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Inferring who-is-who in the Twitter social network

Published: 17 August 2012 Publication History

Abstract

In this paper, we design and evaluate a novel who-is-who service for inferring attributes that characterize individual Twitter users. Our methodology exploits the Lists feature, which allows a user to group other users who tend to tweet on a topic that is of interest to her, and follow their collective tweets. Our key insight is that the List meta-data (names and descriptions) provides valuable semantic cues about who the users included in the Lists are, including their topics of expertise and how they are perceived by the public. Thus, we can infer a user's expertise by analyzing the meta-data of crowdsourced Lists that contain the user. We show that our methodology can accurately and comprehensively infer attributes of millions of Twitter users, including a vast majority of Twitter's influential users (based on ranking metrics like number of followers). Our work provides a foundation for building better search and recommendation services on Twitter.

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cover image ACM Conferences
WOSN '12: Proceedings of the 2012 ACM workshop on Workshop on online social networks
August 2012
80 pages
ISBN:9781450314800
DOI:10.1145/2342549
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 17 August 2012

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Author Tags

  1. crowdsourcing
  2. lists
  3. topic inference
  4. twitter
  5. who is who

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  • Research-article

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SIGCOMM '12
Sponsor:
SIGCOMM '12: ACM SIGCOMM 2012 Conference
August 17, 2012
Helsinki, Finland

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WOSN '12 Paper Acceptance Rate 12 of 36 submissions, 33%;
Overall Acceptance Rate 12 of 36 submissions, 33%

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Cited By

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  • (2022)Characterizing Sponsored Content in Facebook and InstagramProceedings of the 33rd ACM Conference on Hypertext and Social Media10.1145/3511095.3531289(52-63)Online publication date: 28-Jun-2022
  • (2019)Does really no one care? Analyzing the public engagement of communication scientists on TwitterNew Media & Society10.1177/146144481986341322:3(387-408)Online publication date: 27-Jul-2019
  • (2019)Scholarly Twitter MetricsSpringer Handbook of Science and Technology Indicators10.1007/978-3-030-02511-3_28(729-760)Online publication date: 2019
  • (2018)Characterizing and Countering Communal Microblogs During Disaster EventsIEEE Transactions on Computational Social Systems10.1109/TCSS.2018.28029425:2(403-417)Online publication date: Jun-2018
  • (2017)A systematic identification and analysis of scientists on TwitterPLOS ONE10.1371/journal.pone.017536812:4(e0175368)Online publication date: 11-Apr-2017
  • (2017)Quantifying Search BiasProceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing10.1145/2998181.2998321(417-432)Online publication date: 25-Feb-2017
  • (2017)Information Diffusion Predictive Model Using Radiation TransferIEEE Access10.1109/ACCESS.2017.27640015(25946-25957)Online publication date: 2017
  • (2017)Interest-Based Clustering Approach for Social NetworksArabian Journal for Science and Engineering10.1007/s13369-017-2800-z43:2(935-947)Online publication date: 23-Aug-2017
  • (2016)Characterizing communal microblogs during disaster eventsProceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.5555/3192424.3192442(96-99)Online publication date: 18-Aug-2016
  • (2016)Predicting political conflicts from polarized social mediaWeb Intelligence10.3233/WEB-16033314:2(85-97)Online publication date: 25-Apr-2016
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