle the challenge of organizing extensive document collections. Unlike conventional methods that may struggle with the sheer volume and complexity of textual data, these topic models employ probabilistic techniques to uncover hidden thematic structures within large archives. By analyzing word distributions and document co-occurrences, these algorithms can effectively categorize and summarize information, making it easier to navigate through vast corpora. This approach is particularly relevant in the digital age, where the exponential growth of online content demands sophisticated tools for efficient data management. Through a comprehensive review of various probabilistic models, this paper aims to provide insights into their application, highlighting their capabilities in revealing the latent topics that define the essence of document sets. This not only facilitates better organization but also enhances the retrieval and analysis of information, proving invaluable in fields such as digital libraries, online publishing, and social media analysis.

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.

This thesis addresses the problem of predicting message-level sentiments of English micro-blog messages from Twitter. Convolutional neural networks (CNN) have shown great promise in the task of sentiment classification. Here we expand the CNN proposed by [31, 32] and perform an in-depth analysis to deepen the understanding of these systems. In a first step we compare the performance of different architectures with focus on deeper architectures. We show how different choices of hyper-parameters impact the results and present the benefits and challenges of using deeper architectures. Second, we introduce the procedure for achieving a high quality initialization of the CNN and show the massive impact it has on its predictive capabilities. This procedure consists of creating custom word embeddings followed by pre-training the network using 90M tweets where the polarity of a tweet is inferred by noisy labels. Third, we present an in-depth analysis of the supervised phase. We show the impact of the pre-training on the supervised phase. We show how the choice of the optimization algorithm and the step size impact the variance of the score during the supervised phase. The analysis is concluded by an inspection of the errors made by the system. This gives us insights into the inner workings of the CNN and hints as to what is needed for further improvement. The CNN presented in this thesis is part of an ensemble which ranked 1st in the SemEval-2016 competition, Task 4a [22]. The task consists of classifying a given tweet as either positive, negative or neutral. This further demonstrates the potential these systems have in the field of sentiment analysis.

Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we make a conclusion of the survey and list future research directions.

Multinomial distributions over words are frequently used to model topics in text collections. A common, major challenge in applying all such topic models to any text mining problem is to label a multinomial topic model accurately so that a user can interpret the discovered topic. So far, such labels have been generated manually in a subjective way. In this paper, we propose probabilistic approaches to automatically labeling multinomial topic models in an objective way. We cast this labeling problem as an optimization problem involving minimizing Kullback-Leibler divergence between word distributions and maximizing mutual information between a label and a topic model. Experiments with user study have been done on two text data sets with different genres.The results show that the proposed labeling methods are quite effective to generate labels that are meaningful and useful for interpreting the discovered topic models. Our methods are general and can be applied to labeling topics learned through all kinds of topic models such as PLSA, LDA, and their variations.

The worldwide adoption of Twitter turned it into one of the most popular platforms for content analysis as it serves as a gauge of the public’s feeling and opinion on a variety of topics. This is particularly true of political discussions and lawmakers’ actions and initiatives. Yet, one common but unrealistic assumption is that the data of interest for analysis is readily available in a comprehensive and accurate form. Data need to be retrieved, but due to the brevity and noisy nature of Twitter content, it is difficult to formulate user queries that match relevant posts that use different terminology without introducing a considerable volume of unwanted content. This problem is aggravated when the analysis must contemplate multiple and related topics of interest, for which comments are being concurrently posted. This article presents Active Tweet Retrieval Visualization (ATR-Vis), a user-driven visual approach for the retrieval of Twitter content applicable to this scenario. The method proposes a set of active retrieval strategies to involve an analyst in such a way that a major improvement in retrieval coverage and precision is attained with minimal user effort. ATR-Vis enables non-technical users to benefit from the aforementioned active learning strategies by providing visual aids to facilitate the requested supervision. This supports the exploration of the space of potentially relevant tweets, and affords a better understanding of the retrieval results. We evaluate our approach in scenarios in which the task is to retrieve tweets related to multiple parliamentary debates within a specific time span. We collected two Twitter datasets, one associated with debates in the Canadian House of Commons during a particular week in May 2014, and another associated with debates in the Brazilian Federal Senate during a selected week in May 2015. The two use cases illustrate the effectiveness of ATR-Vis for the retrieval of relevant tweets, while quantitative results show that our approach achieves high retrieval quality with a modest amount of supervision. Finally, we evaluated our tool with three external users who perform searching in social media as part of their professional work.

