A modified approach of hot topics found on micro-blog

Abstract

Due to the simplicity, immediacy and convenience, micro-blog is gaining more and more attention from all kinds of people, especially the researchers. Recently, topic detection on micro-blog has attracted more interests due largely to the rapid development of micro-blog. However, retrieving information from micro-blog is challenging, as the texts of the micro-blog are short, ungrammatical, and unstructured, and they are full of noise. Therefore, the traditional hot topic detection method performed less. In order to solve this problem, this paper proposed a method of hot topics found based on speed growth. In this method, the pretreated micro-blogs were divided into different windows, and the time information was extracted in each window; then, for each word, it was expressed as feature trajectory of binary group sequence; then, calculated the growth speed of the word and the users relevant to the word in every adjacent two windows, selected the words whose growth speed is greater than a certain threshold as hot keywords; then, hot topics were found through the hot keywords clustering. The experiment was done based on SINA micro-blog dataset, the miss rate and false detection rate were done to prove the feasibility of the algorithm, results showed that the method improved the efficiency of the detection to a certain extent. © Springer Science+Business Media Dordrecht 2014.