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Virality Prediction and Community Structure in Social Networks
Open accessAbstract
How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed spread like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection contributes to our understanding in computational social science, social media analytics, and marketing applications.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Granovetter, M. S. (1973) · American Journal of Sociology · doi:10.1086/225469 · Cited by 40,491
Birds of a Feather: Homophily in Social Networks
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001) · Annual Review of Sociology · doi:10.1146/annurev.soc.27.1.415 · Cited by 18,567
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
Newman, M. (2010) · doi:10.1093/acprof:oso/9780199206650.001.0001 · Cited by 6,595
Infectious Diseases of Humans: Dynamics and Control
Anderson, R. M., & May, R. M. (1991) · doi:10.1093/oso/9780198545996.001.0001 · Cited by 6,179
Threshold models of collective behavior
Granovetter, M. (1978) · American Journal of Sociology · doi:10.1086/226707 · Cited by 6,072
Epidemic Spreading in Scale-Free Networks
Pastor-Satorras, R., & Vespignani, A. (2001) · Physical Review Letters · doi:10.1103/physrevlett.86.3200 · Cited by 5,731
The Spread of Obesity in a Large Social Network over 32 Years
Christakis, N. A., & Fowler, J. H. (2007) · New England Journal of Medicine · doi:10.1056/nejmsa066082 · Cited by 5,007
Maps of random walks on complex networks reveal community structure
Rosvall, M., & Bergstrom, C. T. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0706851105 · Cited by 4,583
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., et al. (2009) · Science · doi:10.1126/science.1167742 · Cited by 3,365
Identification of influential spreaders in complex networks
Kitsak, M., Gallos, L. K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H. E., & Makse, H. A. (2010) · Nature Physics · doi:10.1038/nphys1746 · Cited by 3,212
What Makes Online Content Viral?
Berger, J., & Milkman, K. L. (2012) · Journal of Marketing Research · doi:10.1509/jmr.10.0353 · Cited by 3,178 · cited 2×
“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].”
BackgroundDynamical Processes on Complex Networks
Barrat, A., Barthélemy, M., & Vespignani, A. (2008) · doi:10.1017/cbo9780511791383 · Cited by 2,704
The spread of behavior in an online social network experiment
Centola, D. (2010) · Science · doi:10.1126/science.1185231 · Cited by 2,692
A simple model of global cascades on random networks
Watts, D. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.082090499 · Cited by 2,673
A 61-million-person experiment in social influence and political mobilization
Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D. I., Marlow, C., Settle, J. E., & Fowler, J. H. (2012) · Nature · doi:10.1038/nature11421 · Cited by 2,575
The dynamics of viral marketing
Leskovec, J., Adamic, L. A., & Huberman, B. A. (2007) · ACM Transactions on the Web · doi:10.1145/1232722.1232727 · Cited by 2,032
Link communities reveal multiscale complexity in networks
Ahn, Y.-Y., Bagrow, J. P., & Lehmann, S. (2010) · Nature · doi:10.1038/nature09182 · Cited by 1,949
Self-citationStructure and tie strengths in mobile communication networks
Onnela, J.-P., Saramäki, J., Hyvönen, J., Szabó, G., Lazer, D., Kaski, K., Kertész, J., & Barabási, A.-L. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0610245104 · Cited by 1,926
Group formation in large social networks: membership, growth, and evolution
Backstrom, L., Huttenlocher, D., Kleinberg, J., & Lan, X. (2006) · Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1150402.1150412 · Cited by 1,805
Meme-tracking and the dynamics of the news cycle
Leskovec, J., Backstrom, L., & Kleinberg, J. (2009) · Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1557019.1557077 · Cited by 1,530
Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks
Aral, S., Muchnik, L., & Sundararajan, A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0908800106 · Cited by 1,207
Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network
Suh, B., Hong, L., Pirolli, P., & Chi, E. H. (2010) · 2010 IEEE Second International Conference on Social Computing · doi:10.1109/socialcom.2010.33 · Cited by 1,166
Homophily and contagion are generically confounded in observational social network studies
Shalizi, C. R., & Thomas, A. C. (2011) · Sociological Methods & Research · doi:10.1177/0049124111404820 · Cited by 994 · cited 1×
“Separating social contagion and homophily is difficult [41, 42], and we interpret complex contagion broadly to include homophily; we focus on how both social reinforcement and homophily effects collectively boost the trapping of memes within dense communities, not on the distinctions between them.”
