Title

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

Document Type

Conference Proceeding

Source of Publication

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Publication Date

1-1-2020

Abstract

© 2020, Springer Nature Switzerland AG. 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%.

ISBN

9783030596170

ISSN

0302-9743

Publisher

Springer Science and Business Media Deutschland GmbH

Volume

12406 LNCS

First Page

150

Last Page

163

Disciplines

Computer Sciences | Social and Behavioral Sciences

Keywords

Context, Frequency, Spatiotemporal, Trend, Twitter

Scopus ID

85092197395

Indexed in Scopus

yes

Open Access

no

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