TY - GEN
T1 - Characteristics of Similar-Context Trending Hashtags in Twitter
T2 - 27th International Conference on Web Services, ICWS 2020, held as part of the Services Conference Federation, SCF 2020
AU - Alothali, Eiman
AU - Hayawi, Kadhim
AU - Alashwal, Hany
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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%.
AB - 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%.
KW - Context
KW - Frequency
KW - Spatiotemporal
KW - Trend
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85092197395&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85092197395&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-59618-7_10
DO - 10.1007/978-3-030-59618-7_10
M3 - Conference contribution
AN - SCOPUS:85092197395
SN - 9783030596170
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 150
EP - 163
BT - Web Services – ICWS 2020 - 27th International Conference, Held as Part of the Services Conference Federation, SCF 2020, Proceedings
A2 - Ku, Wei-Shinn
A2 - Kanemasa, Yasuhiko
A2 - Serhani, Mohamed Adel
A2 - Zhang, Liang-Jie
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 18 September 2020 through 20 September 2020
ER -