The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Measuring User Influence in Twitter: The Million Follower Fallacy
Open accessAbstract
Directed links in social media could represent anything from intimate friendships to common interests, or even a passion for breaking news or celebrity gossip. Such directed links determine the flow of information and hence indicate a user's influence on others — a concept that is crucial in sociology and viral marketing. In this paper, using a large amount of data collected from Twitter, we present an in-depth comparison of three measures of influence: indegree, retweets, and mentions. Based on these measures, we investigate the dynamics of user influence across topics and time. We make several interesting observations. First, popular users who have high indegree are not necessarily influential in terms of spawning retweets or mentions. Second, most influential users can hold significant influence over a variety of topics. Third, influence is not gained spontaneously or accidentally, but through concerted effort such as limiting tweets to a single topic. We believe that these findings provide new insights for viral marketing and suggest that topological measures such as indegree alone reveals very little about the influence of a user.
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“artificially inflating the number of followers can also be finalized to make an account more trustworthy and influential, in order to stand from the crowd and to attract other genuine followers [5].”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as [5]
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Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Linking biases impact the study of, e.g., social networks structure and evolution, social influence, and information diffusion phenomena”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Cha et al., 2010
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Different graph construction methods can lead to different structural graph properties in the various kinds of networks that can be constructed from social data”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Cha et al., 2010
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Citations
Cites
Granovetter, M. S. (1973) · American Journal of Sociology · doi:10.1086/225469 · Cited by 40,491 · cited 1×
“Furthermore , because of the tight connection between users as suggested in the triadic closure (Granovetter 1973), retweeting in a social network can serve as a powerful tool to reinforce a message—for instance, the probability of adopting an innovation increases when not one but a group of users…”
BackgroundRogers, E. M., Singhal, A., & Quinlan, M. M. (2019) · Routledge eBooks · doi:10.4324/9780203710753-35 · Cited by 37,670
Stanton, B. F., & Rogers, E. M. (1963) · Journal of Farm Economics · doi:10.2307/1235767 · Cited by 6,726
Detecting influenza epidemics using search engine query data
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009) · Nature · doi:10.1038/nature07634 · Cited by 4,295 · cited 1×
“Google similarly normalizes the data when analyzing their search trends (Ginsberg et al. 2009).”
The Tipping Point: How Little Things Can Make a Big Difference
Meadows-Klue, D. (2004) · Interactive Marketing · doi:10.1057/palgrave.im.4340262 · Cited by 3,064 · cited 3×
“The notion of influence plays a vital role in how businesses operate and how a society functions—for instance, see observations on how fashion spreads (Gladwell 2002) and how people vote (Berry and Keller 2003).”
Mining the network value of customers
Domingos, P., & Richardson, M. (2001) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/502512.502525 · Cited by 2,884 · cited 2×
“Instead, it posits that the key factors determining influence are (i) the interpersonal relationship among ordinary users and (ii) the readiness of a society to adopt an innovation (Watts and Dodds 2007; Domingos and Richardson 2001).”
BackgroundPersonal Influence: The Part Played by People in the Flow of Mass Communications
Willigan, W. L., Katz, E., & Lazarsfeld, P. F. (1956) · The American Catholic Sociological Review · doi:10.2307/3708755 · Cited by 2,173
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 2,065
Book Review: Personal Influence: The Part Played by People in the Flow of Mass Communications
Roshwalb, I., Katz, E., & Lazarsfeld, P. F. (1956) · Journal of Marketing · doi:10.2307/1247718 · Cited by 1,900
Influentials, Networks, and Public Opinion Formation
Watts, D. J., & Dodds, P. S. (2007) · Journal of Consumer Research · doi:10.1086/518527 · Cited by 1,864
Research works printed but not linked: 1
- Levy, M., and Windhal, S. 1985. Media Gratications Research, Chapter The Concept of Audience Activity. Sage.
Cited by
Sorted by citations
The Culture of Connectivity: A Critical History of Social Media
van Dijck, J. (2013) · doi:10.1093/acprof:oso/9780199970773.001.0001 · Cited by 2,688
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
“Consistent with previous work [7, 18, 35], both the in-degree (‘followers”) and out-degree (“friends”) distributions are highly skewed, but the former much more so—whereas the maximum # of followers was nearly 4M, the maximum # of friends was only about 760K—reflecting the passive and one-way nature…”
BackgroundResultEmotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior
Stieglitz, S., & Dang-Xuan, L. (2013) · Journal of Management Information Systems · doi:10.2753/mis0742-1222290408 · Cited by 1,604
Who says what to whom on twitter
Wu, S., Hofman, J. M., Mason, W., & Watts, D. J. (2011) · International World Wide Web Conference (WWW) · doi:10.1145/1963405.1963504 · Cited by 969
“[3] compared three measures of influence—number”
Prominent Features of Rumor Propagation in Online Social Media
Kwon, S., Cha, M., Jung, K., Chen, W., & Wang, Y. (2013) · 2013 IEEE 13th International Conference on Data Mining · doi:10.1109/icdm.2013.61 · Cited by 859
“We used the Twitter data reported in [4], which comprise the profile information of 54 million users, the 1.”
MethodSelf-citation
Metadata
- Authors
- Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, K.
- Affiliations
- Max Planck Society (DE)
- University of London (GB)
- Venue
- Proceedings of the International AAAI Conference on Web and Social Media
- Published
- 2010-05-16
- Volume
- 4
- Issue
- 1
- Pages
- 10-17
- ISSN
- 2162-3449 (ISSN-L)
- 2334-0770
- OpenAlex
- W1814023381
- Semantic Scholar
- 1ad8410d0ded269af4a0116d8b38842a7549f0ae
- Cited by
- 3,022
- References
- 21
- Citations by year
- 2026: 44 · 2025: 74 · 2024: 61 · 2023: 83 · 2022: 130 · 2021: 212 · 2020: 184 · 2019: 198 · 2018: 257 · 2017: 266 · 2016: 253 · 2015: 287 · 2014: 329 · 2013: 310 · 2012: 193
- FWCI
- 142.70
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the International AAAI Conference on Web and Social Media (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Communication › Social Media and Politics
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Keywords
- user influence, Twitter, influence measures, in-degree, retweets, mentions, directed links, viral marketing, information flow, topic dynamics, influential users
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