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

Revisit Behavior in Social Media: The Phoenix-R Model and Discoveries

How many listens will an artist receive on a online radio? How about plays on a YouTube video? How many of these visits are new or returning users? Modeling and mining popularity dynamics of social activity has important implications for researchers, content creators and providers. We here investigate the effect of revisits (successive visits from a single user) on content popularity. Using four datasets of social activity, with up to tens of millions media objects (e.g., YouTube videos, Twitter hashtags or LastFM artists), we show the effect of revisits in the popularity evolution of such objects. Secondly, we propose the Phoenix-R model which captures the popularity dynamics of individual objects. Phoenix-R has the desired properties of being: (1) parsimonious, being based on the minimum description length principle, and achieving lower root mean squared error than state-of-the-art baselines; (2) applicable, the model is effective for predicting future popularity values of objects.Comment: To appear on European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 201