Sentiment analysis of microblog texts has drawn lots of attention in both the academic and industrial fields. However, most of the current work only focuses on polarity classification. In this paper, we present an opinion mining system for Chinese microblogs called CMiner. Instead of polarity classification, CMiner focuses on more complicated opinion mining tasks - opinion target extraction and opinion summarization. Novel algorithms are developed for the two tasks and integrated into the end-to-end system. CMiner can help to effectively understand the users' opinion towards different opinion targets in a microblog topic. Specially, we develop an unsupervised label propagation algorithm for opinion target extraction. The opinion targets of all messages in a topic are collectively extracted based on the assumption that similar messages may focus on similar opinion targets. In addition, we build an aspect-based opinion summarization framework for microblog topics. After getting the opinion targets of all the microblog messages in a topic, we cluster the opinion targets into several groups and extract representative targets and summaries for each group. A co-ranking algorithm is proposed to rank both the opinion targets and microblog sentences simultaneously. Experimental results on a benchmark dataset show the effectiveness of our system and the algorithms.

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

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.

In this article, we first present the benefit of combing text analysis (topic models in particular) with interactive visualization. We then highlight examples from prior work on topic and time-oriented visual text analysis. Lastly, we present four challenges that warrant additional future research in the field of visual text analysis.

Due to the explosion of social networking and the information sharing among their users, the interest in analyzing social networks has increased over the recent years. Two general interests in this kind of studies are community detection and visualization. In the first case, most of the classic algorithms for community detection use only the structural information to identify groups, that is, how clusters are formed according to the topology of the relationships. However, these methods do not take into account any semantic information which could guide the clustering process, and which may add elements to conduct further analyses. In the second case most of the layout algorithms for clustered graphs have been designed to differentiate the groups within the graph, but they are not designed to analyze the interactions between such groups. Identifying these interactions gives an insight into the way different communities exchange messages or information, and allows the social network researcher to identify key actors, roles, and paths from one community to another.
 This article presents a novel model to use, in a conjoint way, the semantic information from the social network and its structural information to, first, find structurally and semantically related groups of nodes, and second, a layout algorithm for clustered graphs which divides the nodes into two types, one for nodes with edges connecting other communities and another with nodes connecting nodes only within their own community. With this division the visualization tool focuses on the connections between groups facilitating deep studies of augmented social networks.


Question: A two-tiered message aggregation technique for identifying topics on Twitter.

The SNOW 2014 Data Challenge: A Critical Review of News Topic Detection Techniques in Social Media Streams

The SNOW 2014 Data Challenge was designed to establish a public benchmark and evaluation framework for the detection of topics within social media content streams. Specifically, the challenge focused on identifying the most pertinent news topics from a collection of tweets within designated short time intervals. This paper delves into the challenge's formulation, the methodology for data aggregation and assessment, as well as the outcomes garnered by the eleven participating teams. Additionally, it offers a reflective analysis of the key findings and emergent issues from the event. The challenge's objective was to assess and compare the efficacy of various topic detection methods in the context of social media, particularly Twitter. By providing a standardized dataset and evaluation criteria, the SNOW 2014 Data Challenge facilitated a comprehensive analysis of the strengths and limitations of current approaches to topic detection in social media. The insights derived from this challenge are instrumental in guiding future research and development in this domain.

This paper proposes an unsupervised framework for tracking real world events from their traces on Twitter and Instagram. Empirical data suggests that event detection from Instagram streams errs on the false-negative side due to the relative sparsity of Instagram data (compared to Twitter data), whereas event detection from Twitter can suffer from false-positives, at least if not paired with careful analysis of tweet content. To tackle both problems simultaneously, we design a unified unsupervised algorithm that fuses events detected originally on Instagram (called I-events) and events detected originally on Twitter (called T-events), that occur in adjacent periods, in an attempt to combine the benefits of both sources while eliminating their individual disadvantages. We evaluate the proposed framework with real data crawled from Twitter and Instagram. The results indicate that our algorithm significantly improves tracking accuracy compared to baselines.

The rapid growth of online social media in the form of collaborativelycreated content presents new opportunities and challenges to both producers and consumers of information. With the large amount of data produced by various social media services, text analytics provides an effective way to meet usres’ diverse information needs. In this chapter, we first introduce the background of traditional text analytics and the distinct aspects of textual data in social media. We next discuss the research progress of applying text analytics in social media from different perspectives, and show how to improve existing approaches to text representation in social media, using real-world examples.

