Document language models, query models, and risk minimization for information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
The Journal of Machine Learning Research
Language model information retrieval with document expansion
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter
HICSS '10 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences
#TwitterSearch: a comparison of microblog search and web search
Proceedings of the fourth ACM international conference on Web search and data mining
Empirical study of topic modeling in Twitter
Proceedings of the First Workshop on Social Media Analytics
Proceedings of the fifth ACM international conference on Web search and data mining
Improving retrieval of short texts through document expansion
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
An analysis of topical proximity in the twitter social graph
SocInfo'12 Proceedings of the 4th international conference on Social Informatics
Leveraging geographical metadata to improve search over social media
Proceedings of the 22nd international conference on World Wide Web companion
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Social media users create virtual connections for various reasons: personal and professional. While significant research efforts have been spent on exploring the dynamics of creation of social network connections, little is known about how those connections influence the content generated by social media users. In this work, we quantitatively evaluate the influence of social networks on social media content providers. Additionally, we propose several document expansion methods, which leverage the content generated by the social networks of the authors of social media documents and compare their effectiveness. Experimental results on a large sample of Twitter data indicate that retrieval models discriminatively leveraging social network content for document expansion outperform both traditional, socially-unaware retrieval models and retrieval models that indiscriminatively utilize all social connections.