@i seek 'fb.me': identifying users across multiple online social networks
Open accessAbstract
An online user joins multiple social networks in order to enjoy different services. On each joined social network, she creates an identity and constitutes its three major dimensions namely profile, content and connection network. She largely governs her identity formulation on any social network and therefore can manipulate multiple aspects of it. With no global identifier to mark her presence uniquely in the online domain, her online identities remain unlinked, isolated and difficult to search. Literature has proposed identity search methods on the basis of profile attributes, but has left the other identity dimensions e.g. content and network, unexplored. In this work, we introduce two novel identity search algorithms based on content and network attributes and improve on traditional identity search algorithm based on profile attributes of a user. We apply proposed identity search algorithms to find a user's identity on Facebook, given her identity on Twitter. We report that a combination of proposed identity search algorithms found Facebook identity for 39% of Twitter users searched while traditional method based on profile attributes found Facebook identity for only 27.4%. Each proposed identity search algorithm access publicly accessible attributes of a user on any social network. We deploy an identity resolution system, Finding Nemo, which uses proposed identity search methods to find a Twitter user's identity on Facebook. We conclude that inclusion of more than one identity search algorithm, each exploiting distinct dimensional attributes of an identity, helps in improving the accuracy of an identity resolution process.
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Specialized fraud workers control multiple user accounts and seek employment from product developers to post fake reviews or activities.
“Specialized fraud workers (also referred to as fraud freelancers, or fraudsters) control multiple user accounts and seek employment by product developers to post fake reviews or activities for their products.”
From Fraud De-Anonymization for Fun and Profit (2018) · cites this paper as [23]
Cites
De-anonymizing Social Networks
Narayanan, A., & Shmatikov, V. (2009) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2009.22 · Cited by 1,324 · see citing works
Magno, G., Rodrigues, T., & Almeida, V. A. F. (2010) · Cited by 649 · see citing works
Grier, C., Thomas, K., Paxson, V., & Zhang, M. (2010) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/1866307.1866311 · Cited by 558 · see citing works
Zheleva, E., & Getoor, L. C. (2009) · International World Wide Web Conference (WWW) · doi:10.1145/1526709.1526781 · Cited by 532 · see citing works
Chu, Z., Gianvecchio, S., Wang, H., & Jajodia, S. (2010) · Annual Computer Security Applications Conference · doi:10.1145/1920261.1920265 · Cited by 498 · see citing works
Semantic-integration research in the database community: A brief survey
Doan, A., & Halevy, A. Y. (2005) · doi:10.1609/aimag.v26i1.1801 · Cited by 424 · see citing works
On the leakage of personally identifiable information via online social networks
Krishnamurthy, B., & Wills, C. E. (2009) · doi:10.1145/1592665.1592668 · Cited by 371 · see citing works
Exploiting innocuous activity for correlating users across sites
Goga, O., Lei, H., Parthasarathi, S. H. K., Friedland, G. H., Sommer, R., & Teixeira, R. (2013) · International World Wide Web Conference (WWW) · doi:10.1145/2488388.2488428 · Cited by 246 · see citing works
Studying User Footprints in Different Online Social Networks
Malhotra, A., Totti, L., Meira, W., Kumaraguru, P., & Almeida, V. A. F. (2012) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2012.184 · Cited by 221 · see citing works
Self-citationDetecting spammers and content promoters in online video social networks
Benevenuto, F., Rodrigues, T., Almeida, V., Almeida, J., & Gonçalves, M. (2009) · Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1571941.1572047 · Cited by 220 · see citing works
How Unique and Traceable Are Usernames?