Social networks produce an enormous quantity of data. Facebook consists of over 400 million active users sharing over 5 billion pieces of information each month. Analyzing this vast quantity of unstructured data presents challenges for software and hardware. We present GraphCT, a Graph Characterization Toolkit for massive graphs representing social network data. On a 128-processor Cray XMT, GraphCT estimates the betweenness centrality of an artificially generated (R-MAT) 537 million vertex, 8.6 billion edge graph in 55 minutes and a real-world graph (Kwak, et al.) with 61.6 million vertices and 1.47 billion edges in 105 minutes. We use GraphCT to analyze public data from Twitter, a microblogging network. Twitter's message connections appear primarily tree-structured as a news dissemination system. Within the public data, however, are clusters of conversations. Using GraphCT, we can rank actors within these conversations and help analysts focus attention on a much smaller data subset.

Nowadays there is an important need by journalists and media monitoring companies to cluster news in large amounts of web articles, in order to ensure fast access to their topics or events of interest. Our aim in this work is to identify groups of news articles that share a common topic or event, without a priori knowledge of the number of clusters. The estimation of the correct number of topics is a challenging issue, due to the existence of “noise”, i.e. news articles which are irrelevant to all other topics. In this context, we introduce a novel density-based news clustering framework, in which the assignment of news articles to topics is done by the well-established Latent Dirichlet Allocation, but the estimation of the number of clusters is performed by our novel method, called “DBSCAN-Martingale”, which allows for extracting noise from the dataset and progressively extracts clusters from an OPTICS reachability plot. We evaluate our framework and the DBSCAN-Martingale on the 20newsgroups-mini dataset and on 220 web news articles, which are references to specific Wikipedia pages. Among twenty methods for news clustering, without knowing the number of clusters k, the framework of DBSCAN-Martingale provides the correct number of clusters and the highest Normalized Mutual Information.

Since the mid-2000s, there has been exponential growth of asynchronous online conversations, thanks to the rise of social media. Analyzing and gaining insights from such conversations can be quite challenging for a user, especially when the discussion becomes very long. A promising solution to this problem is topic modeling, since it may help the user to understand quickly what was discussed in a long conversation and to explore the comments of interest. However, the results of topic modeling can be noisy, and they may not match the user’s current information needs. To address this problem, we propose a novel topic modeling system for asynchronous conversations that revises the model on the fly on the basis of users’ feedback. We then integrate this system with interactive visualization techniques to support the user in exploring long conversations, as well as in revising the topic model when the current results are not adequate to fulfill the user’s information needs. Finally, we report on an evaluation with real users that compared the resulting system with both a traditional interface and an interactive visual interface that does not support human-in-the-loop topic modeling. Both the quantitative results and the subjective feedback from the participants illustrate the potential benefits of our interactive topic modeling approach for exploring conversations, relative to its counterparts.

Television broadcasters are beginning to combine social micro-blogging systems such as Twitter with television to create social video experiences around events. We looked at one such event, the first U.S. presidential debate in 2008, in conjunction with aggregated ratings of message sentiment from Twitter. We begin to develop an analytical methodology and visual representations that could help a journalist or public affairs person better understand the temporal dynamics of sentiment in reaction to the debate video. We demonstrate visuals and metrics that can be used to detect sentiment pulse, anomalies in that pulse, and indications of controversial topics that can be used to inform the design of visual analytic systems for social media events.

This paper covers the two approaches for sentiment analysis: i) lexicon based method; ii) machine learning method. We describe several techniques to implement these approaches and discuss how they can be adopted for sentiment classification of Twitter messages. We present a comparative study of different lexicon combinations and show that enhancing sentiment lexicons with emoticons, abbreviations and social-media slang expressions increases the accuracy of lexicon-based classification for Twitter. We discuss the importance of feature generation and feature selection processes for machine learning sentiment classification. To quantify the performance of the main sentiment analysis methods over Twitter we run these algorithms on a benchmark Twitter dataset from the SemEval-2013 competition, task 2-B. The results show that machine learning method based on SVM and Naive Bayes classifiers outperforms the lexicon method. We present a new ensemble method that uses a lexicon based sentiment score as input feature for the machine learning approach. The combined method proved to produce more precise classifications. We also show that employing a cost-sensitive classifier for highly unbalanced datasets yields an improvement of sentiment classification performance up to 7%.

OBJECTIVES
The current study analyzes a large set of Twitter data from 1,384 US counties to determine whether excessive alcohol consumption rates can be predicted by the words being posted from each county.

