Julio Cesar dos Reis

TL;DR: A new set of features is presented and the prediction performance of current approaches and features for automatic detection of fake news are measured, revealing interesting findings on the usefulness and importance of features for detecting false news.

TL;DR: Evaluating existing efforts proposed to do language specific sentiment analysis for English 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.

TL;DR: A highly exploratory investigation that produced hundreds of thousands of models from a large and diverse set of features found a strong link between features and model predictions, showing that some features are clearly tailored for detecting certain types of fake news, thus evidencing that different combinations of features cover a specific region of the fake news space.

TL;DR: In this article, the authors characterize Gab, aiming at understanding who are the users who joined it and what kind of content they share in this system, and they 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.

TL;DR: Analysis of messages shared on a number of political-oriented WhatsApp groups, focusing on textual content, revealed that textual messages with misinformation tend to be concentrated on fewer topics, often carrying words related to the cognitive process of insight, which characterizes chain messages.