A rumor is commonly defined as a statement whose true value is unverifiable. Rumors may spread misinformation (false information) or disinformation (deliberately false information) on a network of people. Identifying rumors is crucial in online social media where large amounts of information are easily spread across a large network by sources with unverified authority. In this paper, we address the problem of rumor detection in microblogs and explore the effectiveness of 3 categories of features: content-based, network-based, and microblog-specific memes for correctly identifying rumors. Moreover, we show how these features are also effective in identifying disinformers, users who endorse a rumor and further help it to spread. We perform our experiments on more than 10,000 manually annotated tweets collected from Twitter and show how our retrieval model achieves more than 0.95 in Mean Average Precision (MAP). Finally, we believe that our dataset is the first large-scale dataset on rumor detection. It can open new dimensions in analyzing online misinformation and other aspects of microblog conversations.

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Rumor has it: Identifying Misinformation in Microblogs

The microblogging site Twitter generates a constant stream of communication, some of which concerns events of general interest. An analysis of Twitter may, therefore, give insights into why particular events resonate with the population. This article reports a study of a month of English Twitter posts, assessing whether popular events are typically associated with increases in sentiment strength, as seems intuitively likely. Using the top 30 events, determined by a measure of relative increase in (general) term usage, the results give strong evidence that popular events are normally associated with increases in negative sentiment strength and some evidence that peaks of interest in events have stronger positive sentiment than the time before the peak. It seems that many positive events, such as the Oscars, are capable of generating increased negative sentiment in reaction to them. Nevertheless, the surprisingly small average change in sentiment associated with popular events (typically 1% and only 6% for Tiger Woods' confessions) is consistent with events affording posters opportunities to satisfy pre-existing personal goals more often than eliciting instinctive reactions. © 2011 Wiley Periodicals, Inc.

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Sentiment in Twitter events

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

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Detecting and Tracking Political Abuse in Social Media

Blogs and social networks have recently become a valuable resource for mining sentiments in fields as diverse as customer relationship management, public opinion tracking and text filtering. In fact knowledge obtained from social networks such as Twitter and Facebook has been shown to be extremely valuable to marketing research companies, public opinion organizations and other text mining entities. However, Web texts have been classified as noisy as they represent considerable problems both at the lexical and the syntactic levels. In this research we used a random sample of 3516 tweets to evaluate consumers' sentiment towards well-known brands such as Nokia, T-Mobile, IBM, KLM and DHL. We used an expert-predefined lexicon including around 6800 seed adjectives with known orientation to conduct the analysis. Our results indicate a generally positive consumer sentiment towards several famous brands. By using both a qualitative and quantitative methodology to analyze brands' tweets, this study adds breadth and depth to the debate over attitudes towards cosmopolitan brands.

More than words: Social networks' text mining for consumer brand sentiments

Online social media are complementing and in some cases replacing person-to-person social interaction and redefining the diffusion of information. In particular, microblogs have become crucial grounds on which public relations, marketing, and political battles are fought. We demonstrate a web service that tracks political memes in Twitter and helps detect astroturfing, smear campaigns, and other misinformation in the context of U.S. political elections. We also present some cases of abusive behaviors uncovered by our service. Our web service is based on an extensible framework that will enable the real-time analysis of meme diffusion in social media by mining, visualizing, mapping, classifying, and modeling massive streams of public microblogging events.

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Truthy: mapping the spread of astroturf in microblog streams

Users on Twitter, a microblogging service, started the phenomenon of adding tags to their messages sometime around February 2008. These tags are distinct from those in other Web 2.0 systems because users are less likely to index messages for later retrieval. We compare tagging patterns in Twitter with those in Delicious to show that tagging behavior in Twitter is different because of its conversational, rather than organizational nature. We use a mixed method of statistical analysis and an interpretive approach to study the phenomenon. We find that tagging in Twitter is more about filtering and directing content so that it appears in certain streams. The most illustrative example of how tagging in Twitter differs is the phenomenon of the Twitter micro-meme: emergent topics for which a tag is created, used widely for a few days, then disappears. We describe the micro-meme phenomenon and discuss the importance of this new tagging practice for the larger real-time search context.

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Conversational tagging in twitter

Genre is an important feature for organizing and accessing video games. However, current descriptors of video game genres are unstandardized, undefined, and embedded with multiple information dimensions. This paper describes the development of a more complex and sophisticated scheme consisting of 12 facets and 358 foci for describing and representing video game genre information. Using facet analysis, the authors analyzed existing genre labels from scholarly, commercial, and popular sources, and then synthesized them into discrete categories of indexing terms. This new, more robust scheme provides a framework for improved intellectual access to video games along multiple dimensions.

https://www.ideals.illinois.edu/bitstream/handle/2142/47323/057_ready.pdf?sequence=2

Facet Analysis of Video Game Genres

Category systems have traditionally been created by small committees of people who had authority over the system they were designing. With the rise of large-scale social media systems, category schemes are being created by groups with differing perspectives, values, and expectations for how categories will be used. Prior studies of social tagging and folksonomy focused on the application and evolution of the collective category scheme, but struggled to uncover some of the collective rationale undergirding the decision-making processes in those schemes. In this paper, we qualitatively analyze the early discussions among editors of Wikipedia about the design and creation of its category system. We highlight three themes that dominated the discussion: hierarchy, scope and navigation, and relate these themes to their more formal roots in the information science literature. We distill out four styles of collaboration with regard to category systems that apply broadly to social tagging and other folksonomies. We conclude the paper with implications for collaborative tools and category systems as applied to large-scale collaborative systems.

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Tagging Wikipedia: collaboratively creating a category system

In this paper we discuss the temporal aspects of indexing and classification in information systems Basing this discussion off of the three sources of research of scheme change: of indexing: (1) analytical research on the types of scheme change and (2) empirical data on scheme change in systems and (3) evidence of cataloguer decision-making in the context of scheme change From this general discussion we propose two constructs along which we might craft metrics to measure scheme change: collocative integrity and semantic gravity The paper closes with a discussion of these constructs

Katherine Thornton

Papers

Conversational tagging in twitter

Facet Analysis of Video Game Genres

Tagging Wikipedia: collaboratively creating a category system

Some temporal aspects of indexing and classification: toward a metrics for measuring scheme change

Anatomy and Eugenics: Case Study of Change and Stasis in Classificatory Structures