We analyze the information credibility of news propagated through Twitter, a popular microblogging service. Previous research has shown that most of the messages posted on Twitter are truthful, but the service is also used to spread misinformation and false rumors, often unintentionally.On this paper we focus on automatic methods for assessing the credibility of a given set of tweets. Specifically, we analyze microblog postings related to "trending" topics, and classify them as credible or not credible, based on features extracted from them. We use features from users' posting and re-posting ("re-tweeting") behavior, from the text of the posts, and from citations to external sources.We evaluate our methods using a significant number of human assessments about the credibility of items on a recent sample of Twitter postings. Our results shows that there are measurable differences in the way messages propagate, that can be used to classify them automatically as credible or not credible, with precision and recall in the range of 70% to 80%.

/pdf/information-credibility-on-twitter-2d1l7ckae1.pdf

Information credibility on twitter

We propose and evaluate a probabilistic framework for estimating a Twitter user's city-level location based purely on the content of the user's tweets, even in the absence of any other geospatial cues By augmenting the massive human-powered sensing capabilities of Twitter and related microblogging services with content-derived location information, this framework can overcome the sparsity of geo-enabled features in these services and enable new location-based personalized information services, the targeting of regional advertisements, and so on Three of the key features of the proposed approach are: (i) its reliance purely on tweet content, meaning no need for user IP information, private login information, or external knowledge bases; (ii) a classification component for automatically identifying words in tweets with a strong local geo-scope; and (iii) a lattice-based neighborhood smoothing model for refining a user's location estimate The system estimates k possible locations for each user in descending order of confidence On average we find that the location estimates converge quickly (needing just 100s of tweets), placing 51% of Twitter users within 100 miles of their actual location

/pdf/you-are-where-you-tweet-a-content-based-approach-to-geo-54ciekoz9l.pdf

You are where you tweet: a content-based approach to geo-locating twitter users

Twitter is a free social networking and micro-blogging service that enables its millions of users to send and read each other's “tweets,” or short, 140-character messages. The service has more than 190 million registered users and processes about 55 million tweets per day. Useful information about news and geopolitical events lies embedded in the Twitter stream, which embodies, in the aggregate, Twitter users' perspectives and reactions to current events. By virtue of sheer volume, content embedded in the Twitter stream may be useful for tracking or even forecasting behavior if it can be extracted in an efficient manner. In this study, we examine the use of information embedded in the Twitter stream to (1) track rapidly-evolving public sentiment with respect to H1N1 or swine flu, and (2) track and measure actual disease activity. We also show that Twitter can be used as a measure of public interest or concern about health-related events. Our results show that estimates of influenza-like illness derived from Twitter chatter accurately track reported disease levels.

/pdf/the-use-of-twitter-to-track-levels-of-disease-activity-and-4ewdmt1dfu.pdf

The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic

Twitter as a new form of social media can potentially contain much useful information, but content analysis on Twitter has not been well studied. In particular, it is not clear whether as an information source Twitter can be simply regarded as a faster news feed that covers mostly the same information as traditional news media. In This paper we empirically compare the content of Twitter with a traditional news medium, New York Times, using unsupervised topic modeling. We use a Twitter-LDA model to discover topics from a representative sample of the entire Twitter. We then use text mining techniques to compare these Twitter topics with topics from New York Times, taking into consideration topic categories and types. We also study the relation between the proportions of opinionated tweets and retweets and topic categories and types. Our comparisons show interesting and useful findings for downstream IR or DM applications.

/pdf/comparing-twitter-and-traditional-media-using-topic-models-40ux4xfa4w.pdf

Comparing twitter and traditional media using topic models

Metadata and data have become a regular currency for citizens to pay for their communication services and security—a trade-off that has nestled into the comfort zone of most people. This article deconstructs the ideological grounds of datafication. Datafication is rooted in problematic ontological and epistemological claims. As part of a larger social media logic, it shows characteristics of a widespread secular belief. Dataism, as this conviction is called, is so successful because masses of people — naively or unwittingly — trust their personal information to corporate platforms. The notion of trust becomes more problematic because people’s faith is extended to other public institutions (e.g. academic research and law enforcement) that handle their (meta)data. The interlocking of government, business, and academia in the adaptation of this ideology makes us want to look more critically at the entire ecosystem of connective media.

/pdf/datafication-dataism-and-dataveillance-big-data-between-ur4oh7cwx7.pdf

Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology

Numerical study of localization in Anderson model for disordered systems

Energy Band Structure of TlCl

A self-consistent cluster theory of amorphous materials

A relativistic formulation of the Korringa-Kohn-Rostoker method for band calculation is presented for non-muffin-tin potential. The method developed here includes the full relativistic effects together with the effect of the non-muffin-tin character which comes from the overlapping ionic potentials. The method is appropriate for the crystals of heavy elements, especially in a loosely packed structure.

Makoto Okazaki

Papers

Numerical study of localization in Anderson model for disordered systems

Energy Band Structure of TlCl

A self-consistent cluster theory of amorphous materials

Relativistic Korringa-Kohn-Rostoker Method of Band Calculation for Non-Muffin-Tin Potential

Local and Band Structures in the Fundamental Absorption Spectra of One-Dimensional Crystals