research-article

Mining expertise and interests from social media

Authors:

Inbal RonenAuthors Info & Claims

WWW '13: Proceedings of the 22nd international conference on World Wide Web

Pages 515 - 526

https://doi.org/10.1145/2488388.2488434

Published: 13 May 2013 Publication History

Abstract

The rising popularity of social media in the enterprise presents new opportunities for one of the organization's most important needs--expertise location. Social media data can be very useful for expertise mining due to the variety of existing applications, the rich metadata, and the diversity of user associations with content. In this work, we provide an extensive study that explores the use of social media to infer expertise within a large global organization. We examine eight different social media applications by evaluating the data they produce through a large user survey, with 670 enterprise social media users. We distinguish between two semantics that relate a user to a topic: expertise in the topic and interest in it and compare these two semantics across the different social media applications.

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Mining expertise and interests from social media

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cover image ACM Other conferences

WWW '13: Proceedings of the 22nd international conference on World Wide Web

May 2013

1628 pages

ISBN:9781450320351

DOI:10.1145/2488388

General Chairs:
Daniel Schwabe
PUC-Rio - Brazil
,
Virgílio Almeida
UFMG - Brazil
,
Hartmut Glaser
CGI.br - Brazil
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Labs - Spain & Chile
,
Sue Moon
KAIST - South Korea

Copyright © 2013 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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NICBR: Nucleo de Informatcao e Coordenacao do Ponto BR
CGIBR: Comite Gestor da Internet no Brazil

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

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Published: 13 May 2013

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WWW '13: 22nd International World Wide Web Conference

May 13 - 17, 2013

Rio de Janeiro, Brazil

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WWW '13 Paper Acceptance Rate 125 of 831 submissions, 15%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Kang HSoliman NLatzke MChang JBragg J(2023)ComLittee: Literature Discovery with Personal Elected Author CommitteesProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581371(1-20)Online publication date: 19-Apr-2023
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Alagha ASingh SOtrok HMizouni R(2023)Influence- and Interest-Based Worker Recruitment in Crowdsourcing Using Online Social NetworksIEEE Transactions on Network and Service Management10.1109/TNSM.2022.321768920:2(1924-1936)Online publication date: Jun-2023
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