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Am i more similar to my followers or followees?: analyzing homophily effect in directed social networks

Published: 01 September 2014 Publication History

Abstract

Homophily is the formation of social ties between two individuals due to similar characteristics or interests. Based on homophily, in a social network it is expected to observe a higher degree of homogeneity among connected than disconnected people. Many researchers use this simple yet effective principal to infer users' missing information and interests based on the information provided by their neighbors. In a directed social network, the neighbors can be further divided into followers and followees. In this work, we investigate the homophily effect in a directed network. To explore the homophily effect in a directed network, we study if a user's personal preferences can be inferred from those of users connected to her (followers or followees). We investigate which of followers or followees are more effective in helping to infer users' personal preferences. Our findings can help to raise the awareness of users over their privacy and can help them better manage their privacy.

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Cited By

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  • (2018)Automatic acquisition of a taxonomy of microblogs users interestsWeb Semantics: Science, Services and Agents on the World Wide Web10.1016/j.websem.2017.05.00445:C(23-40)Online publication date: 20-Dec-2018
  • (2014)Scalable learning of users' preferences using networked dataProceedings of the 25th ACM conference on Hypertext and social media10.1145/2631775.2631796(4-12)Online publication date: 1-Sep-2014

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      cover image ACM Conferences
      HT '14: Proceedings of the 25th ACM conference on Hypertext and social media
      September 2014
      346 pages
      ISBN:9781450329545
      DOI:10.1145/2631775
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      Published: 01 September 2014

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      Author Tags

      1. homophily
      2. preference prediction
      3. relational learning
      4. social media mining

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      HT '14 Paper Acceptance Rate 49 of 86 submissions, 57%;
      Overall Acceptance Rate 378 of 1,158 submissions, 33%

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      • (2018)Automatic acquisition of a taxonomy of microblogs users interestsWeb Semantics: Science, Services and Agents on the World Wide Web10.1016/j.websem.2017.05.00445:C(23-40)Online publication date: 20-Dec-2018
      • (2014)Scalable learning of users' preferences using networked dataProceedings of the 25th ACM conference on Hypertext and social media10.1145/2631775.2631796(4-12)Online publication date: 1-Sep-2014

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