BackgroundPredicting the popularity of online content
Szabo, G., & Huberman, B. A. (2010) · Communications of the ACM · doi:10.1145/1787234.1787254 · Cited by 983
Dynamics of rumor spreading in complex networks
Moreno, Y., Nekovee, M., & Pacheco, A. F. (2004) · Physical Review E · doi:10.1103/physreve.69.066130 · Cited by 936
DALEY, D. J., & KENDALL, D. G. (1964) · Nature · doi:10.1038/2041118a0 · Cited by 872
Aral, S., & Walker, D. (2011) · Management Science · doi:10.1287/mnsc.1110.1421 · Cited by 786
Structural diversity in social contagion
Ugander, J., Backstrom, L., Marlow, C., & Kleinberg, J. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1116502109 · Cited by 695
An Experimental Study of Homophily in the Adoption of Health Behavior
Centola, D. (2011) · Science · doi:10.1126/science.1207055 · Cited by 681
Small but slow world: How network topology and burstiness slow down spreading
Karsai, M., Kivelä, M., Pan, R. K., Kaski, K., Kertész, J., Barabási, A.-L., & Saramäki, J. (2011) · Physical Review E · doi:10.1103/physreve.83.025102 · Cited by 668
Competition among memes in a world with limited attention
Weng, L., Flammini, A., Vespignani, A., & Menczer, F. (2012) · Scientific Reports · doi:10.1038/srep00335 · Cited by 620
Self-citationUniversal Behavior in a Generalized Model of Contagion
Dodds, P. S., & Watts, D. J. (2004) · Physical Review Letters · doi:10.1103/physrevlett.92.218701 · Cited by 571
Predicting the Behavior of Techno-Social Systems
Vespignani, A. (2009) · Science · doi:10.1126/science.1171990 · Cited by 495
Generalization of Epidemic Theory: An Application to the Transmission of Ideas
GOFFMAN, W., & NEWILL, V. A. (1964) · Nature · doi:10.1038/204225a0 · Cited by 392
Dynamical classes of collective attention in twitter
Lehmann, J., Gonçalves, B., Ramasco, J. J., & Cattuto, C. (2012) · Proceedings of the 21st international conference on World Wide Web · doi:10.1145/2187836.2187871 · Cited by 380
Social Features of Online Networks: The Strength of Intermediary Ties in Online Social Media
Grabowicz, P. A., Ramasco, J. J., Moro, E., Pujol, J. M., & Eguiluz, V. M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0029358 · Cited by 263
Cascades on correlated and modular random networks
Gleeson, J. P. (2008) · Physical Review E · doi:10.1103/physreve.77.046117 · Cited by 220
The Digital Evolution of Occupy Wall Street
Conover, M. D., Ferrara, E., Menczer, F., & Flammini, A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0064679 · Cited by 205
Self-citationBorge-Holthoefer, J., Rivero, A., García, I., Cauhé, E., Ferrer, A., Ferrer, D., Francos, D., Iñiguez, D., Pérez, M. P., Ruiz, G., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0023883 · Cited by 182
The Geospatial Characteristics of a Social Movement Communication Network
Conover, M., Davis, C. A., Ferrara, E., McKelvey, K., Menczer, F., & Flammini, A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0055957 · Cited by 154
Self-citationDigging Digg: Comment Mining, Popularity Prediction, and Social Network Analysis
Jamali, S., & Rangwala, H. (2009) · International Conference on Web Information Systems and Mining (WISM) · doi:10.1109/wism.2009.15 · Cited by 127
Cascading dynamics in modular networks
Galstyan, A., & Cohen, P. R. (2007) · Physical Review E · doi:10.1103/physreve.75.036109 · Cited by 106
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Self-citationA Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances
Zhou, F., Xu, X., Trajcevski, G., & Zhang, K. (2022) · ACM Computing Surveys · doi:10.1145/3433000 · Cited by 259
How algorithmic popularity bias hinders or promotes quality
Ciampaglia, G. L., Nematzadeh, A., Menczer, F., & Flammini, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-34203-2 · Cited by 204
Self-citationWhat makes cancer information viral on social media?
Wang, X., Chen, L., Shi, J., & Peng, T.-Q. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2018.12.024 · Cited by 93
Alhabash, S., Almutairi, N., Lou, C., & Kim, W. (2019) · Media Psychology · doi:10.1080/15213269.2017.1416296 · Cited by 80
Metadata
- Authors
- Weng, L., Menczer, F., & Ahn, Y.-Y.
- Venue
- Scientific Reports
- Published
- 2013-08-28
- Volume
- 3
- Issue
- 1
- Pages
- 2522-2522
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2156716308
- Cited by
- 512
- References
- 44
- FWCI
- 26.79
- Type
- journal article
- Language
- en
- Open access
- gold
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