Popularity spikes hurt future chances for viral propagation of protomemes

Abstract

Once a meme gets popular, it will have to evolve to keep being popular.

References

1. Adamic, L., Lento, T., Adar, E., and Ng, P. Information Evolution in Social Networks. In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining (San Francisco, CA, Feb. 22--25). ACM Press, New York, 2016.
2. Bauckhage, C., Kersting, K., and Hadiji, F. Mathematical models of fads explain the temporal dynamics of Internet memes. In Proceedings of the Seventh AAAI International Conference on Weblogs and Social Media (Cambridge, MA, July 8--11). Association for the Advancement of Artificial Intelligence, Palo Alto, CA, 2013.
3. Berger, J. and Milkman, K.L. What makes online content viral? Journal of Marketing Research 49, 2 (Apr. 2012), 192--205.
4. Borgelt, C. Efficient implementations of Apriori and Eclat. In Proceedings of the International Conference on Data Mining (Melbourne, FL, Nov. 19--22). IEEE Computer Society, New York, 2003.
5. Cha, M., Haddadi, H., Benevenuto, F., and Gummadi, P.K. Measuring user influence in Twitter: The million-follower fallacy. In Proceedings of the AAAI International Conference on Weblogs and Social Media (Washington, D.C., May 23--26). Association for the Advancement of Artificial Intelligence, Palo Alto, CA, 2010, 30--38.
6. Cheng, J., Adamic, L.A., Dow, P.A., Kleinberg, J.M., and Leskovec, J. Can cascades be predicted? In Proceedings of the World Wide Web Conference (Seoul, Korea, Apr. 7--11). ACM Press, New York, 2014, 925--936.
7. Christakis, N.A. and Fowler, J.H. The spread of obesity in a large social network over 32 years. The New England Journal of Medicine 357, 4 (July 2007), 370--379.
8. Christakis, N.A. and Fowler, J.H. The collective dynamics of smoking in a large social network. The New England Journal of Medicine 358, 21 (May 2008), 2249--2258.
9. Ciampaglia, G.L., Flammini, A., and Menczer, F. The production of information in the attention economy. Scientific Reports 5, 2015; https://www.nature.com/articles/srep09452
10. Coscia, M. Competition and success in the meme pool: A case study on quickmeme.com. In Proceedings of the Seventh AAAI International Conference on Weblogs and Social Media (Boston, MA, July 8--10). Association for the Advancement of Artificial Intelligence, Palo Alto, CA, 2013.
11. Coscia, M. Average is boring: How similarity kills a meme's success. In Scientific Reports 4, 2014; https://www.nature.com/articles/srep06477
12. Dawkins, R. The Selfish Gene. Oxford University Press, Oxford, U.K., 1976.
13. Gabrilovich, E., Dumais, S., and Horvitz, E. Newsjunkie: Providing personalized newsfeeds via analysis of information novelty. In Proceedings of the 13^th International World Wide Web Conference (New York, May 17--22). ACM Press, New York, 2004, 482--490.
14. Gilbert, E. Widespread underprovision on Reddit. In Proceedings of the 2013 Conference on Computer Supported Cooperative Work (San Antonio, TX, Feb. 23--27). ACM Press, New York, 2013, 803--808.
15. Gleeson, J.P., O'Sullivan, K.P., Baños, R.A., and Moreno, Y. Determinants of meme popularity. arXiv, 2015; http://cosnet.bifi.es/wp-content/uploads/2015/02/1501.05956v1.pdf
16. Harrigan, N., Achananuparp, P., and Lim, E.-P. Influentials, novelty, and social contagion: The viral power of average friends, close communities, and old news. Social Networks 34, 4 (Oct. 2012), 470--480.
17. Lakkaraju, H., McAuley, J.J., and Leskovec, J. What's in a name? Understanding the interplay between titles, content, and communities in social media. In Proceedings of the Seventh International Conference on Weblogs and Social Media (Boston, MA, July 8--11). Association for the Advancement of Artificial Intelligence, Palo Alto, CA, 2013.
18. Leskovec, J., Backstrom, L., and Kleinberg, J. Meme-tracking and the dynamics of the news cycle. In Proceedings of the 15^th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Paris, France, June 28-July 1). ACM Press, New York, 2009, 497--506.
19. Nematzadeh, A., Ferrara, E., Flammini, A., and Ahn, Y.-Y. Optimal network modularity for information diffusion. Physical Review Letters 113, 8 (Aug. 2014).
20. Romero, D.M., Meeder, B., and Kleinberg, J. Differences in the mechanics of information diffusion across topics: Idioms, political hashtags, and complex contagion on Twitter. In Proceedings of the 20^th International Conference on World Wide Web (Hyderabad, India, Mar. 28-Apr. 1). ACM Press, New York, 2011, 695--704.
21. Sanli, C. and Lambiotte, R. Local variation of hashtag spike trains and popularity in Twitter. arXiv, 2015; https://arxiv.org/abs/1503.03349
22. Suen, C., Huang, S., Eksombatchai, C., Sosic, R., and Leskovec, J. Nifty: A system for large-scale information flow tracking and clustering. In Proceedings of the 22^nd International Conference on World Wide Web (Rio de Janeiro, Brazil, May 13--17). ACM Press, New York, 2013, 1237--1248.
23. Weng, L., Flammini, A., Vespignani, A., and Menczer F. Competition among memes in a world with limited attention. Scientific Reports 2, 2012; https://www.nature.com/articles/srep00335
24. Weng, L., Menczer, F., and Ahn, Y.-Y. Predicting successful memes using network and community structure. In Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media (Ann Arbor, MI, June 2--4). AAAI Press, Palo Alto, CA, 2014.
25. Weninger, T., Zhu, X.A., and Han, J. An exploration of discussion threads in social news sites: A case study of the Reddit community. In Proceedings of the International Conference on Advances in Social Network Analysis and Mining (Niagara Falls, ON, Canada, Aug. 25--28). IEEE Computer Society, New York, 2013, 579--583.

Index Terms

• Applied computing

  • Law, social and behavioral sciences

• Human-centered computing

  • Collaborative and social computing

• Information systems

  • World Wide Web

• Networks

  • Network types

    • Public Internet