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Equality and Social Mobility in Twitter Discussion Groups

Published: 08 February 2016 Publication History

Abstract

Online groups, including chat groups and forums, are becoming important avenues for gathering and exchanging information ranging from troubleshooting devices, to sharing experiences, to finding medical information and advice. Thus, issues about the health and stability of these groups are of particular interest to both industry and academia. In this paper we conduct a large scale study with the objectives of first, characterizing essential aspects of the interactions between the participants of such groups and second, characterizing how the nature of these interactions relate to the health of the groups. Specifically, we concentrate on Twitter Discussion Groups (TDGs), self-organized groups that meet on Twitter by agreeing on a hashtag, date and time. These groups have repeated, real-time meetings and are a rising phenomenon on Twitter. We examine the interactions in these groups in terms of the social equality and mobility of the exchange of attention between participants, according to the @mention convention on Twitter. We estimate the health of a group by measuring the retention rate of participants and the change in the number of meetings over time. We find that social equality and mobility are correlated, and that equality and mobility are related to a group's health. In fact, equality and mobility are as predictive of a group's health as some prior characteristics used to predict health of other online groups. Our findings are based on studying 100 thousand sessions of over two thousand discussion groups over the period of June 2012 to June 2013. These finding are not only relevant to stakeholders interested in maintaining these groups, but to researchers and academics interested in understanding the behavior of participants in online discussions. We also find the parallel with findings on the relationship between economic mobility and equality and health indicators in real-world nations striking and thought-provoking.

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  • (2016)Knowledge Discovery from Big Social Key-Value Data2016 IEEE International Conference on Computer and Information Technology (CIT)10.1109/CIT.2016.37(484-491)Online publication date: Dec-2016
  • (2016)Knowledge Discovery from Social Graph DataProcedia Computer Science10.1016/j.procs.2016.08.25096:C(682-691)Online publication date: 1-Oct-2016

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    cover image ACM Conferences
    WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining
    February 2016
    746 pages
    ISBN:9781450337168
    DOI:10.1145/2835776
    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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    Published: 08 February 2016

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    Author Tags

    1. generative model
    2. graph analysis
    3. social networks

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    WSDM 2016
    WSDM 2016: Ninth ACM International Conference on Web Search and Data Mining
    February 22 - 25, 2016
    California, San Francisco, USA

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    WSDM '16 Paper Acceptance Rate 67 of 368 submissions, 18%;
    Overall Acceptance Rate 498 of 2,863 submissions, 17%

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    • (2016)Knowledge Discovery from Big Social Key-Value Data2016 IEEE International Conference on Computer and Information Technology (CIT)10.1109/CIT.2016.37(484-491)Online publication date: Dec-2016
    • (2016)Knowledge Discovery from Social Graph DataProcedia Computer Science10.1016/j.procs.2016.08.25096:C(682-691)Online publication date: 1-Oct-2016

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