Perito, D., Castelluccia, C., Kâafar, M. A., & Manils, P. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-22263-4_1 · Cited by 206 · see citing works
Identifying Users Across Social Tagging Systems
Iofciu, T., Fankhauser, P., Abel, F., et al. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14153 · Cited by 206 · see citing works
Semantic-integration research in the database community
Doan, A., & Halevy, A. Y. (2005) · AI Magazine · doi:10.5555/1090488.1090497 · Cited by 201 · see citing works
User Profile Matching in Social Networks
Raad, E., Chbeir, R., & Dipandà, A. (2010) · International Conference on Network-Based Information Systems · doi:10.1109/nbis.2010.35 · Cited by 169 · see citing works
Linking social networks on the web with FOAF: a semantic web case study
Golbeck, J., & Rothstein, M. (2008) · National Conference on Artificial Intelligence · Cited by 140 · see citing works
On the leakage of personally identifiable information via online social networks
Krishnamurthy, B., & Wills, C. E. (2010) · ACM SIGCOMM Computer Communication Review · doi:10.1145/1672308.1672328 · Cited by 113 · see citing works
Joint Link-Attribute User Identity Resolution in Online Social Networks
Bartunov, S., Anton, K., Park, S.-T., et al. (2012) · Cited by 105 · see citing works
Large Online Social Footprints--An Emerging Threat
Irani, D., Webb, S., Li, K., & Pu, C. (2009) · doi:10.1109/cse.2009.459 · Cited by 103 · see citing works
Privacy in India: Attitudes and Awareness
Kumaraguru, P., & Cranor, L. F. (2006) · Lecture notes in computer science · doi:10.1007/11767831_16 · Cited by 94 · see citing works
Self-citationMotoyama, M., & Varghese, G. (2009) · ACM International Workshop on Web Information and Data Management (WIDM) · doi:10.1145/1651587.1651604 · Cited by 89 · see citing works
Correlating user profiles from multiple folksonomies
Szomszor, M., Cantador, I., & Alani, H. (2008) · doi:10.1145/1379092.1379103 · Cited by 74 · see citing works
Detecting Spammers and Content Promoters in Online Video Social Networks
Benevenuto, F., Rodrigues, T., Almeida, J. M., et al. (2009) · IEEE INFOCOM Workshops · doi:10.1109/infcomw.2009.5072127 · Cited by 63 · see citing works
Chen, T., Kâafar, M. A., Friedman, A., et al. (2012) · doi:10.1145/2342549.2342565 · Cited by 46 · see citing works
Study of Static Classification of Social Spam Profiles in MySpace
Irani, D., Webb, S., & Pu, C. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14017 · Cited by 35 · see citing works
The credibility of digital identity information on the social web
Rowe, M. (2010) · doi:10.1145/1772938.1772947 · Cited by 20 · see citing works
Privacy in India: Attitudes and Awareness V 2.0Preprint
Kumaraguru, P., & Sachdeva, N. (2012) · SSRN Electronic Journal · doi:10.2139/ssrn.2188749 · Cited by 19 · see citing works
Self-citationCross-Pollination of Information in Online Social Media: A Case Study on Popular Social Networks
Jain, P., Rodrigues, T., Magno, G., et al. (2011) · doi:10.1109/passat/socialcom.2011.182 · Cited by 15 · see citing works
Self-citationWhACKY! - What anyone could know about you from Twitter
Correa, D., Sureka, A., & Sethi, R. (2012) · International Conference on Privacy, Security and Trust (PST) · doi:10.1109/pst.2012.6297918 · Cited by 11 · see citing works
Social networks profile mapping using games
Shehab, M., Ko, M. N., & Touati, H. (2012) · Cited by 10 · see citing works
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Fraud De-Anonymization for Fun and Profit
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- Authors
- Jain, P., Kumaraguru, P., & Joshi, A.
- Affiliations
- Indraprastha Institute of Information Technology Delhi (IN)
- University of Maryland, Baltimore County (US)
- Venue
- Proceedings of the 22nd International Conference on World Wide Web
- Publisher
- ACM
- Published
- 2013-05-13
- Pages
- 1259-1268
- OpenAlex
- W2113105081
- Cited by
- 128
- References
- 29
- Citations by year
- 2024: 4 · 2023: 3 · 2022: 4 · 2021: 7 · 2020: 14 · 2019: 9 · 2018: 15 · 2017: 16 · 2016: 26 · 2015: 16 · 2014: 11 · 2013: 3
- FWCI
- 31.55
- Type
- conference paper
- Language
- en
- Open access
- green
- doi.org
- Locations
- International World Wide Web Conference (WWW) (publishedVersion)
- Maryland Shared Open Access Repository (USMAI Consortium) (submittedVersion)
- Digital Repository at the University of Maryland (University of Maryland College Park)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Keywords
- identity resolution, social network users, Facebook, Twitter, content attributes, Finding Nemo
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