Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8606
Title: Hot topic detection and evaluation of multi-relation effects
Authors: Zirbilek N.E.
Erakın, Mustafa
Özyer T.
Alhajj R.
Keywords: hot topic detection
multi-relations
social media
tweeter
Communication platforms
Daily lives
Hot topic detection
Hot topics
Microblogging
Multi-relation
Product recommendation
Real- time
Social media
Tweeter
Social networking (online)
Issue Date: 2021
Publisher: Association for Computing Machinery, Inc
Source: Zirbilek, N. E., Erakin, M., Özyer, T., & Alhajj, R. (2021, November). Hot topic detection and evaluation of multi-relation effects. In Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 416-422).
Abstract: With the growth of social media, Twitter has become one of the most popularly used microblogging communication platforms between people. Due to the wide preference of Twitter, popular issues in public, events like local or global news and daily life stories can immediately publish on Twitter. Thus, a substantial number of hot topics are created by Twitter users in real-time. These topics can exhibit every incident of everyday life. Therefore, detection of hot topics can be used in many applications such as observing public judgment, product recommendation, and incidence detection. In this paper, we propose a method for detecting Twitter hot topics and evaluate the effect of multi-relations such as retweets and hashtags on hot topics. The dataset was generated by fetching tweets for a certain time and location by using GetOldTweets3 API. Then using the LDA topic modeling algorithm the hot topics were identified for each multi relation. Finally, the effect of each relation is described by using the coherence scores) © 2021 ACM.
Description: ACM Special Interest Group on Knowledge Discovery in Data (SIGKDD);Elsevier;IEEE Computer Society;IEEE TCDE;Springer
13th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021 -- 8 November 2021 -- 176732
URI: https://doi.org/10.1145/3487351.3490972
https://hdl.handle.net/20.500.11851/8606
ISBN: 9781450391283
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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