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predefined keywords are taken into account, we introduce a lifecycle for each keyword to be observed, expressing their average behavior (e.g. average frequency changes) over time. As a motivation, we show that some keywords exhibit periodic behavior that can be handled by our model. The proposed lifecycle model enables us to
number of keywords for the searches of the events, especially in relation with analytics and searches on SNs for the reflectance of those events. A special attention is given to synonyms.
sense information in the real world and mention them online. The web interfaces of these networks often support features such as keyword search that allow an user to quickly find entities of interest. While these interfaces are adequate for regular users, they are often too restrictive to answer complex queries such as (1
target of interest specified by the user from Twitter regardless of their popularity. Assuming that the related observations are likely to contain words people often associate with each other, the associative relations among words are learned from the past messages. When a user gives a keyword representing his/her current
) based method to determine informative tweet and the real-time event detection algorithm to detect the timely occurrence of the given event. In this study, CNN model trained from the tweets related to the earthquake in the past labeled by crowdsourcing plays a role as the classifier to predict an earthquake keyword related
the average Tweet frequency of keywords per day in and around a potential event area and use these estimations to classify whether the keywords are related to a local event. The proposed scheme achieves a precision rate of 68% which is a significant improvement compared to related work that states a precision rate of
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
volunteers gain popularity and credibility, they become emerging leaders. This study proposes a method to detect emerging leaders on Twitter after a catastrophe. It consisted of collecting 4M of tweets using keywords related to Ecuador's earthquake on April 2016. Secondly, we quantifying relevant interactions in order to
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
tweets based on features such as the keywords in a tweet, the number of words, and their context. Subsequently, we produce a probabilistic spatiotemporal model for the target event that can find the center of the event location. We regard each Twitter user as a sensor and apply particle filtering, which are widely used for
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