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Event prediction in social network through Twitter messages analysis

Event prediction in social network through Twitter messages analysis Event detection using social media analysis has attracted researchers’ attention. Prediction of events especially in the management of social crises can be of particular significance. In this study, events are predicted through analyzing Twitter messages and examining the changes in the rate of Tweets in a specified subject. In the proposed method, the Tweets are initially preprocessed in consecutive fixed-length time windows. Tweets are then categorized using the non-negative matrix factorization analysis and the distance dependent Chinese restaurant process incremental clustering. The categorization results show that a high rate of Tweets entering a cluster represents the occurrence of a new event in near future. Finally, a description of the event is presented in the form of some frequent words in each cluster. In this paper, investigations on a Tweet dataset during a 6-month period indicate that the rate of sending Tweets about predictable events considerably changes before their occurrence. The use of this feature can make it possible to predict events with high degrees of precision. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Social Network Analysis and Mining Springer Journals

Event prediction in social network through Twitter messages analysis

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Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2022
ISSN
1869-5450
eISSN
1869-5469
DOI
10.1007/s13278-022-00911-x
Publisher site
See Article on Publisher Site

Abstract

Event detection using social media analysis has attracted researchers’ attention. Prediction of events especially in the management of social crises can be of particular significance. In this study, events are predicted through analyzing Twitter messages and examining the changes in the rate of Tweets in a specified subject. In the proposed method, the Tweets are initially preprocessed in consecutive fixed-length time windows. Tweets are then categorized using the non-negative matrix factorization analysis and the distance dependent Chinese restaurant process incremental clustering. The categorization results show that a high rate of Tweets entering a cluster represents the occurrence of a new event in near future. Finally, a description of the event is presented in the form of some frequent words in each cluster. In this paper, investigations on a Tweet dataset during a 6-month period indicate that the rate of sending Tweets about predictable events considerably changes before their occurrence. The use of this feature can make it possible to predict events with high degrees of precision.

Journal

Social Network Analysis and MiningSpringer Journals

Published: Dec 1, 2022

Keywords: Social network analysis; Event detection; Event prediction; Twitter; Incremental clustering

References