Abstract
In addition to knowledge enhancement, recreation and providing chat, development of Social Network Sites leads to big data, such data can be of great value, as it shows the tendency of the members based on geographical zone, language and culture. The data can also be useful for content oriented planning. In addition, special events of society can be discovered and classified using these data. In some cases, the events have previously existed in society and are considered to be repetitive, like flood in Indian or Typhoon in Florida state, and sometimes the events are unprecedented in which cases, the new event is classified under a new class. The high cost for computations associated with event detection in real time is considered as a major challenge encountered in this context. In the present paper, a model is presented based on deep learning. In the first phase, the first class is trained based on labeled data, then unlabeled data are introduced to the model in a flow manner, and are classified into current classes based on the model through which they have been trained. The data which are higher than a specified threshold are classified into a new class, and if they are lower than the threshold, they are classified as temporary event. At the end, the effectiveness of the model will be evaluated through an available corpus as a benchmark data set. A significant improvement is shown in recall and precision over five state-of-the-art baselines.
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Rezaei, Z., Komleh, H.E., Eslami, B. (2019). Event Detection in Twitter Big Data by Virtual Backbone Deep Learning. In: Grandinetti, L., Mirtaheri, S., Shahbazian, R. (eds) High-Performance Computing and Big Data Analysis. TopHPC 2019. Communications in Computer and Information Science, vol 891. Springer, Cham. https://doi.org/10.1007/978-3-030-33495-6_2
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DOI: https://doi.org/10.1007/978-3-030-33495-6_2
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