The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
The Simple Rules of Social Contagion
Open accessAbstract
It is commonly believed that information spreads between individuals like a pathogen, with each exposure by an informed friend potentially resulting in a naive individual becoming infected. However, empirical studies of social media suggest that individual response to repeated exposure to information is far more complex. As a proxy for intervention experiments, we compare user responses to multiple exposures on two different social media sites, Twitter and Digg. We show that the position of exposing messages on the user-interface strongly affects social contagion. Accounting for this visibility significantly simplifies the dynamics of social contagion. The likelihood an individual will spread information increases monotonically with exposure, while explicit feedback about how many friends have previously spread it increases the likelihood of a response. We provide a framework for unifying information visibility, divided attention and explicit social feedback to predict the temporal dynamics of user behavior.
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Cites
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Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market
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“Explicit social feedback can significantly magnify user response, albeit making it less useful for popularizing high-quality content [28].”
BackgroundThe role of hubs in the adoption process
Goldenberg, J., Han, S., Lehmann, D. R., & Hong, J. W. (2009) · Journal of Marketing · doi:10.1509/jmkg.73.2.1 · Cited by 695
Modeling Users' Activity on Twitter Networks: Validation of Dunbar's Number
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The dynamics of protest recruitment through an online network
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How Visibility and Divided Attention Constrain Social Contagion
Hodas, N. O., & Lerman, K. (2012) · 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing · doi:10.1109/socialcom-passat.2012.129 · Cited by 136
Self-citation
Cited by
Measuring Emotional Contagion in Social Media
Ferrara, E., & Yang, Z. (2015) · PLOS ONE · doi:10.1371/journal.pone.0142390 · Cited by 532
Dynamics of information diffusion and its applications on complex networks
Zhang, Z.-K., Liu, C., Zhan, X.-X., Lu, X., Zhang, C.-X., & Zhang, Y.-C. (2016) · Physics Reports · doi:10.1016/j.physrep.2016.07.002 · Cited by 484
The emergence of consensus: a primer
Baronchelli, A. (2018) · Royal Society Open Science · doi:10.1098/rsos.172189 · Cited by 161
Competing for Attention in Social Media under Information Overload Conditions
Feng, L., Hu, Y., Li, B., Stanley, H. E., Havlin, S., & Braunstein, L. A. (2015) · PLOS ONE · doi:10.1371/journal.pone.0126090 · Cited by 133
“This suggests that these weak links play a much more important role in information diffusion in OSNs than in face-to-face social networks where there is social interaction [33].”
BackgroundAuditing Algorithmic Bias on Twitter
Bartley, N., Abeliuk, A., Ferrara, E., & Lerman, K. (2021) · 13th ACM Web Science Conference 2021 · doi:10.1145/3447535.3462491 · Cited by 62
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Metadata
- Authors
- Hodas, N. O., & Lerman, K.
- Venue
- Scientific Reports
- Published
- 2014-03-11
- Volume
- 4
- Issue
- 1
- Pages
- 4343-4343
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2078939860
- Cited by
- 246
- References
- 10
- FWCI
- 10.11
- Type
- journal article
- Language
- en
- Open access
- gold
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