Zhiyuan Cheng
ABSTRACT
This paper addresses the problem of identifying local experts in social media systems like Twitter. Local experts -- in contrast to general topic experts -- have specialized knowledge focused around a particular location, and are important for many applications including answering local information needs and interacting with community experts. And yet identifying these experts is difficult. Hence in this paper, we propose a geo-spatial-driven approach for identifying local experts that leverages the fine-grained GPS coordinates of millions of Twitter users. We propose a local expertise framework that integrates both users' topical expertise and their local authority. Concretely, we estimate a user's local authority via a novel spatial proximity expertise approach that leverages over 15 million geo-tagged Twitter lists. We estimate a user's topical expertise based on expertise propagation over 600 million geo-tagged social connections on Twitter. We evaluate the proposed approach across 56 queries coupled with over 11,000 individual judgments from Amazon Mechanical Turk. We find significant improvement over both general (non-local) expert approaches and comparable local expert finding approaches.
- J. Antin, M. de Sa, and E. F. Churchill. Local experts and online review sites. In CSCW 2012. Google Scholar
Digital Library
- L. Backstrom, E. Sun, and C. Marlow. Find me if you can: improving geographical prediction with social and spatial proximity. In WWW 2010. Google ScholarDigital Library
- K. Balog, L. Azzopardi, and M. De Rijke. Formal models for expert finding in enterprise corpora. In SIGIR 2006. Google ScholarDigital Library
- T. Brants. Inter-annotator agreement for a german newspaper corpus. In Proceeding of the 2nd International Conference on Language Resources and Evaluation, 2010.Google Scholar
- C. S. Campbell, P. P. Maglio, A. Cozzi, and B. Dom. Expertise identification using email communications. In CIKM 2003. Google ScholarDigital Library
- Z. Cheng, J. Caverlee, H. Barthwal, and V. Bachani. Finding local experts on twitter. In WWW 2014. Google ScholarDigital Library
- Z. Cheng, J. Caverlee, and K. Lee. You are where you tweet: a content-based approach to geo-locating twitter users. In CIKM 2010. Google ScholarDigital Library
- E. H. Chi. Who knows?: searching for expertise on the social web: technical perspective. Commun. ACM, 55(4), Apr. 2012. Google ScholarDigital Library
- H. Cramer, M. Rost, and L. E. Holmquist. Performing a check-in: emerging practices, norms and'conflicts' in location-sharing using foursquare. In Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services, MobileHCI '11, 2011. Google ScholarDigital Library
- J. L. Fleiss, J. Cohen, and B. Everitt. Large sample standard errors of kappa and weighted kappa. Psychological Bulletin, 72(5), 1969.Google Scholar
- S. Ghosh, N. Sharma, F. Benevenuto, N. Ganguly, and K. Gummadi. Cognos: crowdsourcing search for topic experts in microblogs. In SIGIR 2012. Google ScholarDigital Library
- T. H. Haveliwala. Topic-sensitive pagerank. In WWW 2002. Google ScholarDigital Library
- B. Hecht, L. Hong, B. Suh, and E. H. Chi. Tweets from justin bieber's heart: the dynamics of the location field in user profiles. In SIGCHI 2011. Google ScholarDigital Library
- K. Kamath, J. Caverlee, K. Lee, and Z. Cheng. Spatio-temporal dynamics of online memes: A study of geo-tagged tweets. In WWW 2013. Google ScholarDigital Library
- H. Kwak, C. Lee, H. Park, and S. Moon. What is twitter, a social network or a news media? In WWW 2010. Google ScholarDigital Library
- T. Lappas, K. Liu, and E. Terzi. A survey of algorithms and systems for expert location in social networks. In Social Network Data Analytics. Springer, 2011.Google ScholarCross Ref
- W. Li, C. Eickhoff, and A. P. de Vries. Geo-spatial domain expertise in microblogs. In ECIR 2014.Google ScholarDigital Library
- X. Liu, W. B. Croft, and M. Koll. Finding experts in community-based question-answering services. In CIKM 2005. Google ScholarDigital Library
- C. Marshall and F. Shipman. Experiences surveying the crowd: Reflections on methods, participation, and reliability. In ACM Web Science 2013. Google ScholarDigital Library
- M. Naaman, Y. J. Song, A. Paepcke, and H. Garcia-Molina. Automatic organization for digital photographs with geographic coordinates. In JCDL 2004. Google ScholarDigital Library
- S. Nowak and S. Rüger. How reliable are annotations via crowdsourcing: a study about inter-annotator agreement for multi-label image annotation. In Proceedings of the international conference on Multimedia information retrieval, 2010. Google ScholarDigital Library
- A. Pal and S. Counts. Identifying topical authorities in microblogs. In WSDM 2011. Google ScholarDigital Library
- S. Scellato, A. Noulas, R. Lambiotte, and C. Mascolo. Socio-spatial properties of online location-based social networks. In ICWSM 2011.Google Scholar
- W. R. Tobler. A computer movie simulating urban growth in the detroit region. Economic Geography, 46:pp. 234--240, 1970.Google ScholarCross Ref
- J. Weng, E. P. Lim, J. Jiang, and Q. He. TwitterRank: finding topic-sensitive influential twitterers. In WSDM 2010. Google ScholarDigital Library
- M. Ye, D. Shou, W.-C. Lee, P. Yin, and K. Janowicz. On the semantic annotation of places in location-based social networks. In SIGKDD 2011. Google ScholarDigital Library
- M. Ye, P. Yin, W.-C. Lee, and D.-L. Lee. Exploiting geographical influence for collaborative point-of-interest recommendation. In SIGIR 2011. Google ScholarDigital Library
- M. Yuan, L. Chen, and P. S. Yu. Personalized privacy protection in social networks. PVLDB, 4(2):141--150, 2010. Google ScholarDigital Library
- J. Zhang, M. S. Ackerman, and L. Adamic. Expertise networks in online communities: structure and algorithms. In WWW 2007. Google ScholarDigital Library
- J. Zhang, J. Tang, and J. Li. Expert finding in a social network. In Advances in Databases: Concepts, Systems and Applications, volume 4443 of Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2007.Google Scholar
- V. W. Zheng, Y. Zheng, X. Xie, and Q. Yang. Collaborative location and activity recommendations with gps history data. In WWW 2010. Google ScholarDigital Library
Index Terms
- Who is the barbecue king of texas?: a geo-spatial approach to finding local experts on twitter
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