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.
Sentiment analysis has become a key tool for several social media applications, including analysis of user's opinions about products and services, support to politics during campaigns and even for market trending. There are multiple existing sentiment analysis methods that explore different techniques, usually relying on lexical resources or learning approaches. Despite the large interest on this theme and amount of research efforts in the field, almost all existing methods are designed to work with only English content. Most existing strategies in specific languages consist of adapting existing lexical resources, without presenting proper validations and basic baseline comparisons. In this paper, we take a different step into this field. We focus on evaluating existing efforts proposed to do language specific sentiment analysis. To do it, we evaluated twenty-one methods for sentence-level sentiment analysis proposed for English, comparing them with two language-specific methods. Based on nine language-specific datasets, we provide an extensive quantitative analysis of existing multi-language approaches. Our main result suggests that simply translating the input text on a specific language to English and then using one of the existing English methods can be better than the existing language specific efforts evaluated. We also rank those implementations comparing their prediction performance and identifying the methods that acquired the best results using machine translation across different languages. As a final contribution to the research community, we release our codes and datasets. We hope our effort can help sentiment analysis to become English independent.
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.