Abstract
Social media provides a fertile ground for expertise location. The public nature of the data supports expertise inference with little privacy infringement and, in addition, presentation of direct and detailed evidence for an expert’s skillfulness in the queried topic. In this work, we study the use of social media for expertise evidence. We conducted two user surveys of enterprise social media users within a large global organization, in which participants were asked to rate anonymous experts based on artificial and real evidence originating from different types of social media data. Our results indicate that the social media data types perceived most convincing as evidence are not necessarily the ones from which expertise can be inferred most precisely or effectively. We describe these results in detail and discuss implications for designers and architects of expertise location systems.
Part of the research was conducted while working at IBM Research.
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References
Adamic, L. A., Zhang J., Bakshy, E., & Ackerman, M. S. (2008). Knowledge sharing and yahoo answers: Everyone knows something. In Proceedings of the WWW’08 (pp. 665–674).
Balog, K., Azzopardi, L., & De Rijke, M. (2006). Formal models for expert finding in enterprise corpora. In Proceedings of the SIGIR’06 (pp. 43–50).
Becerra-Fernandez, I. (2000). Facilitating the online search of experts at NASA using expert seeker people-finder. In Proceedings of the PAKM’00.
Chi, E. H. (2012). Who knows? Searching for expertise on the social web: Technical perspective. Communications of the ACM, 55(4), 110–110.
DiMicco, J., Millen, D. R., Geyer, W., Dugan, C., Brownholtz, B., & Muller, M. (2008). Motivations for social networking at work. In Proceedings of the CSCW’08 (pp. 711–720).
Farrell, S., Lau, T., Nusser, S., Wilcox, E., & Muller, M. (2007). Socially augmenting employee profiles with people-tagging. In Proceedings of the UIST’07 (pp. 91–100).
Guy, I., Avraham, U., Carmel, D., Ur, S., Jacovi, M., & Ronen, I. (2013). Mining expertise and interests from social media. In Proceedings of the WWW’13 (pp. 515–526).
Guy, I., Ronen, I., & Wilcox, E. (2009). Do you know? Recommending people to invite into your social network. In Proceedings of the IUI’09 (pp. 77–86).
Herlocker, J. L., Konstan, J. A., & Riedl, J. (2000). Explaining collaborative filtering recommendations. In Proceedings of the CSCW’00 (pp. 241–250).
Kardan, A., Garakani, M., & Bahrani, B. (2010). A method to automatically construct a user knowledge model in a forum environment. In Proceedings of the SIGIR’10 (pp. 717–718).
Kolari, P., Finin, T., Lyons, K., & Yesha, Y. (2008). Expert search using internal corporate blogs. In Workshop on Future Challenges in Expertise Retrieval, SIGIR’08 (pp. 2–5).
Li, C. T., & Shan, M. K. (2013). X2-search: Contextual expert search in social networks’. In Conference on Technologies and Applications of Artificial Intelligence (TAAI) (pp. 176–181).
Macdonald, C., Hannah, D., & Ounis, I. (2008). High quality expertise evidence for expert search. In Advances in Information Retrieval (pp. 283–295).
McDonald, D. W., & Ackerman, M. S. (2000). Expertise recommender: A flexible recommendation system and architecture. In Proceedings of the CSCW’00 (pp. 231–240).
Noll, M. G., Yeung, A. C., Gibbins, N., Meinel, C., & Shadbolt, N. (2009). Telling experts from spammers: Expertise ranking in folksonomies. In Proceedings of the SIGIR’09 (pp. 612–619).
Pu, P., & Chen, L. (2006, January). Trust building with explanation interfaces. In Proceedings of the 11th International Conference on Intelligent User Interfaces (pp. 93–100).
Reichling, T., & Wulf, V. (2009). Expert recommender systems in practice: Evaluating semi-automatic profile generation. In Proceedings of the CHI’09 (pp. 59–68).
Ronen, I., Guy, I., Kravi, E., & Barnea, M. (2014). Recommending social media content to community owners. In Proceedings of the SIGIR’14 (pp. 243–252).
Serdyukov, P., & Hiemstra, D. (2008). Being omnipresent to be almighty: The importance of global web evidence for organizational expert finding. In Workshop on Future Challenges in Expertise Retrieval, SIGIR’08 (pp. 17–24).
Sinha, R., & Swearingen, K. (2002). The role of transparency in recommender systems. In CHI’02 extended abstracts on human factors in computing systems (pp. 830–831).
Tintarev, N., & Masthoff, J. (2007). A survey of explanations in recommender systems. In 23rd International Conference on Data Engineering Workshop (pp. 801–810).
Varshney, K. R., Chenthamarakshan, V., Fancher, S. W., Wang, J., Fang, D., & Mojsilovic, A. (2014). Predicting employee expertise for talent management in the enterprise. In Proceedings of the KDD’14 (pp. 1729–1738).
Vig, J., Sen, S., & Riedl, J. (2009). Tagsplanations: Explaining recommendations using tags. In Proceedings of the 14th International Conference on Intelligent User Interfaces (pp. 47–56).
Xu, Z. (2014). Expertise retrieval in enterprise microblogs with enhanced models and brokers (Doctoral dissertation, The Ohio State University).
Yiman-Seid, D., & Kobsa, A. (2003). Expert-finding systems for organizations: Problem and domain analysis and the DEMOIR approach. JOCEC, 13(1), 1–24.
Zhang, J., Ackerman, M. S., & Adamic, L. (2007). Expertise networks in online communities: Structure and algorithms. In Proceedings of the WWW’07 (pp. 221–230).
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Yogev, A., Guy, I., Ronen, I., Zwerdling, N., Barnea, M. (2015). Social Media-Based Expertise Evidence. In: Boulus-Rødje, N., Ellingsen, G., Bratteteig, T., Aanestad, M., Bjørn, P. (eds) ECSCW 2015: Proceedings of the 14th European Conference on Computer Supported Cooperative Work, 19-23 September 2015, Oslo, Norway. Springer, Cham. https://doi.org/10.1007/978-3-319-20499-4_4
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