of these microblog postings, the types of expressions, and the movement in positive or negative sentiment.We compared automated methods of classifying sentiment in these microblogs with manual coding. Using a case study approach, we analyzed the range, frequency, timing, and content of tweets in a corporate account

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Abstract—One of the key challenges for users of social media is judging the topical expertise of other users in order to select trustful information sources about specific topics and to judge credibility of content produced by others. In this paper, we explore the usefulness of different types

structure and content of the media object. Specifically, we find that the level of Twitter activity serves as a predictor of changes in topics in the media event. Further we find that conversational cues can identify the key players in the media object and that the content of the Twitter posts can somewhat

tweets favor direct statements of a tweeter’s current activity. Judicious use of #hashtags also helps to encourage retweeting. 1

Twitter, or the world of 140 characters poses serious challenges to the efficacy of topic models on short, messy text. While topic mod-els such as Latent Dirichlet Allocation (LDA) have a long history of successful application to news articles and academic abstracts, they are often less coherent

The challenges of Named Entities Recogni-tion (NER) for tweets lie in the insufficien-t information in a tweet and the unavailabil-ity of training data. We propose to com-bine a K-Nearest Neighbors (KNN) classifi-er with a linear Conditional Random Fields (CRF) model under a semi-supervised learn

In this paper, we present overview of our approach for clus-tering tweets. Due to short text of tweets, traditional text clustering mechanisms alone may not produce optimal re-sults. We believe that there is an underlying theme/topic present in majority of tweets which is evident in growing usage

challenges to browsing and finding valuable information. In this paper we propose a graph-theoretic model for tweet recommendation that presents users with items they may have an interest in. Our model ranks tweets and their authors simultaneously using several networks: the social network connecting

watching political debates suggests that the experience may make viewers feel emotionally negative and insecure, espe-cially when their favored candidate is attacked. Debate viewers may also relish ‘‘being part of’ ’ an event of national importance. These possibilities suggest that engaging in social