The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Connecting users across social media sites: a behavioral-modeling approach
Abstract
People use various social media for different purposes. The information on an individual site is often incomplete. When sources of complementary information are integrated, a better profile of a user can be built to improve online services such as verifying online information. To integrate these sources of information, it is necessary to identify individuals across social media sites. This paper aims to address the cross-media user identification problem. We introduce a methodology (MOBIUS) for finding a mapping among identities of individuals across social media sites. It consists of three key components: the first component identifies users' unique behavioral patterns that lead to information redundancies across sites; the second component constructs features that exploit information redundancies due to these behavioral patterns; and the third component employs machine learning for effective user identification. We formally define the cross-media user identification problem and show that MOBIUS is effective in identifying users across social media sites. This study paves the way for analysis and mining across social media sites, and facilitates the creation of novel online services across sites.
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Cites
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Cited by
Social Media Mining: An Introduction
Zafarani, R., Abbasi, M. A., & Liu, H. (2014) · Cambridge University Press eBooks · doi:10.1017/cbo9781139088510 · Cited by 592
Self-citationUser Identity Linkage across Online Social Networks: A Review
Shu, K., Wang, S., Tang, J., Zafarani, R., & Liu, H. (2017) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3068777.3068781 · Cited by 267
Self-citationAn Army of Me: Sockpuppets in Online Discussion Communities
Kumar, S., Cheng, J., Leskovec, J., & Subrahmanian, V. S. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052677 · Cited by 178
When is it biased?: assessing the representativeness of twitter's streaming API
Morstatter, F., Pfeffer, J., & Liu, H. (2014) · Proceedings of the 23rd International Conference on World Wide Web · doi:10.1145/2567948.2576952 · Cited by 90
Self-citationTang, J., & Liu, H. (2015) · Synthesis Lectures on Information Security, Privacy, and Trust · doi:10.2200/s00657ed1v01y201507spt013 · Cited by 53
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Metadata
- Authors
- Zafarani, R., & Liu, H.
- Venue
- Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2013-08-11
- Pages
- 41-49
- OpenAlex
- W1980680715
- Cited by
- 408
- References
- 15
- FWCI
- 69.44
- Type
- conference paper
- Language
- en
- Open access
- closed
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