ABSTRACT
More and more technologies are taking advantage of the explosion of social media (Web search, content recommendation services, marketing, ad targeting, etc.). This paper focuses on the problem of automatically constructing user profiles, which can significantly benefit such technologies. We describe a general and robust machine learning framework for large-scale classification of social media users according to dimensions of interest. We report encouraging experimental results on 3 tasks with different characteristics: political affiliation detection, ethnicity identification and detecting affinity for a particular business.
- L. Barbosa and F. J. Robust Sentiment Detection on Twitter from Biased and Noisy Data. In Proceedings of COLING, 2010. Google ScholarDigital Library
- F. Benevenuto, G. Magno, T. Rodrigues, and V. Almeida. Detecting Spammers on Twitter. In Proceedings of CEAS, 2010.Google Scholar
- D. Blei, A. Ng, and M. Jordan. Latent Dirichlet Allocation. JMLR, (3):993--1022, 2002. Google ScholarDigital Library
- J. Burger and J. Henderson. An exploration of observable features related to blogger age. In Computational Approaches to Analyzing Weblogs, pages 710--718, 2010.Google Scholar
- Burson-Marsteller. Press Releases Archives. In Archive of Sept 10, 2010.Google Scholar
- Z. Cheng, J. Caverlee, and K. Lee. You are where you tweet: A Content-based Approach to Geo-locating Twitter Users. In Proceedings of CIKM, 2010. Google ScholarDigital Library
- J. H. Friedman. Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5):1189--1232, 2001.Google ScholarCross Ref
- J. H. Friedman. Recent Advances in Predictive (Machine) Learning. Journal of Classification, 23(2):175--197, 2006.Google ScholarCross Ref
- N. Garera and D. Yarovsky. Modeling latent biographic attributes in conversational genres. In Proceedings of CIKM, 2007.Google Scholar
- S. Herring and J. Paolillo. Gender and genre variation in weblogs. In Journal of Sociolinguistics, pages 710--718, 2010.Google Scholar
- A. Java, X. Song, T. Finin, and B. Tseng. Why we twitter: understanding microblogging usage and communities. In Proceedings of the 9th WebKDD and 1st SNA-KDD, 2007. Google ScholarDigital Library
- R. Jones, R. Kumar, B. Pang, and A. Tomkins. I Know What you Did Last Summer - Query Logs and User Privacy. In Proceedings of CIKM, 2007. Google ScholarDigital Library
- S. Kim and E. Hovy. CRYSTAL: Analyzing Predictive Opinions on the Web. In Proceedings of EMNLP, 2007.Google Scholar
- J. Otterbacher. Inferring Gender of Movie Reviewers: Exploiting Writing Style, Content and Metadata. In Proceedings of CIKM, 2010. Google ScholarDigital Library
- M. Pasca. What you seek is what you get: Extraction of class attributes from query logs. In Proceedings of IJCAI, 2007. Google ScholarDigital Library
- M. Pennacchiotti and S. Gurumurthy. Investigating Topic Models for Social Media User Recommendation. In Proceedings of WWW, 2011. Google ScholarDigital Library
- Quantcast. Report May 2010. In http://www.quantcast.com/twitter.com, 2010.Google Scholar
- D. Ramage, S. Dumais, and D. Liebling. Characterizing Microblogs with Topic Models. In Proceedings of ICWSM, 2010.Google Scholar
- D. Rao, Y. D., A. Shreevats, and M. Gupta. Classifying Latent User Attributes in Twitter. In Proceedings of SMUC-10, pages 710--718, 2010. Google ScholarDigital Library
- A. Ritter, C. Cherry, and B. Dolan. Unsupervised Modeling of Twitter Conversations. In Proceedings of HLT-NAACL, 2010. Google ScholarDigital Library
- A. Smola and S. Narayanamurthy. An architecture for parallel topic models. In Proceedings of VLDB, 2010. Google ScholarDigital Library
- S. Somasundaran and J. Wiebe. Recognizing Stances in Ideological On-Line Debates. In Proceedings of NAACL-HLT Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pages 116--124, 2010. Google ScholarDigital Library
- M. Thomas, B. Pang, and L. Lee. Get out the vote: determining support or opposition from congressional floor-debate transcripts. In Proceedings of EMNLP, 2006. Google ScholarDigital Library
- I. Weber and C. Castillo. The Demographics of Web Search. In Proceedings of SIGIR, 2010. Google ScholarDigital Library
- J. Wiebe, T. Wilson, and C. Cardie. Annotating expressions of opinions and emotions in language. In Language Resources and Evaluation, pages 165--210, 2005.Google ScholarCross Ref
- J. Ye, C. Jyh-Herng, C. Jang, and Z. Zhaohui. Stochastic gradient boosted distributed decision trees. In Proceedings of CIKM, 2009. Google ScholarDigital Library
Index Terms
- Democrats, republicans and starbucks afficionados: user classification in twitter
Recommendations
The new war correspondents: the rise of civic media curation in urban warfare
CSCW '13: Proceedings of the 2013 conference on Computer supported cooperative workIn this paper we examine the information sharing practices of people living in cities amid armed conflict. We describe the volume and frequency of microblogging activity on Twitter from four cities afflicted by the Mexican Drug War, showing how citizens ...
"Sorry, I was hacked": a classification of compromised twitter accounts
SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied ComputingOnline social networks like Facebook or Twitter have become powerful information diffusion platforms as they have attracted hundreds of millions of users. The possibility of reaching millions of users within these networks not only attracted standard ...
Uses and gratifications of social networking sites for bridging and bonding social capital
Applying uses and gratifications theory (UGT) and social capital theory, our study examined users of four social networking sites (SNSs) (Facebook, Twitter, Instagram, and Snapchat), and their influence on online bridging and bonding social capital. ...
Comments