Anatomy of Viral Social Media Events

Discussion topics go sometimes viral in social media without a seemingly coherent pattern. Existing literature shows these discussions can reach a very high level, but, notably, they prevail to varying degrees. This paper investigates the anatomy of viral social media events using a dataset of 960 viral social media discussion topics that have been identified by an algorithm from a variety of social media sources over two years’ time. A negative binomial regression shows that the average daily amount and the relative change in the daily amount of social media platforms at which the event has been discussed has a positive effect on the duration of the event. Average or relative amount of posts or authors has no or very little effect on event duration. The results suggest that viral social media events last longer when people using different social media platforms get exposed to them. This finding contributes to the literature on social media events, virality, and information diffusion.Peer reviewe

Predicting Successful Memes using Network and Community Structure

We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.Comment: 10 pages, 6 figures, 2 tables. Proceedings of 8th AAAI Intl. Conf. on Weblogs and social media (ICWSM 2014

Filtration Failure: On Selection for Societal Sanity

This paper focuses on the question of filtration through the perspective of “too much information”. It concerns Western society within the context of new media and digital culture. The main aim of this paper is to apply a philosophical reading on the video game concept of Selection for Societal Sanity within the problematics of cultural filtration, control of behaviors and desire, and a problematization of trans-individuation that the selected narrative conveys. The idea of Selection for Societal Sanity, which derives from the first postmodern video game Metal Gear Solid 2: Sons of Liberty (2001), is applied into a philosophical framework based on select concepts from Bernard Stiegler’s writing and incorporating them with current events such as post-truth or fake news in order to explore the role of techne and filtration within social organizations and individual psyches. Alternate forms of behavior, which contest cultural paradigms, are re-problematized as tension between calculability and incalculability, or market value versus social bonding

Uncovering nodes that spread information between communities in social networks

From many datasets gathered in online social networks, well defined community structures have been observed. A large number of users participate in these networks and the size of the resulting graphs poses computational challenges. There is a particular demand in identifying the nodes responsible for information flow between communities; for example, in temporal Twitter networks edges between communities play a key role in propagating spikes of activity when the connectivity between communities is sparse and few edges exist between different clusters of nodes. The new algorithm proposed here is aimed at revealing these key connections by measuring a node's vicinity to nodes of another community. We look at the nodes which have edges in more than one community and the locality of nodes around them which influence the information received and broadcasted to them. The method relies on independent random walks of a chosen fixed number of steps, originating from nodes with edges in more than one community. For the large networks that we have in mind, existing measures such as betweenness centrality are difficult to compute, even with recent methods that approximate the large number of operations required. We therefore design an algorithm that scales up to the demand of current big data requirements and has the ability to harness parallel processing capabilities. The new algorithm is illustrated on synthetic data, where results can be judged carefully, and also on a real, large scale Twitter activity data, where new insights can be gained

Characterizing interactions in online social networks during exceptional events

Nowadays, millions of people interact on a daily basis on online social media like Facebook and Twitter, where they share and discuss information about a wide variety of topics. In this paper, we focus on a specific online social network, Twitter, and we analyze multiple datasets each one consisting of individuals' online activity before, during and after an exceptional event in terms of volume of the communications registered. We consider important events that occurred in different arenas that range from policy to culture or science. For each dataset, the users' online activities are modeled by a multilayer network in which each layer conveys a different kind of interaction, specifically: retweeting, mentioning and replying. This representation allows us to unveil that these distinct types of interaction produce networks with different statistical properties, in particular concerning the degree distribution and the clustering structure. These results suggests that models of online activity cannot discard the information carried by this multilayer representation of the system, and should account for the different processes generated by the different kinds of interactions. Secondly, our analysis unveils the presence of statistical regularities among the different events, suggesting that the non-trivial topological patterns that we observe may represent universal features of the social dynamics on online social networks during exceptional events

Digital Food Marketing to Children and Adolescents: Problematic Practices and Policy Interventions

Examines trends in digital marketing to youth that uses "immersive" techniques, social media, behavioral profiling, location targeting and mobile marketing, and neuroscience methods. Recommends principles for regulating inappropriate advertising to youth

#MeToo as Catalyst: A Glimpse into 21st Century Activism

The Twitter hashtag #MeToo has provided an accessible medium for users to share their personal experiences and make public the prevalence of sexual harassment, assault, and violence against women. This online phenomenon, which has largely involved posting on Twitter and “retweeting” to share other’s posts has revealed crucial information about the scope and nature of sexual harassment and misconduct. More specifically, social media has served as a central forum for this unprecedented global conversation, where previously silenced voices have been amplified, supporters around the world have been united, and resistance has gained steam. This Essay discusses the #MeToo movement within the broader context of social media activism, explaining how this unique form of collective action is rapidly evolving. We offer empirical insights into the types of conversations taking place under the hashtag and the extent to which the movement is leading to broader social change. While it is unclear which changes are sustainable over time, it is clear that the hashtag #MeToo has converted an online phenomenon into tangible change, sparking legal, political, and social changes in the short run. This Essay provides data to illustrate some of these changes, which demonstrate how posting online can serve as an impetus, momentum, and legitimacy for broader movement activity and changes offline more characteristic of traditional movement strategies