Everyone's an influencer: quantifying influence on twitter
Abstract
In this paper we investigate the attributes and relative influence of 1.6M Twitter users by tracking 74 million diffusion events that took place on the Twitter follower graph over a two month interval in 2009. Unsurprisingly, we find that the largest cascades tend to be generated by users who have been influential in the past and who have a large number of followers. We also find that URLs that were rated more interesting and/or elicited more positive feelings by workers on Mechanical Turk were more likely to spread. In spite of these intuitive results, however, we find that predictions of which particular user or URL will generate large cascades are relatively unreliable. We conclude, therefore, that word-of-mouth diffusion can only be harnessed reliably by targeting large numbers of potential influencers, thereby capturing average effects. Finally, we consider a family of hypothetical marketing strategies, defined by the relative cost of identifying versus compensating potential "influencers." We find that although under some circumstances, the most influential users are also the most cost-effective, under a wide range of plausible assumptions the most cost-effective performance can be realized using "ordinary influencers"---individuals who exert average or even less-than-average influence.
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It is plausible to relate individuals' social influence to the size of information cascades they generate with their content.
“Here, we argue that it is also plausible to relate individuals’ social influence to the size of information cascades they generates with their content [35].”
From Influence of augmented humans in online interactions during voting events (2019) · cites this paper as [35]
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- Authors
- Bakshy, E., Hofman, J. M., Mason, W. A., & Watts, D. J.
- Affiliations
- University of Michigan (US)
- Yahoo (United States) (US)
- Venue
- Proceedings of the fourth ACM international conference on Web search and data mining
- Publisher
- ACM
- Published
- 2011-02-09
- Pages
- 65-74
- OpenAlex
- W1967579779
- Cited by
- 1,689
- References
- 41
- Citations by year
- 2026: 31 · 2025: 46 · 2024: 41 · 2023: 78 · 2022: 68 · 2021: 121 · 2020: 121 · 2019: 149 · 2018: 138 · 2017: 114 · 2016: 136 · 2015: 152 · 2014: 202 · 2013: 166 · 2012: 89
- FWCI
- 140.44
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Web Search and Data Mining (WSDM) (publishedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
- Social Sciences › Communication › Social Media and Politics
- Keywords
- Twitter, information dissemination, influential users, follower count, word-of-mouth diffusion, marketing strategy
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