Competition among memes in a world with limited attention

By L. Weng, Alessandro Flammini, Alessandro Vespignani, Filippo Menczer. 2012

831 citations recorded by the source corpus.

The wide adoption of social media has increased the competition among ideas for our finite attention. We employ a parsimonious agent-based model to study whether such a competition may affect the popularity of different memes, the diversity of information we are exposed to and the fading of our collective interests for specific topics. Agents share messages on a social network but can only pay attention to a portion of the information they receive. In the emerging dynamics of information diffusion, a few memes go viral while most do not. The predictions of our model are consistent with empirical data from Twitter, a popular microblogging platform. Surprisingly, we can explain the massive heterogeneity in the popularity and persistence of memes as deriving from a combination of the competition for our limited attention and the structure of the social network, without the need to assume different intrinsic values among ideas.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

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Citations in the corpus

How algorithmic popularity bias hinders or promotes quality (2017, background): “@gmail.com 1 arXiv:1707.00574v2 [cs.CY] 14 Jul 2017 unpredictability. Success of individual actors (e.g., artists) or products (e.g., songs, movies, memes) is in fact hard to predict in these systems [34, 35, 25], mainly due to the presence of strong social reinforcement, information cascades, and t”
How Visibility and Divided Attention Constrain Social Contagion (2012, background): “Computer science researchers have recently recognized the importance of attention in explaining human behavior online [4], [5].”
Limited individual attention and online virality of low-quality information (2017, methodology): “[61], who used an agent-based model to demonstrate that the combination of social network structure and finite attention of social media users are sufficient conditions for the emergence of viral memes.”
EvoRiver: Visual Analysis of Topic Coopetition on Social Media (2014, background): “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].”
Quantifying Information Overload in Social Media and Its Impact on Social Contagions (2014, background): “Last, very recently, there have been attempts to analyze and model information propagation assuming competition and cooperation between contagions (Goyal and Kearns 2012; Myers and Leskovec 2012; Weng et al. 2012).”
Information is not a Virus, and Other Consequences of Human Cognitive Limits (2016, background): “Other works incorporated visibility into models of user behavior that account for user interests [40] and sentiment [41] about topics, their limited attention [42], and the multiple channels for ﬁnding information, such as on Digg’s front page [22].”
Visual Analysis of Topic Competition on Social Media (2013, background): “[45] proposed a parsimonious agent-based model to examine the relationship between competition and the popularity, diversity, and lifetime of a topic.”
Evidence of Online Performance Deterioration in User Sessions on Reddit (2016, methodology): “We measure online peer production performance as the quality of comments produced by Reddit users over the course of a session, deﬁned as a period of activity without a prolonged break.”
The Simple Rules of Social Contagion (2013, neutral): “Among other factors thought to aﬀect social contagion are the novelty [34] or persistence [27] of information, and competition with other information [33].”
The Matthew effect in empirical data (2014, methodology): “These predictions are consistent with empirical data from Twitter, and they explain the massive heterogeneity in the popularity and persistence of memes as deriving from a combination of the competition for our limited attention and the structure of the social network, without the need to assume dif”
On the Origins of Memes by Means of Fringe Web Communities (2018, background): “[81, 82] study the popularity of memes spreading as hashtags on Twitter.”
Dynamics of information diffusion and its applications on complex networks (2016, background): “[166] considered the competition among information in the use’s screen, where each user has a memory list and information screen with limited size, which is also referred to as limited attention.”
Virality Prediction and Community Structure in Social Networks (2013, background): “Existing research has attempted to characterize viral memes in terms of message content [22], temporal variation [16, 24], inﬂuential users [19, 28], ﬁnite user attention [18, 21], and local neighborhood structure [10].”
Hoaxy: A Platform for Tracking Online Misinformation (2016, background): “These fact-checking are consumed and broadcast by social media users like any other type of news content, leading to a complex interplay between ‘memes’ vie for the attention of users [37].”