This paper proposes an analysis of political homophily among Twitter users during the 2016 American Presidential Election. We collected 4.9 million tweets of 18,450 users and their contact network from August 2016 to November 2016. We defined six user classes regarding their sentiment towards Donald Trump and Hillary Clinton: whatever, Trump supporter, Hillary supporter, positive, neutral, and negative. Next, we analyzed their political homophily in three scenarios. Firstly, we analyzed the Twitter follow, mention and retweet connections either unidirectional and reciprocal. In the second scenario, we analyzed multiplex connections, and in the third one, we analyzed friendships with similar speeches. Our results showed that negative users, users supporting Trump, and users supporting Hillary had homophily in all analyzed scenarios. We also found out that the homophily level increase when there are reciprocal connections, similar speeches, or multiplex connections.

In this article, we quantitatively analyze how the term "fake news" is being shaped in news media in recent years. We study the perception and the conceptualization of this term in the traditional media using eight years of data collected from news outlets based in 20 countries. Our results not only corroborate previous indications of a high increase in the usage of the expression "fake news", but also show contextual changes around this expression after the United States presidential election of 2016. Among other results, we found changes in the related vocabulary, in the mentioned entities, in the surrounding topics and in the contextual polarity around the term "fake news", suggesting that this expression underwent a change in perception and conceptualization after 2016. These outcomes expand the understandings on the usage of the term "fake news", helping to comprehend and more accurately characterize this relevant social phenomenon linked to misinformation and manipulation.

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In the context of COVID-19 pandemic, social networks such as Facebook, Twitter, YouTube and Instagram stand out as important sources of information. Among those, YouTube, as the largest and most engaging online media consumption platform, has a large influence in the spread of information and misinformation, which makes it important to study how the platform deals with the problems that arise from disinformation, as well as how its users interact with different types of content. Considering that United States (USA) and Brazil (BR) are two countries with the highest COVID-19 death tolls, we asked the following question: What are the nuances of vaccination campaigns in the two countries? With that in mind, we engage in a comparative analysis of pro and anti-vaccine movements on YouTube. We also investigate the role of YouTube in countering online vaccine misinformation in USA and BR. For this means, we monitored the removal of vaccine related content on the platform and also applied various techniques to analyze the differences in discourse and engagement in pro and anti-vaccine "comment sections". We found that American anti-vaccine content tend to lead to considerably more toxic and negative discussion than their pro-vaccine counterparts while also leading to 18% higher user-user engagement, while Brazilian anti-vaccine content was significantly less engaging. We also found that pro-vaccine and anti-vaccine discourses are considerably different as the former is associated with conspiracy theories (e.g. ccp), misinformation and alternative medicine (e.g. hydroxychloroquine), while the latter is associated with protective measures. Finally, it was observed that YouTube content removals are still insufficient, with only approximately 16% of the anti-vaccine content being removed by the end of the studied period, with the United States registering the highest percentage of removed anti-vaccine content(34%) and Brazil registering the lowest(9.8%).CCS Concepts: • Human-centered computing → Empirical studies in collaborative and social computing.

Software development depends on cooperation between people, and the way it works can define the future of the software project. Developers emotions affect their productivity and way they work, yet there is little information about how developers can influence the mood of each other. As a first step toward understanding how feedback may affect the developers' sentiment, this paper analyzes the mood variations on more than 78k pull requests and 268k pull comments on GitHub. We found that in 31.16% of the cases the developers presented a significant mood variation within one hour when receiving feedback on their pull requests. The variation reduces to 18.16% when evaluating one day before and after the commentary. In software projects with less than 34k lines of code, the number of developers that never contribute again after receiving a negative comment on the first pull request is 10.97%; this number more than doubles to 24.02% when evaluating projects with more than 197k lines of code.

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