%0 Conference Paper %1 Guy:2013:MEI:2488388.2488434 %A Guy, Ido %A Avraham, Uri %A Carmel, David %A Ur, Sigalit %A Jacovi, Michal %A Ronen, Inbal %B Proceedings of the 22Nd International Conference on World Wide Web %C Republic and Canton of Geneva, Switzerland %D 2013 %I International World Wide Web Conferences Steering Committee %K expert_finding experts socialMedia %P 515--526 %T Mining Expertise and Interests from Social Media %U http://dl.acm.org/citation.cfm?id=2488388.2488434 %X 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. %@ 978-1-4503-2035-1

@inproceedings{Guy:2013:MEI:2488388.2488434, 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.}, acmid = {2488434}, added-at = {2015-03-03T11:31:48.000+0100}, address = {Republic and Canton of Geneva, Switzerland}, author = {Guy, Ido and Avraham, Uri and Carmel, David and Ur, Sigalit and Jacovi, Michal and Ronen, Inbal}, biburl = {https://www.bibsonomy.org/bibtex/24b8d62d265b785aaaec5ba2d4019c964/asmelash}, booktitle = {Proceedings of the 22Nd International Conference on World Wide Web}, description = {Mining expertise and interests from social media}, interhash = {0e1fd1c753f026d0323b64d25193991b}, intrahash = {4b8d62d265b785aaaec5ba2d4019c964}, isbn = {978-1-4503-2035-1}, keywords = {expert_finding experts socialMedia}, location = {Rio de Janeiro, Brazil}, numpages = {12}, pages = {515--526}, publisher = {International World Wide Web Conferences Steering Committee}, series = {WWW '13}, timestamp = {2015-03-03T11:33:38.000+0100}, title = {Mining Expertise and Interests from Social Media}, url = {http://dl.acm.org/citation.cfm?id=2488388.2488434}, year = 2013 }