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Social networks like Twitter and Facebook have gained a significant popularity with people from all parts of the society in the past decade, providing a new kind of data source for novel social-aware applications. A great majority of the users are online all the time, posting real-time information on various topics
) 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
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.
An online community network such as Twitter, Yelp or amazon.com links entities (e.g., Users, products) with various relationships (e.g., Friendship, co-purchase, co-review) and make such information available for access through a web interface. Often, these community networks act as "social sensors" in which users
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
A large number of real-world observations by social sensors all over the world can be obtained from various social networking services. Especially, observations covering miscellaneous areas of interest are posted to Twitter as short text messages. Our goal is to extract a wide range of observations related to the
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
Burstiness has been one of the most important criteria for extracting topics and events from documents posted on social media. Recently, researchers are focusing on extracting geolocal topics and events from such social documents because of the increasing number of geo-annotated documents (e.g., Geo-tagged tweets on
Social media such as tweets are emerging as platforms contributing to situational awareness during disasters. Information shared on Twitter by both affected population (e.g., requesting assistance, warning) and those outside the impact zone (e.g., providing assistance) would help first responders, decision makers, and
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
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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