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This ebook constitutes the refereed complaints of the 4th overseas convention on Social Computing, Behavioral-Cultural Modeling and Prediction, held in collage Park, MD, united states, March 29-31, 2011. The forty eight papers and three keynotes offered during this quantity have been conscientiously reviewed and chosen from 88 submissions. The papers disguise a variety of issues together with social community research; modeling; laptop studying and knowledge mining; social behaviors; public well-being; cultural features; and results and search.
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Additional resources for Social Computing, Behavioral-Cultural Modeling and Prediction: 4th International Conference, SBP 2011, College Park, MD, USA, March 29-31, 2011. Proceedings
In the following sections, we discuss our data sampling and analysis of tweets. We used Latent Dirichlet Allocation (LDA) to analyze terms and topics from the entire dataset as well as from a subset of tweets created by querying general, tobacco use-related terms. We highlight interesting topics and connections as well as limitations to both approaches. Finally, we discuss our conclusions regarding our research questions as well as possible areas of future study. 2 Methods and Results In this section, we introduce our methods of sampling and collecting tweets.
For example, Topic 4 contains terms that indicate that establishments like bars, clubs, and restaurants use Twitter as a means to promote business as well as tobacco use. In contrast, Topic 2 contains words that relate to addiction recovery by promoting programs that could help individuals quit smoking. The use of LDA in our study demonstrates its potential to extract valuable topics from extremely large datasets of conversational data. While the method proves a valuable outlet to automate the process of removing irrelevant information and to hone in on desired data, it still requires careful human intervention to select query terms for the construction of a relevant subset, and subsequent Identifying Health-Related Topics on Twitter 25 analysis to determine themes.
Opinion fluctuations and persistant disagreement in social networks. Submitted to Ann. Appl. Probab. (2010) 11. : Identifying sets of key players in a social network. Comp. Math. Org. Theory 12, 21–34 (2006) 12. : Maximizing the Spread of Influence through a Social Network. SIGKDD (2003) 13. : Optimization-Based Selection of Influential Agents in a Rural Afghan Social Network. Master’s Thesis, MIT, Operations Research Center, Thesis Supervisor, Asuman Ozdaglar (June 2010) Identifying Health-Related Topics on Twitter An Exploration of Tobacco-Related Tweets as a Test Topic Kyle W.