We present an analytic study on the language of news media in the context of political fact-checking and fake news detection. We compare the language of real news with that of satire, hoaxes, and propaganda to find linguistic characteristics of untrustworthy text. To probe the feasibility of automatic political fact-checking, we also present a case study based on PolitiFact.com using their factuality judgments on a 6-point scale. Experiments show that while media fact-checking remains to be an open research question, stylistic cues can help determine the truthfulness of text.

Pew research polls report 62 percent of U.S. adults get news on social media (Gottfried and Shearer, 2016). In a December poll, 64 percent of U.S. adults said that "made-up news" has caused a "great deal of confusion" about the facts of current events (Barthel et al., 2016). Fabricated stories in social media, ranging from deliberate propaganda to hoaxes and satire, contributes to this confusion in addition to having serious effects on global stability.In this work we build predictive models to classify 130 thousand news posts as suspicious or verified, and predict four subtypes of suspicious news -satire, hoaxes, clickbait and propaganda. We show that neural network models trained on tweet content and social network interactions outperform lexical models. Unlike previous work on deception detection, we find that adding syntax and grammar features to our models does not improve performance. Incorporating linguistic features improves classification results, however, social interaction features are most informative for finer-grained separation between four types of suspicious news posts.

The emergence of social media has had a significant impact on how people communicate and socialize. Teens use social media to make and maintain social connections with friends and build their reputation. However, the way of analyzing the characteristics of teens in social media has mostly relied on ethnographic accounts or quantitative analyses with small datasets. This paper shows the possibility of detecting age information in user profiles by using a combination of textual and facial recognition methods and presents a comparative study of 27K teens and adults in Instagram. Our analysis highlights that (1) teens tend to post fewer photos but highly engage in adding more tags to their own photos and receiving more Likes and comments about their photos from others, and (2) to post more selfies and express themselves more than adults, showing a higher sense of self-representation. We demonstrate the application of our novel method that shows clear trends of age differences as well as substantiates previous insights in social media.

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In social media, people often press a "Like" button to indicate their shared interest in a particular content or to acknowledge the user who posted the content. Such activities form relationships and networks among people, raising interesting questions about their unique characteristics and implications. However, little research has investigated such Likes as a main study focus. To address this lack of understanding, based on a theoretical framework, we present an analysis of the structural, influential, and contextual aspects of Like activities from the test datasets of 20 million users and their 2 billion Like activities in Instagram.Our study results first highlight that Like activities and networks increase exponentially, and are formed and developed by one's friends and many random users. Second, we observe that five other essential Instagram elements influence the number of Likes to different extents, but following others will not necessarily increase the number of Likes that one receives. Third, we explore the relationship between LDA-based topics and Likes, characterize two user groups-specialists and generalists-and show that specialists tend to receive more Likes and promote themselves more than generalists. We finally discuss theoretical and practical implications and future research directions.

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