research-article

Spatio-temporal and events based analysis of topic popularity in twitter

Authors:

Sebastien Ardon,

Amitabha Bagchi,

Anirban Mahanti,

Aaditeshwar Seth,

Rudra Mohan Tripathy,

Sipat TriukoseAuthors Info & Claims

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

Pages 219 - 228

https://doi.org/10.1145/2505515.2505525

Published: 27 October 2013 Publication History

Abstract

We present the first comprehensive characterization of the diffusion of ideas on Twitter, studying more than 5.96 million topics that include both popular and less popular topics. On a data set containing approximately 10 million users and a comprehensive scraping of 196 million tweets, we perform a rigorous temporal and spatial analysis, investigating the time-evolving properties of the subgraphs formed by the users discussing each topic. We focus on two different notions of the spatial: the network topology formed by follower-following links on Twitter, and the geospatial location of the users. We investigate the effect of initiators on the popularity of topics and find that users with a high number of followers have a strong impact on topic popularity. We deduce that topics become popular when disjoint clusters of users discussing them begin to merge and form one giant component that grows to cover a significant fraction of the network. Our geospatial analysis shows that highly popular topics are those that cross regional boundaries aggressively.

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Cited By

Kamal RBagchi A(2024)A Lovász-Simonovits Theorem for Hypergraphs with Application to Local ClusteringProceedings of the ACM on Management of Data10.1145/36771262:4(1-27)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3677126
Pattanayak PTripathy RPadhy S(2024)A semi-supervised approach of short text topic modeling using embedded fuzzy clustering for Twitter hashtag recommendationDiscover Sustainability10.1007/s43621-024-00218-15:1Online publication date: 4-Apr-2024
https://doi.org/10.1007/s43621-024-00218-1
Pattanayak PTripathy RPadhy S(2024)A Semi-supervised Approach of Cluster-Based Topic Modeling for Effective Tweet Hashtag RecommendationSN Computer Science10.1007/s42979-024-03299-x5:7Online publication date: 10-Oct-2024
https://dl.acm.org/doi/10.1007/s42979-024-03299-x
Show More Cited By

Index Terms

Spatio-temporal and events based analysis of topic popularity in twitter
1. Information systems
  1. Information systems applications
    1. Data mining
  2. World Wide Web
    1. Web applications
    2. Web services

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Published In

cover image ACM Conferences

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

October 2013

2612 pages

ISBN:9781450322638

DOI:10.1145/2505515

General Chairs:
Qi He
LinkedIn, USA
,
Arun Iyengar
IBM T.J. Watson Research Center, USA
,
Program Chairs:
Wolfgang Nejdl
L3S Research Center, Germany
,
Jian Pei
Simon Fraser University, Canada
,
Rajeev Rastogi
Amazon, India

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 27 October 2013

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CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 27 - November 1, 2013

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CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Kamal RBagchi A(2024)A Lovász-Simonovits Theorem for Hypergraphs with Application to Local ClusteringProceedings of the ACM on Management of Data10.1145/36771262:4(1-27)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3677126
Pattanayak PTripathy RPadhy S(2024)A semi-supervised approach of short text topic modeling using embedded fuzzy clustering for Twitter hashtag recommendationDiscover Sustainability10.1007/s43621-024-00218-15:1Online publication date: 4-Apr-2024
https://doi.org/10.1007/s43621-024-00218-1
Pattanayak PTripathy RPadhy S(2024)A Semi-supervised Approach of Cluster-Based Topic Modeling for Effective Tweet Hashtag RecommendationSN Computer Science10.1007/s42979-024-03299-x5:7Online publication date: 10-Oct-2024
https://dl.acm.org/doi/10.1007/s42979-024-03299-x
Boongasame L(2022)A Survey on COVID-19 Fake News Detection on TwitterCybersecurity Crisis Management and Lessons Learned From the COVID-19 Pandemic10.4018/978-1-7998-9164-2.ch010(218-243)Online publication date: 2022
https://doi.org/10.4018/978-1-7998-9164-2.ch010
Prado-Romero MCoto-Santiesteban ACeli AStilo G(2020)A time-sensitive model to predict topic popularity in news providersIntelligent Data Analysis10.3233/IDA-20001224:S1(123-140)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.3233/IDA-200012
Magdy A(2020)MicroblogsSIGSPATIAL Special10.1145/3404820.340482712:1(41-52)Online publication date: 8-Jul-2020
https://dl.acm.org/doi/10.1145/3404820.3404827
Panda AChakraborty SRaval NZhang HMohapatra MAkbar SPal JMaitland CCabezas M(2020)Affording ExtremesProceedings of the 2020 International Conference on Information and Communication Technologies and Development10.1145/3392561.3394637(1-12)Online publication date: 17-Jun-2020
https://dl.acm.org/doi/10.1145/3392561.3394637
Wang YSiriaraya PMittal MXie HKawai Y(2020)Understanding Multilingual Correlation of Geo-Tagged Tweets for POI RecommendationWeb and Wireless Geographical Information Systems10.1007/978-3-030-60952-8_14(135-144)Online publication date: 13-Nov-2020
https://dl.acm.org/doi/10.1007/978-3-030-60952-8_14
Wu LLi JQi J(2019)Characterizing popularity dynamics of hot topics using micro-blogs spatio-temporal dataJournal of Big Data10.1186/s40537-019-0266-46:1Online publication date: 16-Nov-2019
https://doi.org/10.1186/s40537-019-0266-4
Avetisyan ADrobyshevskiy MTurdakov DGhukasyan T(2019)Methods for Information Diffusion AnalysisProgramming and Computing Software10.1134/S036176881907003X45:7(372-380)Online publication date: 1-Dec-2019
https://dl.acm.org/doi/10.1134/S036176881907003X
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