METHODS
Data from over 138 million county-level tweets were analyzed using predictive modeling, differential language analysis, and mediating language analysis.

RESULTS
Twitter language data captures cross-sectional patterns of excessive alcohol consumption beyond that of sociodemographic factors (e.g. age, gender, race, income, education), and can be used to accurately predict rates of excessive alcohol consumption. Additionally, mediation analysis found that Twitter topics (e.g. 'ready gettin leave') can explain much of the variance associated between socioeconomics and excessive alcohol consumption.

CONCLUSIONS
Twitter data can be used to predict public health concerns such as excessive drinking. Using mediation analysis in conjunction with predictive modeling allows for a high portion of the variance associated with socioeconomic status to be explained.

In the recent years, microblogging services, as Twitter, have become a popular tool for expressing feelings, opinions, broadcasting news, and communicating with friends. Twitter users produced more than 340 million tweets per day which may be consider a rich source of user information. We take a supervised approach to the problem, but leverage existing hashtags in Twitter for building our training data. Finally, we tested the Spanish emotional corpus applying two different machine learning algorithms for emotion identification reaching about 65% accuracy.

This paper describes the shared task for the English Twitter geolocation prediction associated with WNUT 2016. We discuss details of the task settings, data preparation and participant systems. The derived dataset and performance figures from each system provide baselines for future research in this realm.


Question: Two-tiered message grouping for topic identification in Twitter.

This paper explores the intricacies of detecting trending topics within the vast landscape of Twitter data. The exponential growth of online social and news media has led to an overwhelming influx of information about diverse real-world events, necessitating sophisticated information filtering techniques to pinpoint relevant topics and events. Trending topic detection emerges as a critical component for effectively monitoring and summarizing the data emanating from social sources. With a plethora of methods and variables at play, the quality of the results can vary significantly. We evaluate six different topic detection methods on three Twitter datasets, each associated with significant events that vary in their temporal scale and topic churn rate. Our analysis reveals that the nature of the event, the fluctuation in activity over time, the sampling procedure, and the data preprocessing steps all have a profound impact on the quality of the detected topics, which in turn is contingent on the specific detection method employed. We observe that conventional natural language processing techniques are well-suited for social streams with narrowly focused topics, but innovative approaches that leverage the temporal distribution of concepts are required for more complex streams that encompass multiple evolving stories. Among the methods we examine, one innovative topic detection technique, which utilizes n-gram co-occurrence and topic ranking, consistently outperforms other state-of-the-art methods across various conditions, thereby offering greater reliability and accuracy.

Geographically-grounded situational awareness (SA) is critical to crisis management and is essential in many other decision making domains that range from infectious disease monitoring, through regional planning, to political campaigning. Social media are becoming an important information input to support situational assessment (to produce awareness) in all domains. Here, we present a geovisual analytics approach to supporting SA for crisis events using one source of social media, Twitter. Specifically, we focus on leveraging explicit and implicit geographic information for tweets, on developing place-time-theme indexing schemes that support overview+detail methods and that scale analytical capabilities to relatively large tweet volumes, and on providing visual interface methods to enable understanding of place, time, and theme components of evolving situations. Our approach is user-centered, using scenario-based design methods that include formal scenarios to guide design and validate implementation as well as a systematic claims analysis to justify design choices and provide a framework for future testing. The work is informed by a structured survey of practitioners and the end product of Phase-I development is demonstrated / validated through implementation in SensePlace2, a map-based, web application initially focused on tweets but extensible to other media.

When multiple conversations occur simultaneously, a listener must decide which conversation each utterance is part of in order to interpret and respond to it appropriately. We refer to this task as disentanglement. We present a corpus of Internet Relay Chat (IRC) dialogue in which the various conversations have been manually disentangled, and evaluate annotator reliability. This is, to our knowledge, the first such corpus for internet chat. We propose a graph-theoretic model for disentanglement, using discourse-based features which have not been previously applied to this task. The model’s predicted disentanglements are highly correlated with manual annotations.

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.

