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Title: Linking organizational social network profiles
Authors: Cheng, J
Sugiyama, K 
Kan, MY 
Issue Date: 7-Jul-2016
Publisher: ACM
Citation: Cheng, J, Sugiyama, K, Kan, MY (2016-07-07). Linking organizational social network profiles. SIGIR '16: The 39th International ACM SIGIR conference on research and development in Information Retrieval. ScholarBank@NUS Repository.
Abstract: Many organizations possess social media accounts on different social networks, but these profiles are not always linked. End applications, users, as well as the organization themselves, can benefit when the profiles are appropriately identified and linked. Most existing works on social network entity linking focus on linking individuals, and do not model features specific for organizational linking. We address this gap not only to link official social media accounts but also to discover and solve the identification and linking of associated affiliate accounts - such as geographical divisions and brands - which are important to distinguish. We instantiate our method for classifying profiles on social network services for Twitter and Facebook, which major organizations use. We classify profiles as to whether they belong to an organization or its affiliates. Our best classifier achieves an accuracy of 0.976 on average in both datasets, significantly improving baselines that exploit the features used in state-of-the-art comparable user linkage strategies.
Source Title: SIGIR '16: The 39th International ACM SIGIR conference on research and development in Information Retrieval
ISBN: 9.78145E+12
DOI: 10.1145/2911451.2914698
Appears in Collections:Staff Publications

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