In this paper, we propose a context-aware recommender system that models students’ programming skills using embeddings of the source code they submit throughout a course. These embeddings predict students’ skills across multiple programming topics, producing profiles that are matched to the skills required by unseen homework problems. To generate recommendations, we compute the cosine similarity between student profiles and problem skill vectors, ranking exercises according to their alignment with each student’s current abilities. We evaluated our approach using real data from students and exercises in an introductory programming course at our university. First, we assessed the effectiveness of our source code embeddings for predicting skills, comparing them with token-based and graph-based alternatives. Results showed that Jina embeddings outperformed TF-IDF, CodeBERT-cpp, and GraphCodeBERT across most skills. Additionally, we evaluated the system’s ability to recommend exercises aligned with weekly course content by analyzing student submissions collected over seven course offerings. Our approach consistently produced more suitable recommendations than baselines based on correctness or solution time, indicating that predicted programming skills provide a stronger signal for problem recommendation.

Instant messaging platforms have become an important means of communication in our world. According to WhatsApp, more than 100 billion messages are sent daily through the app. Communication on these platforms has allowed individuals to express themselves in other types of media, rather than simple text, including audio, videos, images, and, more recently, stickers. This new multimedia format, in particular, emerged with messaging apps and gained considerable popularity among users, promoting new forms of interactions. Stickers range from static images of memes and emojis to animated images similar to GIFs, often used in humorous contexts. However, in the Brazilian context of WhatsApp, they are transcending their role as a mere form of humor to become an important element in political strategy. In this regard, we investigate how stickers are used, revealing unique characteristics that these media bring to public WhatsApp groups and, more specifically, the political use of this new media format. Furthermore, we found evidence of sticker abuse on WhatsApp, where users attack political opponents and spread hate speech and offensive content in public groups without any moderation. To investigate this phenomenon, we collected a large sample of messages from public political WhatsApp groups in Brazil and analyzed the sticker messages shared in this context. Warning! This paper contains images and terms that may be offensive to some audiences.

A key gear in most misinformation ecosystems is the deployment of fake news web sites that publish news in a similar fashion to how news articles are put out by credible sources. The content offered by these sites is disseminated in a complex process that may involve automation, exploitation of message apps and social network algorithms, political bias, and targeted ads to reach large and niche audiences. Due to this high complexity and the rapidly evolving nature of the problem, we are just beginning to understand patterns in the various misinformation ecosystems on the Web. In this work, we offer a first step towards understanding network properties, including data from DNS records, domain registration, TLS certificates, and hosting infrastructure of Brazilian web sites associated with the dissemination of misinformation content on digital platforms. Our findings, in addition to providing a better understanding of the misinformation ecosystem in Brazil, also reveal a novel set of features useful to distinguish low credibility web sites from others.

Despite the valuable social interactions that online media promote, these systems provide space for speech that would be potentially detrimental to different groups of people. The moderation of content imposed by many social media has motivated the emergence of a new social system for free speech named Gab, which lacks moderation of content. This article characterizes and compares moderated textual data from Twitter with a set of unmoderated data from Gab. In particular, we analyze distinguishing characteristics of moderated and unmoderated content in terms of linguistic features, evaluate hate speech and its different forms in both environments. Our work shows that unmoderated content presents different psycholinguistic features, more negative sentiment and higher toxicity. Our findings support that unmoderated environments may have proportionally more online hate speech. We hope our analysis and findings contribute to the debate about hate speech and benefit systems aiming at deploying hate speech detection approaches.

As Internet users increasingly rely on social media sites to receive news, they are faced with a bewildering number of news media choices. For example, thousands of Facebook pages today are registered and categorized as some form of news media outlets. This situation boosted the so-called independent journalism, also known as alternative news media. Identifying and characterizing all the news pages that play an important role in news dissemination is key for understanding the news ecosystems of a country. In this work, we propose a graph-based semi-supervised method to measure the political bias of pages on most countries and show the political split of the alternative media, mainstream media, and public figures pages. We validate our method using the publicly available U.S. dataset and then apply it to Brazilian pages, where we found a larger number of right-wing pages in general, except for alternative news media.

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.