0957-4174/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.eswa.2012.12.015 ⇑ Corresponding author. E-mail addresses: juaner@lsi.uned.es (J. MartinezAraujo). Twitter spam detection is a recent area of research in which most previous works had focused on the identification of malicious user accounts and honeypot-based approaches. However, in this paper we present a methodology based on two new aspects: the detection of spam tweets in isolation and without previous information of the user; and the application of a statistical analysis of language to detect spam in trending topics. Trending topics capture the emerging Internet trends and topics of discussion that are in everybody’s lips. This growing microblogging phenomenon therefore allows spammers to disseminate malicious tweets quickly and massively. In this paper we present the first work that tries to detect spam tweets in real time using language as the primary tool. We first collected and labeled a large dataset with 34 K trending topics and 20 million tweets. Then, we have proposed a reduced set of features hardly manipulated by spammers. In addition, we have developed a machine learning system with some orthogonal features that can be combined with other sets of features with the aim of analyzing emergent characteristics of spam in social networks. We have also conducted an extensive evaluation process that has allowed us to show how our system is able to obtain an F-measure at the same level as the best state-ofthe-art systems based on the detection of spam accounts. Thus, our system can be applied to Twitter spam detection in trending topics in real time due mainly to the analysis of tweets instead of user accounts. 2012 Elsevier Ltd. All rights reserved.

The rapid development of online social media sites is accompanied by the generation of tremendous web contents. Web users are shifting from data consumers to data producers. As a result, topic detection and tracking without taking users' interests into account is not enough. This paper presents a statistical model that can detect interpretable trends and topics from document streams, where each trend (short for trending story) corresponds to a series of continuing events or a storyline. A topic is represented by a cluster of words frequently co-occurred. A trend can contain multiple topics and a topic can be shared by different trends. In addition, by leveraging a Recurrent Chinese Restaurant Process (RCRP), the number of trends in our model can be determined automatically without human intervention, so that our model can better generalize to unseen data. Furthermore, our proposed model incorporates user interest to fully simulate the generation process of web contents, which offers the opportunity for personalized recommendation in online social media. Experiments on three different datasets indicated that our proposed model can capture meaningful topics and trends, monitor rise and fall of detected trends, outperform baseline approach in terms of perplexity on held-out dataset, and improve the result of user participation prediction by leveraging users' interests to different trends.

Real-time interaction, which enables live discussions, has become a key feature of most Web applications. In such an environment, the ability to automatically analyze user opinions and sentiments as discussions develop is a powerful resource known as real time sentiment analysis. However, this task comes with several challenges, including the need to deal with highly dynamic textual content that is characterized by changes in vocabulary and its subjective meaning and the lack of labeled data needed to support supervised classifiers. In this paper, we propose a transfer learning strategy to perform real time sentiment analysis. We identify a task - opinion holder bias prediction - which is strongly related to the sentiment analysis task; however, in constrast to sentiment analysis, it builds accurate models since the underlying relational data follows a stationary distribution.
 Instead of learning textual models to predict content polarity (i.e., the traditional sentiment analysis approach), we first measure the bias of social media users toward a topic, by solving a relational learning task over a network of users connected by endorsements (e.g., retweets in Twitter). We then analyze sentiments by transferring user biases to textual features. This approach works because while new terms may arise and old terms may change their meaning, user bias tends to be more consistent over time as a basic property of human behavior. Thus, we adopted user bias as the basis for building accurate classification models. We applied our model to posts collected from Twitter on two topics: the 2010 Brazilian Presidential Elections and the 2010 season of Brazilian Soccer League. Our results show that knowing the bias of only 10% of users generates an F1 accuracy level ranging from 80% to 90% in predicting user sentiment in tweets.

Twitter has evolved into a powerful communication and information sharing tool used by millions of people around the world to post what is happening now. A hashtag, a keyword prefixed with a hash symbol (#), is a feature in Twitter to organize tweets and facilitate effective search among a massive volume of data. In this paper, we propose an automatic hashtag recommendation system that helps users find new hashtags related to their interests.
 We propose the Hashtag Frequency-Inverse Hashtag Ubiquity (HF-IHU) ranking scheme, which is a variation of the well-known TF-IDF, that considers hashtag relevancy, as well as data sparseness. Experiments on a large Twitter data set demonstrate that our method successfully yields relevant hashtags for user's interest and that recommendations more stable and reliable than ranking tags based on tweet content similarity. Our results show that HF-IHU can achieve over 30% hashtag recall when asked to identify the top 10 relevant hashtags for a particular tweet.

Twitter shares a free 1% sample of its tweets through the "Streaming API". Recently, research has pointed to evidence of bias in this source. The methodologies proposed in previous work rely on the restrictive and expensive Firehose to find the bias in the Streaming API data. We tackle the problem of finding sample bias without costly and restrictive Firehose data. We propose a solution that focuses on using an open data source to find bias in the Streaming API.

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.

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.