research-article

Analyzing and predicting viral tweets

Authors:

Maximilian Jenders,

Gjergji Kasneci,

Felix NaumannAuthors Info & Claims

WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web

Pages 657 - 664

https://doi.org/10.1145/2487788.2488017

Published: 13 May 2013 Publication History

Abstract

Twitter and other microblogging services have become indispensable sources of information in today's web. Understanding the main factors that make certain pieces of information spread quickly in these platforms can be decisive for the analysis of opinion formation and many other opinion mining tasks.

This paper addresses important questions concerning the spread of information on Twitter. What makes Twitter users retweet a tweet? Is it possible to predict whether a tweet will become "viral", i.e., will be frequently retweeted? To answer these questions we provide an extensive analysis of a wide range of tweet and user features regarding their influence on the spread of tweets. The most impactful features are chosen to build a learning model that predicts viral tweets with high accuracy. All experiments are performed on a real-world dataset, extracted through a public Twitter API based on user IDs from the TREC 2011 microblog corpus.

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Cited By

Wang ZWang XXiong FChen H(2024)A Survey of Deep Learning-Based Information Cascade PredictionSymmetry10.3390/sym1611143616:11(1436)Online publication date: 29-Oct-2024
https://doi.org/10.3390/sym16111436
Williams LAnthi EBurnap P(2024)Uncovering Key Factors That Drive the Impressions of Online Emerging Technology NarrativesInformation10.3390/info1511070615:11(706)Online publication date: 5-Nov-2024
https://doi.org/10.3390/info15110706
Khanday AWani MRabani SKhan QAbd El-Latif A(2024)HAPI: An efficient Hybrid Feature Engineering-based Approach for Propaganda Identification in social mediaPLOS ONE10.1371/journal.pone.030258319:7(e0302583)Online publication date: 10-Jul-2024
https://doi.org/10.1371/journal.pone.0302583
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Index Terms

Analyzing and predicting viral tweets
1. Information systems
  1. Information systems applications

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Information

Published In

cover image ACM Other conferences

WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web

May 2013

1636 pages

ISBN:9781450320382

DOI:10.1145/2487788

General Chairs:
Daniel Schwabe
PUC-Rio - Brazil
,
Virgílio Almeida
UFMG - Brazil
,
Hartmut Glaser
CGI.br - Brazil
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Labs - Spain & Chile
,
Sue Moon
KAIST - South Korea

Copyright © 2013 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Sponsors

NICBR: Nucleo de Informatcao e Coordenacao do Ponto BR
CGIBR: Comite Gestor da Internet no Brazil

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2013

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CGIBR

WWW '13: 22nd International World Wide Web Conference

May 13 - 17, 2013

Rio de Janeiro, Brazil

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WWW '13 Companion Paper Acceptance Rate 831 of 1,250 submissions, 66%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Citations

Cited By

Wang ZWang XXiong FChen H(2024)A Survey of Deep Learning-Based Information Cascade PredictionSymmetry10.3390/sym1611143616:11(1436)Online publication date: 29-Oct-2024
https://doi.org/10.3390/sym16111436
Williams LAnthi EBurnap P(2024)Uncovering Key Factors That Drive the Impressions of Online Emerging Technology NarrativesInformation10.3390/info1511070615:11(706)Online publication date: 5-Nov-2024
https://doi.org/10.3390/info15110706
Khanday AWani MRabani SKhan QAbd El-Latif A(2024)HAPI: An efficient Hybrid Feature Engineering-based Approach for Propaganda Identification in social mediaPLOS ONE10.1371/journal.pone.030258319:7(e0302583)Online publication date: 10-Jul-2024
https://doi.org/10.1371/journal.pone.0302583
Chaudhary PKiran PSharma S(2024)Hashtag and Marketing Campaign on Twitter: From the Spectrum of Smartphone Industry PerspectiveScience, Engineering Management and Information Technology10.1007/978-3-031-72284-4_20(323-342)Online publication date: 12-Sep-2024
https://doi.org/10.1007/978-3-031-72284-4_20
Wu YMcAreavey KLiu WMcConville R(2024)A Comparative Analysis of Information Cascade Prediction Using Dynamic Heterogeneous and Homogeneous GraphsComplex Networks & Their Applications XII10.1007/978-3-031-53503-1_14(168-179)Online publication date: 29-Feb-2024
https://doi.org/10.1007/978-3-031-53503-1_14
Thakur NPatel KPoon AShah RAzizi NHan C(2023)A Comprehensive Analysis and Investigation of the Public Discourse on Twitter about Exoskeletons from 2017 to 2023Future Internet10.3390/fi1510034615:10(346)Online publication date: 22-Oct-2023
https://doi.org/10.3390/fi15100346
Zhou KWilson TStarbird KSpiro E(2023)Spotlight Tweets: A Lens for Exploring Attention Dynamics within Online Sensemaking During Crisis EventsACM Transactions on Social Computing10.1145/35772136:1-2(1-33)Online publication date: 26-Jun-2023
https://dl.acm.org/doi/10.1145/3577213
Nayak AGarg MDuvvuru Muni RChen HDuh WHuang HKato MMothe JPoblete B(2023)News Popularity Beyond the Click-Through-Rate for Personalized RecommendationsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591741(1396-1405)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591741
Yang CWang HTang JShi CSun MCui GLiu Z(2023)Full-Scale Information Diffusion Prediction With Reinforced Recurrent NetworksIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.310615634:5(2271-2283)Online publication date: May-2023
https://doi.org/10.1109/TNNLS.2021.3106156
Xu XZhou FZhang KLiu STrajcevski G(2023)CasFlow: Exploring Hierarchical Structures and Propagation Uncertainty for Cascade PredictionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.312647535:4(3484-3499)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TKDE.2021.3126475
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