research-article

Social Media-Based Collaborative Information Access: Analysis of Online Crisis-Related Twitter Conversations

Authors:

Lamjed Ben Jabeur,

Frederic Amblard,

Chihab Hanachi,

Camille RothAuthors Info & Claims

HT '16: Proceedings of the 27th ACM Conference on Hypertext and Social Media

Pages 159 - 168

https://doi.org/10.1145/2914586.2914589

Published: 10 July 2016 Publication History

Abstract

The notion of implicit (or explicit) collaborative information access refers to systems and practices allowing a group of users to unintentionally (respectively intentionally) seek, share and retrieve information to achieve similar (respectively shared) information-related goals. Despite an increasing adoption in social environments, collaboration behavior in information seeking and retrieval is mainly limited to small-sized groups, generally restricted to working spaces. Much remains to be learned about collaborative information seeking within open web social spaces. This paper is an attempt to better understand either implicit or explicit collaboration by studying Twitter, one of the most popular and widely used social networks. We study in particular the complex intertwinement of human interactions induced by both collaboration and social networking. We empirically explore explicit collaborative interactions based on focused conversation streams during two crisis. We identify structural patterns of temporally representative conversation subgraphs and represent their topics using Latent Dirichlet Allocation (LDA) modeling. Our main findings suggest that: 1) the critical mass of collaboration is generally limited to small-sized flat networks, with or without an influential user, 2) users are active as members of weakly overlapping groups and engage in numerous collaborative search and sharing tasks dealing with different topics, and 3) collaborative group ties evolve within the time-span of conversations.

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Cited By

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https://doi.org/10.1007/978-3-031-30399-9_8
Ramya TChristaline J(2022)A Comparative Study on the Identification of Informative Tweets Using Deep Neural Networks During CrisisElectronic Systems and Intelligent Computing10.1007/978-981-16-9488-2_66(697-706)Online publication date: 3-Jun-2022
https://doi.org/10.1007/978-981-16-9488-2_66
Lotfi SMirzarezaee MHosseinzadeh mSeydi V(2021)Analysis of Structural Features in Rumor Conversations Detection in TwitterSignal and Data Processing10.52547/jsdp.18.3.4518:3(45-64)Online publication date: 1-Dec-2021
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Index Terms

Social Media-Based Collaborative Information Access: Analysis of Online Crisis-Related Twitter Conversations
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing systems and tools
      1. Social networking sites

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cover image ACM Conferences

HT '16: Proceedings of the 27th ACM Conference on Hypertext and Social Media

July 2016

354 pages

ISBN:9781450342476

DOI:10.1145/2914586

General Chairs:
Jamie Blustein
Dalhousie University, Canada
,
Eelco Herder
L3S Research Center, Germany
,
Program Chairs:
Jessica Rubart
OWL University of Applied Sciences, Germany
,
Helen Ashman
University of South Australia

Copyright © 2016 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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New York, NY, United States

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Published: 10 July 2016

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HT '16: 27th ACM Conference on Hypertext and Social Media

July 10 - 13, 2016

Nova Scotia, Halifax, Canada

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HT '16 Paper Acceptance Rate 16 of 54 submissions, 30%;

Overall Acceptance Rate 378 of 1,158 submissions, 33%

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https://doi.org/10.1007/978-3-031-30399-9_8
Ramya TChristaline J(2022)A Comparative Study on the Identification of Informative Tweets Using Deep Neural Networks During CrisisElectronic Systems and Intelligent Computing10.1007/978-981-16-9488-2_66(697-706)Online publication date: 3-Jun-2022
https://doi.org/10.1007/978-981-16-9488-2_66
Lotfi SMirzarezaee MHosseinzadeh mSeydi V(2021)Analysis of Structural Features in Rumor Conversations Detection in TwitterSignal and Data Processing10.52547/jsdp.18.3.4518:3(45-64)Online publication date: 1-Dec-2021
https://doi.org/10.52547/jsdp.18.3.45
Khatri D(2021)Use of social media information sources: a systematic literature reviewOnline Information Review10.1108/OIR-04-2020-015245:6(1039-1063)Online publication date: 12-Mar-2021
https://doi.org/10.1108/OIR-04-2020-0152
Hoang TMothe J(2021)Prediction of brand stories spreading on social networksAdvances in Data Analysis and Classification10.1007/s11634-021-00450-x16:3(559-591)Online publication date: 18-Jun-2021
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