User interactions in social networks and their implications
Abstract
Social networks are popular platforms for interaction, communication and collaboration between friends. Researchers have recently proposed an emerging class of applications that leverage relationships from social networks to improve security and performance in applications such as email, web browsing and overlay routing. While these applications often cite social network connectivity statistics to support their designs, researchers in psychology and sociology have repeatedly cast doubt on the practice of inferring meaningful relationships from social network connections alone.
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Linking biases impact the study of social networks structure and evolution, social influence, and information diffusion phenomena.
“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 Wilson et al., 2009
The network constructed based on explicit links was significantly denser than the one based on user interactions.
“Wilson et al. (2009) also showed that the network constructed based on explicit links was significantly denser than the one based on user interactions.”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Wilson et al., 2009
Cites
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Measurement and analysis of online social networks
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What is Twitter, a social network or a news media?
Kwak, H., Lee, C., Park, H., & Moon, S. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772751 · Cited by 6,652 · see citing works
A Survey on Bias and Fairness in Machine Learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2022) · ACM Computing Surveys · doi:10.1145/3457607 · Cited by 5,812 · see citing works
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763 · see citing works
“Exploring the differences between social networks based on explicit vs. implicit links among users, Wilson et al. (2009) also showed that the network constructed based on explicit links was significantly denser than the one based on user interactions.”
BackgroundUncovering social network Sybils in the wild
Yang, Z., Wilson, C., Wang, X., Gao, T., Zhao, B. Y., & Dai, Y. (2014) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2556609 · Cited by 290 · see citing works
Self-citationStringhini, G., Wang, G., Egele, M., Kruegel, C., Vigna, G., Zheng, H., & Zhao, B. Y. (2013) · Proceedings of the 2013 conference on Internet measurement conference · doi:10.1145/2504730.2504731 · Cited by 169 · see citing works
Self-citationUncovering social network sybils in the wild
Yang, Z., Wilson, C., Wang, X., Gao, T., Zhao, B. Y., & Dai, Y. (2011) · Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference · doi:10.1145/2068816.2068841 · Cited by 164 · see citing works
Self-citationChallenges in the Decentralised Web: The Mastodon Case
Raman, A., Joglekar, S., Cristofaro, E. D., Sastry, N., & Tyson, G. (2019) · Proceedings of the Internet Measurement Conference · doi:10.1145/3355369.3355572 · Cited by 118 · see citing works
Making friends and communicating on Facebook: Implications for the access to social capital
Bohn, A., Buchta, C., Hornik, K., & Mair, P. (2014) · Social Networks · doi:10.1016/j.socnet.2013.11.003 · Cited by 106 · see citing works
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016) · doi:10.2139/ssrn.2886526 · Cited by 763 · see citing works
Fairness metrics and bias mitigation strategies for rating predictions
Ashokan, A., & Haas, C. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102646 · Cited by 47 · see citing works
- Authors
- Wilson, C., Boe, B., Sala, A., Puttaswamy, K. P. N., & Zhao, B. Y.
- Affiliations
- University of California, Santa Barbara (US)
- Venue
- Proceedings of the 4th ACM European conference on Computer systems
- Publisher
- ACM
- Published
- 2009-04
- Pages
- 205-218
- OpenAlex
- W2047443612
- Cited by
- 879
- References
- 4
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 7 · 2023: 12 · 2022: 20 · 2021: 24 · 2020: 29 · 2019: 31 · 2018: 59 · 2017: 53 · 2016: 88 · 2015: 94 · 2014: 79 · 2013: 106 · 2012: 125
- FWCI
- 87.20
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- European Conference on Computer Systems (publishedVersion)
- Funders
- National Science Foundation
- Max Planck Institute for Software Systems
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Keywords
- social networks, user interaction, relationship inference, social network security, overlay routing, email, web browsing
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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