Modeling dynamics of attention in social media with user efficiency
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Modeling dynamics of attention in social media with user efficiency Carmen Vaca Ruiz1,3* , Luca Maria Aiello2 and Alejandro Jaimes2 * Correspondence: cvaca@fiec.espol.edu.ec 1 Politecnico di Milano, Piazza Leonardo Da Vinci, 32, Milan, Italy 3 FIEC, Escuela Superior Politecnica del Litoral, Campus Gustavo Galindo, Km 30.5 via Perimetral, Guayaquil, Ecuador Full list of author information is available at the end of the article
Abstract Evolution of online social networks is driven by the need of their members to share and consume content, resulting in a complex interplay between individual activity and attention received from others. In a context of increasing information overload and limited resources, discovering which are the most successful behavioral patterns to attract attention is very important. To shed light on the matter, we look into the patterns of activity and popularity of users in the Yahoo Meme microblogging service. We observe that a combination of different type of social and content-producing activity is necessary to attract attention and the efficiency of users, namely the average attention received per piece of content published, for many users has a defined trend in its temporal footprint. The analysis of the user time series of efficiency shows different classes of users whose different activity patterns give insights on the type of behavior that pays off best in terms of attention gathering. In particular, sharing content with high spreading potential and then supporting the attention raised by it with social activity emerges as a frequent pattern for users gaining efficiency over time. Keywords: online attention; microblogging; social networks; time series
1 Introduction Understanding users’ activities in social media platforms, in terms of the actions they take and how those actions affect the attention they receive (e.g., comments, replies, re-posts of messages they post, etc.), is crucial for understanding the dynamics of social media systems as well as for designing incentives that lead to growth in terms of user activity and number of users. As expected, given the nature of such platforms, users who receive attention from their peers tend to be more engaged with the service and are less likely to churn out []. Insights on the kinds of actions that users take to gain more attention and become “popular” are therefore important because they can help explain how social media platforms evolve. In spite of the importance of analyzing such behavior at a large scale, the dynamics of attention are not well understood. This is largely due to two main reasons: on one hand that there are few datasets that show the evolution of a network from its very beginnings, and on the other hand, because most work has focused on the popularity of content rather than on analyzing the effects of user’s behaviors on how other users react to them. For example, there have been many studies to establish the reasons behind user or item popularity in social networks (e.g., [, ]), but the effects tha
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