The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Influence and Passivity in Social Media
Open accessAbstract
The ever-increasing amount of information flowing through Social Media forces the members of these networks to compete for attention and influence by relying on other people to spread their message. A large study of information propagation within Twitter reveals that the majority of users act as passive information consumers and do not forward the content to the network. Therefore, in order for individuals to become influential they must not only obtain attention and thus be popular, but also overcome user passivity. We propose an algorithm that determines the influence and passivity of users based on their information forwarding activity. An evaluation performed with a 2.5 million user dataset shows that our influence measure is a good predictor of URL clicks, outperforming several other measures that do not explicitly take user passivity into account. We demonstrate that high popularity does not necessarily imply high influence and vice-versa. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Cites
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
Measuring user influence in twitter: The million follower fallacy
Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, K. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14033 · Cited by 3,022
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
Cited by
De Veirman, M., Cauberghe, V., & Hudders, L. (2017) · International Journal of Advertising · doi:10.1080/02650487.2017.1348035 · Cited by 2,548
Measuring social media influencer index- insights from facebook, Twitter and Instagram
Arora, A., Bansal, S., Kandpal, C., Aswani, R., & Dwivedi, Y. (2019) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2019.03.012 · Cited by 601
Detecting and Tracking Political Abuse in Social Media
Ratkiewicz, J., Conover, M., Meiss, M., Goncalves, B., Flammini, A., & Menczer, F. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14127 · Cited by 579
Tweets and Trades: the Information Content of Stock Microblogs
Sprenger, T. O., Tumasjan, A., Sandner, P. G., & Welpe, I. M. (2014) · European Financial Management · doi:10.1111/j.1468-036x.2013.12007.x · Cited by 561
Ratkiewicz (2011) · doi:10.1145/1963192.1963301 · Cited by 541
Metadata
- Authors
- Romero, D. M., Galuba, W., Asur, S., & Huberman, B. A.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2011
- Pages
- 18-33
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2952777860
- Cited by
- 579
- References
- 3
- FWCI
- 49.06
- Type
- conference paper
- Language
- en
- Open access
- green
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