Characteristics of Similar-Context Trending Hashtags in Twitter: A Case Study

Eiman Alothali, Kadhim Hayawi, Hany Alashwal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Twitter is a popular social networking platform that is widely used in discussing and spreading information on global events. Twitter trending hashtags have been one of the topics for researcher to study and analyze. Understanding the posting behavior patterns as the information flows increase by rapid events can help in predicting future events or detection manipulation. In this paper, we investigate similar-context trending hashtags to characterize general behavior of specific-trend and generic-trend within same context. We demonstrate an analysis to study and compare such trends based on spatial, temporal, content, and user activity. We found that the characteristics of similar-context trends can be used to predict future generic trends with analogous spatiotemporal, content, and user features. Our results show that more than 70% users participate in location-based hashtag belongs to the location of the hashtag. Generic trends aim to have more influence in users to participate than specific trends with geographical context. The retweet ratio in specific trends is higher than generic trends with more than 79%.

Original languageEnglish
Title of host publicationWeb Services – ICWS 2020 - 27th International Conference, Held as Part of the Services Conference Federation, SCF 2020, Proceedings
EditorsWei-Shinn Ku, Yasuhiko Kanemasa, Mohamed Adel Serhani, Liang-Jie Zhang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages150-163
Number of pages14
ISBN (Print)9783030596170
DOIs
Publication statusPublished - 2020
Event27th International Conference on Web Services, ICWS 2020, held as part of the Services Conference Federation, SCF 2020 - Honolulu, United States
Duration: Sep 18 2020Sep 20 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12406 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference27th International Conference on Web Services, ICWS 2020, held as part of the Services Conference Federation, SCF 2020
Country/TerritoryUnited States
CityHonolulu
Period9/18/209/20/20

Keywords

  • Context
  • Frequency
  • Spatiotemporal
  • Trend
  • Twitter

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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