Competing for attention in social media under information overload conditions
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Date
2015-07-10
Authors
Feng, Ling
Hu, Yanqing
Li, Baowen
Stanley, Harry Eugene
Havlin, Shlomo
Braunstein, Lidia A.
Version
Published version
OA Version
Citation
Ling Feng, Yanqing Hu, Baowen Li, H Eugene Stanley, Shlomo Havlin, Lidia A Braunstein. 2015. "Competing for Attention in Social Media under Information Overload Conditions." PLOS ONE, Volume 10, Issue 7, 13 pp. https://doi.org/10.1371/journal.pone.0126090
Abstract
Modern social media are becoming overloaded with information because of the rapidly-expanding number of information feeds. We analyze the user-generated content in Sina Weibo, and find evidence that the spread of popular messages often follow a mechanism that differs from the spread of disease, in contrast to common belief. In this mechanism, an individual with more friends needs more repeated exposures to spread further the information. Moreover, our data suggest that for certain messages the chance of an individual to share the message is proportional to the fraction of its neighbours who shared it with him/her, which is a result of competition for attention. We model this process using a fractional susceptible infected recovered (FSIR) model, where the infection probability of a node is proportional to its fraction of infected neighbors. Our findings have dramatic implications for information contagion. For example, using the FSIR model we find that real-world social networks have a finite epidemic threshold in contrast to the zero threshold in disease epidemic models. This means that when individuals are overloaded with excess information feeds, the information either reaches out the population if it is above the critical epidemic threshold, or it would never be well received.
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License
Attribution 4.0 International