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Competition among memes in a world with limited attention
Open accessAbstract
The wide adoption of social media has increased the competition among ideas for our finite attention. We employ a parsimonious agent-based model to study whether such a competition may affect the popularity of different memes, the diversity of information we are exposed to, and the fading of our collective interests for specific topics. Agents share messages on a social network but can only pay attention to a portion of the information they receive. In the emerging dynamics of information diffusion, a few memes go viral while most do not. The predictions of our model are consistent with empirical data from Twitter, a popular microblogging platform. Surprisingly, we can explain the massive heterogeneity in the popularity and persistence of memes as deriving from a combination of the competition for our limited attention and the structure of the social network, without the need to assume different intrinsic values among ideas.
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Citations
Cites
(1976) · Cited by 10,423
Power-Law Distributions in Empirical Data
Clauset, A., Shalizi, C. R., & Newman, M. E. J. (2009) · SIAM Review · doi:10.1137/070710111 · Cited by 8,019
Power-law distributions in empirical data
Clauset, A. J., Shalizi, C. R., & Newman, M. E. J. (2018) · Figshare · doi:10.1184/r1/6586835.v1 · Cited by 6,504
Wikinomics: how mass collaboration changes everything
(2007) · Choice Reviews Online · doi:10.5860/choice.44-6933 · Cited by 3,367
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
ON A CLASS OF SKEW DISTRIBUTION FUNCTIONS
SIMON, H. A. (1955) · Biometrika · doi:10.1093/biomet/42.3-4.425 · Cited by 2,771
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
The origin of bursts and heavy tails in human dynamics
Barabási, A.-L. (2005) · Nature · doi:10.1038/nature03459 · Cited by 2,210
The Mathematical Theory of Infectious Diseases and its applications
Ling, N. R. (1978) · Immunology · Cited by 1,934
Everyone's an influencer: quantifying influence on twitter
Bakshy, E., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011) · Proceedings of the fourth ACM international conference on Web search and data mining · doi:10.1145/1935826.1935845 · Cited by 1,689
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
Leskovec, J., & Faloutsos, C. (2006) · Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1150402.1150479 · Cited by 1,170
The attention economy: understanding the new currency of business
(2002) · Choice Reviews Online · doi:10.5860/choice.39-4674 · Cited by 1,139
Romero, D. M., Meeder, B., & Kleinberg, J. (2011) · Proceedings of the 20th international conference on World wide web · doi:10.1145/1963405.1963503 · Cited by 1,082
DALEY, D. J., & KENDALL, D. G. (1964) · Nature · doi:10.1038/2041118a0 · Cited by 872
Information contagion: an empirical study of the spread of news on digg and twitter social networks
Lerman, K., & Ghosh, R. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14021 · Cited by 813
The attention economy and the Net
Goldhaber, M. H. (1997) · First Monday · doi:10.5210/fm.v2i4.519 · Cited by 805
Robust dynamic classes revealed by measuring the response function of a social system
Crane, R., & Sornette, D. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0803685105 · Cited by 786
Morris, S. (2000) · Review of Economic Studies · doi:10.1111/1467-937x.00121 · Cited by 639
Modeling Users' Activity on Twitter Networks: Validation of Dunbar's Number
Gonçalves, B., Perra, N., & Vespignani, A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0022656 · Cited by 638
Self-citationNovelty and collective attention
Wu, F., & Huberman, B. A. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0704916104 · Cited by 608
Influence and Passivity in Social Media
Romero, D. M., Galuba, W., Asur, S., & Huberman, B. A. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-23808-6_2 · Cited by 579
Patterns of Cascading Behavior in Large Blog Graphs
Leskovec, J., McGlohon, M., Faloutsos, C., Glance, N., & Hurst, M. (2007) · Proceedings of the 2007 SIAM International Conference on Data Mining · doi:10.1137/1.9781611972771.60 · Cited by 570
Predicting the Behavior of Techno-Social Systems
Vespignani, A. (2009) · Science · doi:10.1126/science.1171990 · Cited by 495
Self-citationSemiotic dynamics and collaborative tagging
Cattuto, C., Loreto, V., & Pietronero, L. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0610487104 · Cited by 395
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
Trends in Social Media: Persistence and Decay
Asur, S., Huberman, B. A., Szabo, G., & Wang, C. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1755748 · Cited by 296
Evaluating similarity measures for emergent semantics of social tagging
Markines, B., Cattuto, C., Menczer, F., Benz, D., Hotho, A., & Stumme, G. (2009) · Proceedings of the 18th international conference on World wide web · doi:10.1145/1526709.1526796 · Cited by 287
Self-citationCompeting epidemics on complex networks
Karrer, B., & Newman, M. E. J. (2011) · Physical Review E · doi:10.1103/physreve.84.036106 · Cited by 254
Spontaneous emergence of social influence in online systems
Onnela, J.-P., & Reed-Tsochas, F. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0914572107 · Cited by 251
Characterizing and Modeling the Dynamics of Online Popularity
Ratkiewicz, J., Fortunato, S., Flammini, A., Menczer, F., & Vespignani, A. (2010) · Physical Review Letters · doi:10.1103/physrevlett.105.158701 · Cited by 249
Self-citationWe know what @you #tag: does the dual role affect hashtag adoption?
