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In order to overcome the shortcoming of the static threshold and the problem of the false reports, this paper put forward double filtering based on dynamic threshold and named entity of topic tracking. This method used the KL divergence to obtain the initial threshold and the candidate reports. Then, according to the time characteristics of reports s it studied dynamic threshold method. Finally, it extracted named entities of the reports to calculate similarity of named entities and the same number of named entities, which completed topic tracking. The experiment proves the feasibility of the algorithm and improves the efficiency of the topic tracking to a certain extent. In addition, it reduces the rate of false positives and non-response rates. [ABSTRACT FROM AUTHOR]

In hot topics found on micro-blog, because the text of micro-blog is short and less words, and the terms are not standard, so the traditional hot topic detection method can not find hot topics effectively. In order to solve this problem, this paper presented a method of hot topics found based on speed growth. Firstly, it divided the pretreated micro-blogs on the basis of the equal number of window, and added up the term frequency in each window, and expressed as feature trajectory of binary group sequence. Secondly, it calculated the growth slope of every adjacent two windows to find the words with growth speed. Thirdly, it calculated the growth speed of the word's relevant users and the growth speed of the word's relevant micro-blogs to ensure the word was hot subject or not. Finally, it found hot topics through the hot subject clustering. The experimental proves the feasibility of the algorithm, results show that the method improves the efficiency of the detection to a certain extent, and reduces the undetected rate and false detection rate, it can effectively discover hot topics on micro-blog in time. [ABSTRACT FROM AUTHOR]