Abstract
Social mediating technologies have engendered radically new ways of information and communication, particularly during events; in case of natural disaster like earthquakes tsunami and American presidential election. The growing complexity of these social mediating technologies in terms of size, number of users, and variety of bloggers relationships have generated a big data which requires innovative approaches in order to analyse, extract and detect non-obvious and popular events. This paper is based on data obtained from Twitter because of its popularity and sheer data volume. This content can be combined and processed to detect events, entities and popular moods to feed various new large-scale data-analysis applications. On the downside, these content items are very noisy and highly informal, making it difficult to extract sense out of the stream. Taking to account all the difficulties, we propose a new event detection approach combining linguistic features and Twitter features. Finally, we present our event detection system from microblogs that aims (1) detect new events, (2) to recognize temporal markers pattern of an event, (3) and to classify important events according to thematic pertinence, author pertinence and tweet volume.
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Cherichi, S., Faiz, R. (2016). Upgrading Event and Pattern Detection to Big Data. In: Nguyen, N., Iliadis, L., Manolopoulos, Y., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2016. Lecture Notes in Computer Science(), vol 9876. Springer, Cham. https://doi.org/10.1007/978-3-319-45246-3_36
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DOI: https://doi.org/10.1007/978-3-319-45246-3_36
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