Yang, L., Sun, T., Zhang, M., & Mei, Q. (2012) · Proceedings of the 21st international conference on World Wide Web · doi:10.1145/2187836.2187872 · Cited by 211
Cascading Behavior in Large Blog GraphsPreprint
Leskovec, J., McGlohon, M., Faloutsos, C., Glance, N., & Hurst, M. F. (2007) · arXiv (Cornell University) · doi:10.48550/arxiv.0704.2803 · Cited by 165
Götz, M., Leskovec, J., McGlohon, M., & Faloutsos, C. (2009) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v3i1.13941 · Cited by 138
Falkinger, J. (2006) · Journal of Economic Theory · doi:10.1016/j.jet.2005.12.001 · Cited by 138
Trends in Social Media: Persistence and Decay
Asur, S., Huberman, B. A., Szabo, G., & Wang, C. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14167 · Cited by 122
Proceedings of the European Conference on Complex Systems 2012
Gilbert, T., Kirkilionis, M., & Nìcolis, G. (2013) · Springer proceedings in complexity · doi:10.1007/978-3-319-00395-5 · Cited by 96
The Meme Ranking Problem: Maximizing Microblogging Virality
Ienco, D., Bonchi, F., & Castillo, C. (2010) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2010.127 · Cited by 57
Stochastic Models of User-Contributory Web Sites
Hogg, T., & Lerman, K. (2009) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v3i1.13939 · Cited by 54
A scalable, collaborative similarity measure for social annotation systems
Markines, B., & Menczer, F. (2009) · ACM Conference on Hypertext and Hypermedia (HT) · doi:10.1145/1557914.1557982 · Cited by 28
Self-citationAn individual-based model of collective attentionPreprint
Moussaïd, M., Helbing, D., & Guy Theraulaz, A. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0909.2757 · Cited by 6
Dunbar, R. I. M. (1998) · Evolutionary Anthropology: Issues, News, and Reviews · doi:10.1002/(sici)1520-6505(1998)6:5
Cited by
Beyond misinformation: Understanding and coping with the “post-truth” era.
Lewandowsky, S., Ecker, U. K. H., & Cook, J. (2017) · Journal of Applied Research in Memory and Cognition · doi:10.1016/j.jarmac.2017.07.008 · Cited by 1,985
Twenge, J. M., Martin, G. N., & Spitzberg, B. H. (2019) · Psychology of Popular Media Culture · doi:10.1037/ppm0000203 · Cited by 703
Virality Prediction and Community Structure in Social Networks
Weng, L., Menczer, F., & Ahn, Y.-Y. (2013) · Scientific Reports · doi:10.1038/srep02522 · Cited by 512
Self-citationDynamics 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
Socializing in emergencies—A review of the use of social media in emergency situations
Simon, T., Goldberg, A., & Adini, B. (2015) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2015.07.001 · Cited by 461
Metadata
- Authors
- Weng, L., Flammini, A., Vespignani, A., & Menczer, F.
- Venue
- Scientific Reports
- Published
- 2012-03-29
- Volume
- 2
- Issue
- 1
- Pages
- 335-335
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2072606289
- Cited by
- 620
- References
- 44
- FWCI
- 37.22
- Type
- journal article
- Language
- en
- Open access
- gold
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