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Burst detection is one of the most popular techniques for extracting remarkable keywords in online social documents posted through social media. With the growing interest in geosocial media these days, many researchers are focusing on extracting geolocal keywords related to local topics and events from such social
empirical rules, then, burst detection algorithm is adopted to discover peak interval of all phases, finally, we use a summarization technique TextRank to extract keywords from contents to summarize the topics in each phase. In addition, we perform experiments on two real-world datasets collected from different social media
Twitter). In our previous work, we developed a method for identifying local temporal burstiness to detect local hot keywords considering the users' location. The previous method is based on Kleinberg's temporal burst detection algorithm, which presupposes that the rate of posting remains constant. However, this leads to a
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