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analysis platform to detect the global topics. Our framework targets various countries' social media and extracts keywords from messages written by different languages. Then the framework translates keywords from a local language to English, so that we can understand meanings of keywords. Since our framework is based on
and unstructured, resulting to unsatisfactory classification performance of conventional learning-based approaches. Thus, we propose a simple yet effective algorithm to identify relevant messages based on matching keywords and hashtags, and provide a comparison between matching-based and learning-based approaches. To
described in this paper, we investigate the real-time interaction of events such as earthquakes in Twitter, Facebook and other social media, and propose an algorithm to monitor tweets and to detect a target event. We devised a filter of data based on features such as the keywords, the number of times they are present, and
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
Twitter is a user-friendly social network which deserves its real-time nature. With the help of an algorithm, the investigation can be made with regard to some of the real-time events such as earthquake. The target event is assumed and classified based on the keywords, number of words and their context. The
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