The moderation of content in many social media systems, such as Twitter and Facebook, motivated the emergence of a new social network system that promotes free speech, named Gab. Soon after that, Gab has been removed from Google Play Store for violating the company's hate speech policy and it has been rejected by Apple for similar reasons. In this paper we characterize Gab, aiming at understanding who are the users who joined it and what kind of content they share in this system. Our findings show that Gab is a very politically oriented system that hosts banned users from other social networks, some of them due to possible cases of hate speech and association with extremism. We provide the first measurement of news dissemination inside a right-leaning echo chamber, investigating a social media where readers are rarely exposed to content that cuts across ideological lines, but rather are fed with content that reinforces their current political or social views.
The massive popularity of online social media provides a unique opportunity
for researchers to study the linguistic characteristics and patterns of user's
interactions. In this paper, we provide an in-depth characterization of
language usage across demographic groups in Twitter. In particular, we extract
the gender and race of Twitter users located in the U.S. using advanced image
processing algorithms from Face++. Then, we investigate how demographic groups
(i.e. male/female, Asian/Black/White) differ in terms of linguistic styles and
also their interests. We extract linguistic features from 6 categories
(affective attributes, cognitive attributes, lexical density and awareness,
temporal references, social and personal concerns, and interpersonal focus), in
order to identify the similarities and differences in particular writing set of
attributes. In addition, we extract the absolute ranking difference of top
phrases between demographic groups. As a dimension of diversity, we also use
the topics of interest that we retrieve from each user. Our analysis unveils
clear differences in the writing styles (and the topics of interest) of
different demographic groups, with variation seen across both gender and race
lines. We hope our effort can stimulate the development of new studies related
to demographic information in the online space.Comment: Proceedings of the 28th ACM Conference on Hypertext and Social Media
2017 (HT '17
Part 3: Social Media and Mobile Applications of AIInternational audienceThe prediction of social media information propagation is a problem that has attracted a lot of interest over the recent years, especially because of the application of such predictions for effective marketing campaigns. Existing approaches have shown that the information cascades in social media are small and have a large width. We validate these results for Tree-Shaped Tweet Cascades created by the ReTweet action. The main contribution of our work is a methodology for predicting the information diffusion that will occur given a user’s tweet. We base our prediction on the linguistic features of the tweet as well as the user profile that created the initial tweet. Our results show that we can predict the Tweet-Pattern with good accuracy. Moreover, we show that influential networks within the Twitter graph tend to use different Tweet-Patterns
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.