Social media has emerged to be a popular platform for people to express their viewpoints on political protests like the Arab Spring. Millions of people use social media to communicate and mobilize their viewpoints on protests. Hence, it is a valuable tool for organizing social movements. However, the mechanisms by which protest affects the population is not known, making it difficult to estimate the number of protestors. In this paper, we are inspired by sociological theories of protest participation and propose a framework to predict from the user's past status messages and interactions whether the next post of the user will be a declaration of protest. Drawing concepts from these theories, we model the interplay between the user's status messages and messages interacting with him over time and predict whether the next post of the user will be a declaration of protest. We evaluate the framework using data from the social media platform Twitter on protests during the recent Nigerian elections and demonstrate that it can effectively predict whether the next post of a user is a declaration of protest.
The adoption of hashtags in major social networks including Twitter, Facebook, and Google+ is a strong evidence of its importance in facilitating information diffusion and social chatting. To understand the factors (e.g., user interest, posting time and tweet content) that may affect hashtag annotation in Twitter and to capture the implicit relations between latent topics in tweets and their corresponding hashtags, we propose two PLSA-style topic models to model the hashtag annotation behavior in Twitter. Content-Pivoted Model (CPM) assumes that tweet content guides the generation of hashtags while Hashtag-Pivoted Model (HPM) assumes that hashtags guide the generation of tweet content. Both models jointly incorporate user, time, hashtag and tweet content in a probabilistic framework. The PLSA-style models also enable us to verify the impact of social factor on hashtag annotation by introducing social network regularization in the two models. We evaluate the proposed models using perplexity and demonstrate their effectiveness in two applications: retrospective hashtag annotation and related hashtag discovery. Our results show that HPM outperforms CPM by perplexity and both user and time are important factors that affect model performance. In addition, incorporating social network regularization does not improve model performance. Our experimental results also demonstrate the effectiveness of our models in both applications compared with baseline methods. Topic recommendation can help users deal with the information overload issue in micro-blogging communities. This paper proposes to use the implicit information network formed by the multiple relationships among users, topics and micro-blogs, and the temporal information of micro-blogs to find semantically and temporally relevant topics of each topic, and to profile users' time-drifting topic interests. The Content based, Nearest Neighborhood based and Matrix Factorization models are used to make personalized recommendations. The effectiveness of the proposed approaches is demonstrated in the experiments conducted on a real world dataset that collected from Twitter.com. Social media is gaining popularity as a medium of communication before, during, and after crises. In several recent disasters, it has become evident that social media sites like Twitter and Facebook are an important source of information, and in cases they have even assisted in relief efforts. We propose a novel approach to identify a subset of active users during a crisis who can be tracked for fast access to information. Using a Twitter dataset that consists of 12.9 million tweets from 5 countries that are part of the "Arab Spring" movement, we show how instant information access can be achieved by user identification along two dimensions: user's location and the user's affinity towards topics of discussion. Through evaluations, we demonstrate that users selected by our approach generate more information and the quality of the information is better than that of users identified using state-of-the-art techniques. Social media is being increasingly used to request information and help in situations like natural disasters, where time is a critical commodity. However, generic social media platforms are not explicitly designed for timely information seeking, making it difficult for users to obtain prompt responses. Algorithms to ensure prompt responders for questions in social media have to understand the factors affecting their response time. In this paper, we draw from sociological studies on information seeking and organizational behavior to model the future availability and past response behavior of the candidate responders. We integrate these criteria with their interests to identify users who can provide timely and relevant responses to questions posted in social media. We propose a learning algorithm to derive optimal rankings of responders for a given question. We present questions posted on Twitter as a form of information seeking activity in social media. Our experiments demonstrate that the proposed framework is useful in identifying timely and relevant responders for questions in social media. Since the introduction of microblogging services, there has been a continuous growth of short-text social networking on the Internet. With the generation of large amounts of microposts, there is a need for effective categorization and search of the data. Twitter, one of the largest microblogging sites, allows users to make use of hashtags to categorize their posts. However, the majority of tweets do not contain tags, which hinders the quality of the search results. In this paper, we propose a novel method for unsupervised and content-based hashtag recommendation for tweets. Our approach relies on Latent Dirichlet Allocation (LDA) to model the underlying topic assignment of language classified tweets. The advantage of our approach is the use of a topic distribution to recommend general hashtags. Researchers and social observers have both believed that hashtags, as a new type of organizational objects of information, play a dual role in online microblogging communities (e.g., Twitter). On one hand, a hashtag serves as a bookmark of content, which links tweets with similar topics; on the other hand, a hashtag serves as the symbol of a community membership, which bridges a virtual community of users. Are the real users aware of this dual role of hashtags? Is the dual role affecting their behavior of adopting a hashtag? Is hashtag adoption predictable? We take the initiative to investigate and quantify the effects of the dual role on hashtag adoption. We propose comprehensive measures to quantify the major factors of how a user selects content tags as well as joins communities. Experiments using large scale Twitter datasets prove the effectiveness of the dual role, where both the content measures and the community measures significantly correlate to hashtag adoption on Twitter. With these measures as features, a machine learning model can effectively predict the future adoption of hashtags that a user has never used before. Twitter network is currently overwhelmed by massive amount of tweets generated by its users. To effectively organize and search tweets, users have to depend on appropriate hashtags inserted into tweets. We begin our research on hashtags by first analyzing a Twitter dataset generated by more than 150,000 Singapore users over a three-month period. Among several interesting findings about hashtag usage by this user community, we have found a consistent and significant use of new hashtags on a daily basis. This suggests that most hashtags have very short life span. We further propose a novel hashtag recommendation method based on collaborative filtering and the method recommends hashtags found in the previous month's data. Our method considers both user preferences and tweet content in selecting hashtags to be recommended. Our experiments show that our method yields better performance than recommendation based only on tweet content, even by considering the hashtags adopted by a small number (1 to 3)of users who share similar user preferences.