A Continuous Bursty Topic Discovery from Twitter through Topic Sketch

Online Topic Discovery and Recommendation in Twitter

Users engaged in the Social Web increasingly rely upon continuous streams of Twitter messages (tweets) for real-time access to information and fresh knowledge about current affairs. However, given the deluge of tweets, it is a challenge for individuals to find relevant and appropriately ranked information. We propose to address this knowledge management problem by going beyond the general persp...

Time Series Topic Modeling and Bursty Topic Detection of Correlated News and Twitter

News and twitter are sometimes closely correlated, while sometimes each of them has quite independent flow of information, due to the difference of the concerns of their information sources. In order to effectively capture the nature of those two text streams, it is very important to model both their correlation and their difference. This paper first models their correlation by applying a time ...

A Probabilistic Model for Bursty Topic Discovery in Microblogs

Bursty topics discovery in microblogs is important for people to grasp essential and valuable information. However, the task is challenging since microblog posts are particularly short and noisy. This work develops a novel probabilistic model, namely Bursty Biterm Topic Model (BBTM), to deal with the task. BBTM extends the Biterm Topic Model (BTM) by incorporating the burstiness of biterms as p...

Unsupervised Discovery of Fine-Grained Topic Clusters in Twitter Posts

Controversial Topic Discovery on Members of Congress with Twitter

This paper addresses how Twitter can be used for identifying conflict between communities of users. We aggregate documents by topic and by community and perform sentiment analysis, which allows us to analyze the overall opinion of each community about each topic. We rank the topics with opposing views (negative for one community and positive for the other). For illustration of the proposed meth...