Information contagion: an empirical study of the spread of news on digg and twitter social networks
Open accessAbstract
Social networks have emerged as a critical factor in information dissemination, search, marketing, expertise and influence discovery, and potentially an important tool for mobilizing people. Social media has made social networks ubiquitous, and also given researchers access to massive quantities of data for empirical analysis. These data sets offer a rich source of evidence for studying dynamics of individual and group behavior, the structure of networks and global patterns of the flow of information on them. However, in most previous studies, the structure of the underlying networks was not directly visible but had to be inferred from the flow of information from one individual to another. As a result, we do not yet understand dynamics of information spread on networks or how the structure of the network affects it. We address this gap by analyzing data from two popular social news sites. Specifically, we extract social networks of active users on Digg and Twitter, and track how interest in news stories spreads among them. We show that social networks play a crucial role in the spread of information on these sites, and that network structure affects dynamics of information flow.
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Studies looking at similarities and differences among social platforms found differences with respect to user personalities, news spreading, or content sharing.
“For instance, studies looking at similarities and differences among social platforms found differences with respect to user personalities (Hughes et al., 2012), news spreading (Lerman and Ghosh, 2010), or content sharing (Ottoni et al., 2014).”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Lerman and Ghosh, 2010
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Graphs over time: densification laws, shrinking diameters and possible explanations
Leskovec, J., Kleinberg, J., & Faloutsos, C. (2005) · Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining · doi:10.1145/1081870.1081893 · Cited by 2,281 · see citing works
Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market
Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006) · Science · doi:10.1126/science.1121066 · Cited by 2,219 · see citing works
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The role of social networks in information diffusion
Bakshy, E., Rosenn, I., Marlow, C., & Adamic, L. (2012) · Proceedings of the 21st international conference on World Wide Web · doi:10.1145/2187836.2187907 · Cited by 1,526 · see citing works
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763 · see citing works
“9 For instance, studies looking at similarities and differences among social platforms found differences with respect to user personalities (Hughes et al., 2012), news spreading (Lerman and Ghosh, 2010), or content sharing (Ottoni et al., 2014).”
BackgroundTufekci, Z. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14517 · Cited by 780 · see citing works
Ghiassi, M., Skinner, J., & Zimbra, D. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.05.057 · Cited by 485 · see citing works
(How) will the revolution be retweeted?: information diffusion and the 2011 Egyptian uprising
Starbird, K., & Palen, L. (2012) · Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work · doi:10.1145/2145204.2145212 · Cited by 409 · see citing works
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 · see citing works
Self-citationCompeting 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 · see citing works
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016) · doi:10.2139/ssrn.2886526 · Cited by 763 · see citing works
The role of hidden influentials in the diffusion of online information cascades
Baños, R. A., Borge-Holthoefer, J., & Moreno, Y. (2013) · EPJ Data Science · doi:10.1140/epjds18 · Cited by 79 · see citing works
Emotions in online rumor diffusion
Pröllochs, N., Bär, D., & Feuerriegel, S. (2021) · EPJ Data Science · doi:10.1140/epjds/s13688-021-00307-5 · Cited by 63 · see citing works
Diffusion of Scientific Articles across Online Platforms
Zakhlebin, I., & Horvát, E.-Á. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7341 · Cited by 16 · see citing works
Online Hate: Is Hate an Infectious Disease? Is Social Media a Promoter?
Popa‐Wyatt, M. (2023) · Journal of Applied Philosophy · doi:10.1111/japp.12679 · Cited by 17 · see citing works
- Authors
- Lerman, K., & Ghosh, R.
- Affiliations
- University of Southern California (US)
- ORCID
- Lerman, Kristina
- Venue
- Proceedings of the International AAAI Conference on Web and Social Media
- Published
- 2010-05-16
- Volume
- 4
- Issue
- 1
- Pages
- 90-97
- ISSN
- 2162-3449 (ISSN-L)
- 2334-0770
- OpenAlex
- W1752870744
- Cited by
- 813
- References
- 2
- Citations by year
- 2026: 6 · 2025: 18 · 2024: 20 · 2023: 29 · 2022: 24 · 2021: 59 · 2020: 50 · 2019: 43 · 2018: 63 · 2017: 76 · 2016: 80 · 2015: 79 · 2014: 107 · 2013: 78 · 2012: 45
- FWCI
- 35.29
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the International AAAI Conference on Web and Social Media (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (0915678)
- 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 contagion, social networks, network structure, Digg, Twitter, information dissemination, social media
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