Suchergebnisse

Online discussion forums are a type of social media which contains rich usercontributed facts, opinions, and user interactions on diverse topics. The large volume of opinionated data generated in online discussions provides an ideal testbed for user opinion mining. In particular, mining user opinions on social and political issues from online discussions is useful not only to government organizations and companies but also to social and political scientists. In this dissertation, we propose to study the task of mining user viewpoints or stances from online discussions on social and political issues. Specifically, we will talk about our proposed approaches for these sub-tasks, namely, viewpoint discovery, micro-level and macro-level stance prediction, and user viewpoint summarization. We first study how to model user posting behaviors for viewpoint discovery. We have two models for modeling user posting behaviors. Our first model takes three important characteristics of online discussions into consideration: user consistency, topic preference, and user interactions. Our second model focuses on mining interaction features from structured debate posts, and studies how to incorporate such features for viewpoint discovery. Second, we study how to model user opinions for viewpoint discovery. To model user opinions, we leverage the advances in sentiment analysis to extract users opinions in their arguments. Nevertheless, user opinions are sparse in social media and therefore we propose to apply collaborative filtering through matrix factorization to generalize the extracted opinions. Furthermore, we study micro-level and macro-level stance prediction. We propose an integrated model that jointly models arguments, stances, and attributes. Last but not least, we seek to summarize the viewpoints by finding representative posts as one may find the amount of posts holding the same viewpoint is still large. In summary, this dissertation discusses a number of key problems in mining user viewpoints in online discussions and proposes appropriate solutions to these problems. We also discuss other related tasks and point out some future work.

Discovering user demographic attributes from social media is a problem of considerable interest. The problem setting can be generalized to include three components — users, topics and behaviors. In recent studies on this problem, however, the behavior between users and topics are not effectively incorporated. In our work, we proposed an integrated unsupervised model which takes into consideration all the three components integral to the task. Furthermore, our model incorporates collaborative filtering with probabilistic matrix factorization to solve the data sparsity problem, a computational challenge common to all such tasks. We evaluated our method on a case study of user political affiliation identification, and compared against state-of-the-art baselines. Our model achieved an accuracy of 70.1% for user party detection task.

Predicting users political party in social media has important impacts on many real world applications such as targeted advertising, recommendation and personalization. Several political research studies on it indicate that political parties' ideological beliefs on sociopolitical issues may influence the users political leaning. In our work, we exploit users' ideological stances on controversial issues to predict political party of online users. We propose a collaborative filtering approach to solve the data sparsity problem of users stances on ideological topics and apply clustering method to group the users with the same party. We evaluated several state-of-the-art methods for party prediction task on debate.org dataset. The experiments show that using ideological stances with Probabilistic Matrix Factorization (PMF) technique achieves a high accuracy of 88.9% at 22.9% data sparsity rate and 80.5% at 70% data sparsity rate on users' party prediction task.