Characterizing Attention Cascades in WhatsApp Groups

An important political and social phenomena discussed in several countries, like India and Brazil, is the use of WhatsApp to spread false or misleading content. However, little is known about the information dissemination process in WhatsApp groups. Attention affects the dissemination of information in WhatsApp groups, determining what topics or subjects are more attractive to participants of a group. In this paper, we characterize and analyze how attention propagates among the participants of a WhatsApp group. An attention cascade begins when a user asserts a topic in a message to the group, which could include written text, photos, or links to articles online. Others then propagate the information by responding to it. We analyzed attention cascades in more than 1.7 million messages posted in 120 groups over one year. Our analysis focused on the structural and temporal evolution of attention cascades as well as on the behavior of users that participate in them. We found specific characteristics in cascades associated with groups that discuss political subjects and false information. For instance, we observe that cascades with false information tend to be deeper, reach more users, and last longer in political groups than in non-political groups.Comment: Accepted as a full paper at the 11th International ACM Web Science Conference (WebSci 2019). Please cite the WebSci versio

A First Look at CQVID-19 Messages on WhatsApp in Pakistan

The worldwide spread of COVID-19 has prompted extensive online discussions, creating an `infodemic' on social media platforms such as WhatsApp and Twitter. However, the information shared on these platforms is prone to be unreliable and/or misleading. In this paper, we present the first analysis of COVID-19 discourse on public WhatsApp groups from Pakistan. Building on a large scale annotation of thousands of messages containing text and images, we identify the main categories of discussion. We focus on COVID-19 messages and understand the different types of images/text messages being propagated. By exploring user behavior related to COVID messages, we inspect how misinformation is spread. Finally, by quantifying the flow of information across WhatsApp and Twitter, we show how information spreads across platforms and how WhatsApp acts as a source for much of the information shared on Twitter

Share and multiply: modeling communication and generated traffic in private WhatsApp groups

Group-based communication is a highly popular communication paradigm, which is especially prominent in mobile instant messaging (MIM) applications, such as WhatsApp. Chat groups in MIM applications facilitate the sharing of various types of messages (e.g., text, voice, image, video) among a large number of participants. As each message has to be transmitted to every other member of the group, which multiplies the traffic, this has a massive impact on the underlying communication networks. However, most chat groups are private and network operators cannot obtain deep insights into MIM communication via network measurements due to end-to-end encryption. Thus, the generation of traffic is not well understood, given that it depends on sizes of communication groups, speed of communication, and exchanged message types. In this work, we provide a huge data set of 5,956 private WhatsApp chat histories, which contains over 76 million messages from more than 117,000 users. We describe and model the properties of chat groups and users, and the communication within these chat groups, which gives unprecedented insights into private MIM communication. In addition, we conduct exemplary measurements for the most popular message types, which empower the provided models to estimate the traffic over time in a chat group

Are all Chats suitable for learning purposes? A study of the required characteristics

Proceedings of: 5th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion, DSAI 2013. Took place in November 13-15, 2013, in Vigo, Spain. The web site is http://dsai2013.utad.pt/The Chat is being used for more than one decade in learning environments as a useful Computer Supported Collaborative Learning (CSCL)Tool. However, nowadays some students still usually face accessibility barriers when using Chats and, as a result, they cannot learn in the same way as their classmates. Thus, some of the equality principles of education are not accomplished. This paper shows a study of chat's characteristics and analyzes if commercial Chats with general purposes can be used for learning environments in an accessible way. This study has been carried out from the point of view of the Universal Design for Learning (UDL) guidelines 2.0. The study analyzes fifteen commercial chats (desktop, mobile and web chats) and provides some recommendations in order to improve the accessibility of chats in learning environments.This research work has been partially supported by the research project MA2VICMR (S2009/TIC-1542) and by the project MULTIMEDICA (TIN2010-20644-C03-01).Publicad

WhatsApp and audio misinformation during the Covid-19 pandemic

Given user choices and the commercial offerings of internet providers, WhatsApp has increasingly become established as a new standard for communication by audio, image, and text. This paper explores the role of misinformation during the Covid-19 pandemic by using content disseminated through WhatsApp, thereby making three main contributions: a discussion about the potential shift toward nontextual and nonvisual forms of misinformation; the new social role of audio, namely related to the critique of policies and political actors during the early stage of the Covid-19 pandemic; and the questioning of the First Draft News disinformation conceptual model by proposing a complementary approach that focuses only on factuality. Conclusions were drawn after conducting a content analysis of 988 units of Covid-19-related audio files, images, videos, and texts shared via WhatsApp during the early stage of the pandemic. A typology was identified to address distinct claims that focus on five different topics (society, policy and politics, health science, pandemic, and other), as well as audio messaging trending as a novel format for spreading misinformation. The results help us to contextualize and discuss a potential shift toward nontextual and nonvisual forms of misinformation, reflecting the increasing adoption of the audio format among WhatsApp users and making WhatsApp a fertile environment for the circulation and dissemination of misinformation regarding Covid-19-related themes. In a society characterized by the rapid consumption of information, the idea that content must have a degree of falsehood to mislead is an indicator of the distance between theoretical models and social reality. This indicator is important to identify true content as potential misinformation on the basis of its factuality.info:eu-repo/semantics/publishedVersio

WhatsApp and audio misinformation during the Covid-19 pandemic

Given user choices and the commercial offerings of internet providers, WhatsApp has increasingly become established as a new standard for communication by audio, image, and text. This paper explores the role of misinformation during the Covid-19 pandemic by using content disseminated through WhatsApp, thereby making three main contributions: a discussion about the potential shift toward nontextual and nonvisual forms of misinformation; the new social role of audio, namely related to the critique of policies and political actors during the early stage of the Covid-19 pandemic; and the questioning of the First Draft News disinformation conceptual model by proposing a complementary approach that focuses only on factuality. Conclusions were drawn after conducting a content analysis of 988 units of Covid-19-related audio files, images, videos, and texts shared via WhatsApp during the early stage of the pandemic. A typology was identified to address distinct claims that focus on five different topics (society, policy and politics, health science, pandemic, and other), as well as audio messaging trending as a novel format for spreading misinformation. The results help us to contextualize and discuss a potential shift toward nontextual and nonvisual forms of misinformation, reflecting the increasing adoption of the audio format among WhatsApp users and making WhatsApp a fertile environment for the circulation and dissemination of misinformation regarding Covid-19-related themes. In a society characterized by the rapid consumption of information, the idea that content must have a degree of falsehood to mislead is an indicator of the distance between theoretical models and social reality. This indicator is important to identify true content as potential misinformation on the basis of its factuality