Trends in Social Media: Persistence and Decay

By Sitaram Asur, Bernardo A. Huberman, Gábor Szabó, Chunyan Wang. International Conference on Web and Social Media, 2011

408 citations recorded by the source corpus.

Social media generates a prodigious wealth of real-time content at an incessant rate. From all the content that people create and share, only a few topics manage to attract enough attention to rise to the top and become temporal trends which are displayed to users.The question of what factors cause the formation and persistence of trends is an important one that has not been answered yet. In this paper, we conduct an intensive study of trending topics on Twitter and provide a theoretical basis for the formation, persistence and decay of trends. We find that the resonance of the content with the users of the social network plays a major role in causing trends. Also, we observe that a majority of the content propagated to cause trends arise from traditional media sources with social media acting as a selective amplifier for them.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/a65ab5170464c91051f0d8d2373e003821b96bce

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Statistical and Nonlinear Physics

Citations in the corpus

EvoRiver: Visual Analysis of Topic Coopetition on Social Media (2014, result): “The assumption has been contradicted in previous studies [7, 40, 41], which suggest that memes primarily compete for the attention of social media users [7, 40, 41].”
How Visibility and Divided Attention Constrain Social Contagion (2012, background): “Finally, novelty as a model-specific parameter is an inheren t property of the information represented by the URL [47], while visibility is a property of the user interface.”
Visual Analysis of Topic Competition on Social Media (2013, background): “Several studies have also shown that increasing the number of competing topics results in decreasing popularity of the topics [4, 48].”
Style in the Age of Instagram: Predicting Success within the Fashion Industry using Social Media (2015, positive): “For example, Asur and Huber-man [4, 3] used Twitter data to predict the box-ofﬁce performance of newly-released blockbuster movies, and later Mesty´an et al. [35] improved these results using data from Wikipedia.”
Dynamical classes of collective attention in twitter (2011, background): “but also pose scientiﬁc challenges related to understanding the mechanisms ruling their dynamics [2, 7, 4, 14, 11].”
From chatter to headlines: harnessing the real-time web for personalized news recommendation (2012, background): “[4], the number of tweets for a trending topic grows linearly over time.”