The growth of social networks has raised concerns about the misuse of these platforms by disinformation campaigns, social bots, and coordinated activities. Among these platforms, WhatsApp has become a focal point for this abuse, particularly in Brazil, one of the countries with the highest use of the platform. Despite acknowledging the presence of coordinated campaigns and implementing restrictions on the number of messages forwarded per user, the platform continues to be abused. Due to its private nature and the difficulty of collecting information, little is known about these campaigns and the messages they disseminate. Given this context, our study investigates the presence of coordinated activities on WhatsApp in Brazil, identifying their content and purpose, especially how these messages relate to recent Brazilian political events. To answer these questions, we analyzed 13 million messages from 1,444 political groups over seven months from July 2022 to January 2023. Using network analysis, our findings suggest a significant prevalence of coordinated activity in the propagation of news messages, 26% of which originate from misinformation sites. Furthermore, we found that images play a key role in coordinated activity, accounting for 15% of messages, which are also used to mislead. Finally, coordinated accounts were used to organize collective actions, including attacks and protests against election results.

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.

Online messaging platforms are key communication tools but are vulnerable to fake news and conspiracy theories. Mainstream platforms such as Facebook are increasing content moderation of harmful and conspiratorial content. In response, users from fringe communities are migrating to alternative platforms like Telegram. These platforms offer more freedom and less intervention. Currently, Telegram is one of the leading messaging platforms hosting fringe communities. Despite the popularity, as a research community, we lack knowledge of how content spreads over this network. Motivated by the importance and impact of messaging platforms on society, we aim to measure the information propagation within fringe communities on the Telegram network, focusing on how public groups and channels exchange messages. We collect and explore about 140 million messages from 9,000 channels and groups on Telegram. We examine message forwarding and the lifetime of the messages from different aspects. Among other things, we find inequality in content creation; 6% of the users are responsible for 90% of forwarded messages. We also discover that while the forwarding feature considerably amplifies the reach of messages, the spread of content within our dataset remains largely localized. Additionally, we find that 5% of the channels are responsible for 40% of the forwarded messages in the entire dataset. Finally, our lifetime analysis shows that messages disseminated in groups with numerous active users exhibit significantly longer lifespans compared to those circulated in channels.

WhatsApp provides a fertile ground for the large-scale dissemination of information, particularly in countries like Brazil and India. Given its increasing popularity and use for political discussions, it is paramount to ensure that WhatsApp groups are adequately protected from attackers who aim to disrupt the activity of WhatsApp groups. Motivated by this, in this work, we characterize two types of attacks that may disrupt WhatsApp groups. We look into the flooding attack, where an attacker shares a large number of usually duplicate messages within a short period, and the hijacking attack, where attackers aim to obtain complete control of the group. We collect a large dataset of 19M messages shared in 1.6K WhatsApp public political groups from Brazil and analyze them to identify and characterize flooding and hijacking attacks. Among other things, we find that approximately 7% of the groups receive flooding attacks, which are usually short-lived (usually less than four minutes), and groups can receive multiple flooding attacks, even within the same day. Also, we find that most flooding attacks are executed using stickers (62% of all flooding attacks) and that, in most cases, attackers use both flooding and hijacking attacks to obtain complete control of the WhatsApp groups. Our work aims to raise user awareness about such attacks on WhatsApp and emphasizes the need to develop effective moderation tools to assist group administrators in preventing or mitigating such attacks.

WhatsApp has evolved into a popular communication tool, facilitating the exchange of billions of multimedia messages globally. With its large public groups and forwarding features, the platform has enabled messages to go viral, rapidly disseminating across the WhatsApp network. This has also brought WhatsApp to a central position in spreading misinformation campaigns, prompting the company to implement measures to counter bulk message dissemination, such as limiting simultaneous forwards and flagging viral content. Despite these measures, there remains a gap in our understanding of how forwarded messages function within this ecosystem and the effectiveness of the restrictions in containing the spread of viral content. In this study, we analyze approximately 10 million messages from 1,101 public WhatsApp groups dedicated to political discussion in Brazil, focusing on forwarded content. We investigate the structure of message forwarding, assess the reach of Forwarded Many Times (FTM) labeling mechanism, and evaluate the platform's ability to detect and flag duplicated media. Our findings reveal that forwarded messages constitute a substantial portion of the content shared in public WhatsApp groups. Moreover, we discover that the measures implemented by WhatsApp to restrict the dissemination of such messages can be easily circumvented, allowing users to intentionally bypass the architecture of the system and share media beyond the imposed limits. Notably, we identify that 59% of duplicated content flagged as FMT by WhatsApp does not receive the corresponding flag and find evidences of misinformation circulating virally in those groups. This research provides valuable insights into the dynamics of forwarded messages on WhatsApp and highlights the need for more effective strategies to combat the spread of viral content within the platform.

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.

WhatsApp has revolutionized the way people communicate and interact. It is not only cheaper than the traditional Short Message Service (SMS) communication but it also brings a new form of mobile communication: the group chats. Such groups are great forums for collective discussions on a variety of topics. In particular, in events of great social mobilization, such as strikes and electoral campaigns, WhatsApp group chats are very attractive as they facilitate information exchange among interested people. Yet, recent events have raised concerns about the spreading of misinformation in WhatsApp. In this work, we analyze information dissemination within WhatsApp, focusing on publicly accessible political-oriented groups, collecting all shared messages during major social events in Brazil: a national truck drivers' strike and the Brazilian presidential campaign. We analyze the types of content shared within such groups as well as the network structures that emerge from user interactions within and cross-groups. We then deepen our analysis by identifying the presence of misinformation among the shared images using labels provided by journalists and by a proposed automatic procedure based on Google searches. We identify the most important sources of the fake images and analyze how they propagate across WhatsApp groups and from/to other Web platforms.

Sentiment analysis has become a very important tool for analysis of social media data. There are several methods developed, covering distinct aspects of the problem and disparate strategies. However, no single technique fits well in all cases or for all data sources. Supervised approaches may be able to adapt to specific situations, but require manually labeled training, which is very cumbersome and expensive to acquire, mainly for a new application. In this context, we propose to combine several popular and effective state‐of‐the‐practice sentiment analysis methods by means of an unsupervised bootstrapped strategy. One of our main goals is to reduce the large variability (low stability) of the unsupervised methods across different domains. The experimental results demonstrate that our combined method (aka, 10SENT) improves the effectiveness of the classification task, considering thirteen different data sets. Also, it tackles the key problem of cross‐domain low stability and produces the best (or close to best) results in almost all considered contexts, without any additional costs (e.g., manual labeling). Finally, we also investigate a transfer learning approach for sentiment analysis to gather additional (unsupervised) information for the proposed approach, and we show the potential of this technique to improve our results.

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.