The DOI matches its Crossref record; 59 of the 63 references it cites were found in a registry or an index.
How Visibility and Divided Attention Constrain Social Contagion
Open accessAbstract
How far and how fast does information spread in social media? Researchers have recently examined a number of factors that affect information diffusion in online social networks, including: the novelty of information, users' activity levels, who they pay attention to, and how they respond to friends' recommendations. Using URLs as markers of information, we carry out a detailed study of retweeting, the primary mechanism by which information spreads on the Twitter follower graph. Our empirical study examines how users respond to an incoming stimulus, i.e., a tweet (message) from a friend, and reveals that dynamically decaying visibility, which is the increasing cognitive effort required for discovering and acting upon a tweet, combined with limited attention play dominant roles in retweeting behavior. Specifically, we observe that users retweet information when it is most visible, such as when it near the top of their Twitter feed. Moreover, our measurements quantify how a user's limited attention is divided among incoming tweets, providing novel evidence that highly connected individuals are less likely to propagate an arbitrary tweet. Our study indicates that the finite ability to process incoming information constrains social contagion, and we conclude that rapid decay of visibility is the primary barrier to information propagation online.
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“Given the dynamic nature of social media, its large size, and individuals' finite ability to process incoming information (Oken Hodas and Lerman 2012), we should expect Twitter users to maximize the value of their online experience by choosing to follow political actors who can provide information that can be of higher value to them.”
From Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data (2015) · cites this paper as Oken Hodas and Lerman 2012
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The spread of low-credibility content by social bots
Shao, C., Ciampaglia, G. L., Varol, O., Yang, K.-C., Flammini, A., & Menczer, F. (2018) · Nature Communications · doi:10.1038/s41467-018-06930-7 · Cited by 1,125
Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data
Barberá, P. (2015) · Political Analysis · doi:10.1093/pan/mpu011 · Cited by 982
The spread of low-credibility content by social bots
Shao, C., Ciampaglia, G. L., Varol, O., Yang, K.-C., Flammini, A., & Menczer, F. (2018) · RePEc: Research Papers in Economics · Cited by 574
Measuring Emotional Contagion in Social Media
Ferrara, E., & Yang, Z. (2015) · PLoS ONE · doi:10.1371/journal.pone.0142390 · Cited by 528
Understanding the Political Representativeness of Twitter Users
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Metadata
- Authors
- Hodas, N. O., & Lerman, K.
- Venue
- 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing
- Publisher
- IEEE
- Published
- 2012-09
- Pages
- 249-257
- OpenAlex
- W1965563190
- Cited by
- 136
- References
- 63
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 1 · 2023: 5 · 2022: 2 · 2021: 13 · 2020: 14 · 2019: 9 · 2018: 13 · 2017: 12 · 2016: 14 · 2015: 17 · 2014: 16 · 2013: 15 · 2012: 2
- FWCI
- 13.80
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International Conference on Social Computing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Social Media and Politics
- Keywords
- information dissemination, social contagion, retweets, Twitter, divided attention, cognitive effort, social networking sites, user attention
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