research-article

Questions about questions: an empirical analysis of information needs on Twitter

Authors:

Qiaozhu MeiAuthors Info & Claims

WWW '13: Proceedings of the 22nd international conference on World Wide Web

Pages 1545 - 1556

https://doi.org/10.1145/2488388.2488523

Published: 13 May 2013 Publication History

Abstract

Conventional studies of online information seeking behavior usually focus on the use of search engines or question answering (Q&A) websites. Recently, the fast growth of online social platforms such as Twitter and Facebook has made it possible for people to utilize them for information seeking by asking questions to their friends or followers. We anticipate a better understanding of Web users' information needs by investigating research questions about these questions. How are they distinctive from daily tweeted conversations? How are they related to search queries? Can users' information needs on one platform predict those on the other?

In this study, we take the initiative to extract and analyze information needs from billions of online conversations collected from Twitter. With an automatic text classifier, we can accurately detect real questions in tweets (i.e., tweets conveying real information needs). We then present a comprehensive analysis of the large-scale collection of information needs we extracted. We found that questions being asked on Twitter are substantially different from the topics being tweeted in general. Information needs detected on Twitter have a considerable power of predicting the trends of Google queries. Many interesting signals emerge through longitudinal analysis of the volume, spikes, and entropy of questions on Twitter, which provide insights to the understanding of the impact of real world events and user behavioral patterns in social platforms.

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Sevastjanova RJentner WSperrle FKehlbeck RBernard JEl-assady M(2021)QuestionComb: A Gamification Approach for the Visual Explanation of Linguistic Phenomena through Interactive LabelingACM Transactions on Interactive Intelligent Systems10.1145/342944811:3-4(1-38)Online publication date: 3-Sep-2021
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Index Terms

Questions about questions: an empirical analysis of information needs on Twitter
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Other conferences

WWW '13: Proceedings of the 22nd international conference on World Wide Web

May 2013

1628 pages

ISBN:9781450320351

DOI:10.1145/2488388

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).

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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

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Published: 13 May 2013

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WWW '13: 22nd International World Wide Web Conference

May 13 - 17, 2013

Rio de Janeiro, Brazil

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WWW '13 Paper Acceptance Rate 125 of 831 submissions, 15%;

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

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Cited By

Su YSun YZhang MWang J(2024)Vexless: A Serverless Vector Data Management System Using Cloud FunctionsProceedings of the ACM on Management of Data10.1145/36549902:3(1-26)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654990
Symeonidis SPeikos GArampatzis A(2022)Unsupervised consumer intention and sentiment mining from microblogging data as a business intelligence toolOperational Research10.1007/s12351-022-00714-022:5(6007-6036)Online publication date: 12-May-2022
https://doi.org/10.1007/s12351-022-00714-0
Sevastjanova RJentner WSperrle FKehlbeck RBernard JEl-assady M(2021)QuestionComb: A Gamification Approach for the Visual Explanation of Linguistic Phenomena through Interactive LabelingACM Transactions on Interactive Intelligent Systems10.1145/342944811:3-4(1-38)Online publication date: 3-Sep-2021
https://dl.acm.org/doi/10.1145/3429448
Herrera JParra DPoblete B(2020)Social QA in non-CQA platformsFuture Generation Computer Systems10.1016/j.future.2019.12.023105:C(631-649)Online publication date: 1-Apr-2020
https://dl.acm.org/doi/10.1016/j.future.2019.12.023
Tolmie PProcter RRouncefield MLiakata MZubiaga A(2018)Microblog Analysis as a Program of WorkACM Transactions on Social Computing10.1145/31629561:1(1-40)Online publication date: 18-Jan-2018
https://dl.acm.org/doi/10.1145/3162956
Ranganath SHu XTang JWang SLiu H(2018)Understanding and Identifying Rhetorical Questions in Social MediaACM Transactions on Intelligent Systems and Technology10.1145/31083649:2(1-22)Online publication date: 10-Jan-2018
https://dl.acm.org/doi/10.1145/3108364
Gkanatsios NMermikli KKatsikas S(2018)Metis: A Scalable Natural-Language-Based Intelligent Personal Assistant for Maritime ServicesInformation and Software Technologies10.1007/978-3-319-99972-2_2(14-28)Online publication date: 29-Aug-2018
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Santini MStrandqvist WNyström MAlirezai MJönsson A(2018)Can We Quantify Domainhood? Exploring Measures to Assess Domain-Specificity in Web CorporaDatabase and Expert Systems Applications10.1007/978-3-319-99133-7_17(207-217)Online publication date: 7-Aug-2018
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Herrera JPoblete BParra D(2018)Learning to Leverage Microblog Information for QA RetrievalAdvances in Information Retrieval10.1007/978-3-319-76941-7_38(507-520)Online publication date: 1-Mar-2018
https://doi.org/10.1007/978-3-319-76941-7_38
Yamamoto SKando NSatoh T(2017)Life aspect inference of tweets based on probability distributionWeb Intelligence10.3233/WEB-17035215:1(55-65)Online publication date: 21-Feb-2017
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