This paper exploits a large number of self-labeled emotion tweets as the training data from the source domain to improve emotion identification in target domains (i.e., blogs and fairy tales), where there is a short supply of labeled data. Due to the noisy and ambiguous nature of self-labeled emotion training data, the existing domain adaptation methods that typically depend on high-quality labeled source-domain data do not work satisfactorily. This paper describes an adaptive source-domain training instance selection method to address the problem of noisy source-domain training data. The proposed approach can effectively identify the most informative training examples based on three carefully designed measures: consistency, diversity, and similarity. It uses an iterative method that consists of the following steps in each iteration: selecting informative samples from the source domain with the informativeness measures, merging with the target-domain training data, evaluating the performance of learned classifier for the target domain, and updating the informativeness measures for the next iteration. It stops until no new training instance is selected or in a designated number of iterations. Experiments show that our approach performs effectively for cross-domain emotion identification and consistently outperforms baseline approaches across four domains.

Inappropriate tweets can cause severe damages on authors' reputation or privacy. However, many users do not realize the negative consequences until they publish these tweets. Published tweets have lasting effects that may not be eliminated by simple deletion because other users may have read them or third-party tweet analysis platforms have cached them. Regrettable tweets, i.e., tweets with identifiable regrettable contents, cause the most damage on their authors because other users can easily notice them. In this paper, we study how to identify the regrettable tweets published by \emph{normal individual users} via the contents and users' historical deletion patterns. We identify normal individual users based on their publishing, deleting, followers and friends statistics. We manually examine a set of randomly sampled deleted tweets from these users to identify regrettable tweets and understand the corresponding regrettable reasons. By applying content-based features and personalized history-based features, we develop classifiers that can effectively predict regrettable tweets.

Cursing is not uncommon during conversations in the physical world: 0.5% to 0.7% of all the words we speak are curse words, given that 1% of all the words are first-person plural pronouns (e.g., we, us, our). On social media, people can instantly chat with friends without face-to-face interaction, usually in a more public fashion and broadly disseminated through highly connected social network. Will these distinctive features of social media lead to a change in people's cursing behavior? In this paper, we examine the characteristics of cursing activity on a popular social media platform - Twitter, involving the analysis of about 51 million tweets and about 14 million users. In particular, we explore a set of questions that have been recognized as crucial for understanding cursing in offline communications by prior studies, including the ubiquity, utility, and contextual dependencies of cursing.