for determining the gender of uncharacterized Twitter users. We explore several different classifier types on this dataset. We show the degree to which classifier accuracy varies based on tweet volumes as well as when various kinds of profile metadata are included in the models. We also perform a large

of seven months. In the process, we collect a dataset of 1.8 billion tweets, 80 million of which belong to spam accounts. We use our dataset to characterize the behavior and lifetime of spam accounts, the campaigns they execute, and the wide-spread abuse of legitimate web services such as URL shorteners

Abstract. Sentiment analysis over Twitter offers organisations and individuals a fast and effective way to monitor the publics ’ feelings towards them and their competitors. To assess the performance of sentiment analysis methods over Twitter a small set of evaluation datasets have been released

Though Twitter acts as a realtime news source with people acting as sensors and sending event updates from all over the world, rumors spread via Twitter have been noted to cause considerable damage. Given a set of popular Twitter events along with related users and tweets, we study the problem

are simply the ones with better past performance records: While we do find evidence of persistence in performance from one fund to the next, our measures of network centrality continue to have a positive and significant effect on fund exit rates when we control for persistence. The way we construct

longitudinal panel dataset of 8K Twitter users, we evalu-ated the performance of HybridRank and predicted the in-fluence of users. We used the number of times that a user was retweeted or mentioned as a proxy for influence. Hy-bridRank outperforms a (good) baseline method for men-tions outside the focal user’s

". Experimental results show that our method can successfully discover communities in Twitter, and gives a much better performance than random selection. From a side view, our experiment also shows that users in our dataset of Twitter can be approximately categorized into 400 communities.

phenomena of an individual ceasing to “follow” another in Twitter’s directed social network. We examine these relationships using a dataset of 245,586 Twitter “follow ” edges, and the persistence of these edges after nine months. We show that structural properties of individuals and dyads at Time 1 have a

Abstract: We investigate the impact of U.S. bombing on later economic development in Vietnam. The Vietnam War featured the most intense bombing campaign in military history and had massive humanitarian costs. We use a unique U.S. military dataset containing bombing intensity at the district level

-tionnaires. We conduct a computational analysis of self-disclosure with a large dataset of naturally-occurring conversa-tions, a semi-supervised machine learning algorithm, and a computational analysis of the effects of self-disclosure on subse-quent conversations. We use a longitu-dinal dataset of 17 million