Real Time Event Detection in Twitter

Event detection has been an important task for a long time. When it comes to Twitter, new problems are presented. Twitter data is a huge temporal data flow with much noise and various kinds of topics. Traditional sophisticated methods with a high computational complexity aren't designed to handle such data flow efficiently. In this paper, we propose a mixture Gaussian model for bursty word extraction in Twitter and then employ a novel time-dependent HDP model for new topic detection. Our model can grasp new events, the location and the time an event becomes bursty promptly and accurately. Experiments show the effectiveness of our model in real time event detection in Twitter. ? 2013 Springer-Verlag Berlin Heidelberg.EI

Exploiting Language Models to Classify Events from Twitter

Classifying events is challenging in Twitter because tweets texts have a large amount of temporal data with a lot of noise and various kinds of topics. In this paper, we propose a method to classify events from Twitter. We firstly find the distinguishing terms between tweets in events and measure their similarities with learning language models such as ConceptNet and a latent Dirichlet allocation method for selectional preferences (LDA-SP), which have been widely studied based on large text corpora within computational linguistic relations. The relationship of term words in tweets will be discovered by checking them under each model. We then proposed a method to compute the similarity between tweets based on tweets' features including common term words and relationships among their distinguishing term words. It will be explicit and convenient for applying to k-nearest neighbor techniques for classification. We carefully applied experiments on the Edinburgh Twitter Corpus to show that our method achieves competitive results for classifying events

Concept-Based Visual Analysis of Dynamic Textual Data

Analyzing how interrelated ideas flow within and between multiple social groups helps understand the propagation of information, ideas, and thoughts on social media. The existing dynamic text analysis work on idea flow analysis is mostly based on the topic model. Therefore, when analyzing the reasons behind the flow of ideas, people have to check the textual data of the ideas, which is annoying because of the huge amount and complex structures of these texts. To solve this problem, we propose a concept-based dynamic visual text analytics method, which illustrates how the content of the ideas change and helps users analyze the root cause of the idea flow. We use concepts to summarize the content of the ideas and show the flow of concepts with the flow lines. To ensure the stability of the flow lines, a constrained t-SNE projection algorithm is used to display the change of concepts over time and the correlation between them. In order to better convey the anomalous change of the concepts, we propose a method to detect the time periods with anomalous change of concepts based on anomaly detection and highlight them. A qualitative evaluation and a case study on real-world Twitter datasets demonstrate the correctness and effectiveness of our visual analytics method.Comment: in Chinese languag

Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p