research-article

Deep Twitter diving: exploring topical groups in microblogs at scale

Authors:

Parantapa Bhattacharya,

Saptarshi Ghosh,

Juhi Kulshrestha,

Mainack Mondal,

Muhammad Bilal Zafar,

Krishna P. GummadiAuthors Info & Claims

CSCW '14: Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing

Pages 197 - 210

https://doi.org/10.1145/2531602.2531636

Published: 15 February 2014 Publication History

Abstract

We present a semantic methodology to identify topical groups in Twitter on a large number of topics, each consisting of users who are experts on or interested in a specific topic. Early studies investigating the nature of Twitter suggest that it is a social media platform consisting of a relatively small section of elite users, producing information on a few popular topics such as media, politics, and music, and the general population consuming it. We show that this characterization ignores a rich set of highly specialized topics, ranging from geology, neurology, to astrophysics and karate - each being discussed by their own topical groups. We present a detailed characterization of these topical groups based on their network structures and tweeting behaviors. Analyzing these groups on the backdrop of the common identity and bond theory in social sciences shows that these groups exhibit characteristics of topical-identity based groups, rather than social-bond based ones.

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Cited By

Ding JLiu ZXiao SChen YLi YJin DUhlig S(2019)Beyond the Click: A First Look at the Role of a Microblogging Platform in the Web EcosystemIEEE Transactions on Network and Service Management10.1109/TNSM.2018.287781916:2(743-754)Online publication date: Jun-2019
https://doi.org/10.1109/TNSM.2018.2877819
Han ZLi SCui CHan DSong H(2019)Geosocial Media as a Proxy for Security: A ReviewIEEE Access10.1109/ACCESS.2019.29491157(154224-154238)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2949115
Kulshrestha JEslami MMessias JZafar MGhosh SGummadi KKarahalios K(2019)Search bias quantificationInformation Retrieval10.1007/s10791-018-9341-222:1-2(188-227)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s10791-018-9341-2
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Deep Twitter diving: exploring topical groups in microblogs at scale

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cover image ACM Conferences

CSCW '14: Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing

February 2014

1600 pages

ISBN:9781450325400

DOI:10.1145/2531602

General Chairs:
Susan Fussell
Cornell University
,
Wayne Lutters
University of Maryland, Baltimore County
,
Program Chairs:
Meredith Ringel Morris
Microsoft Research
,
Madhu Reddy
Penn State University

Copyright © 2014 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 the author(s) 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: 15 February 2014

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February 15 - 19, 2014

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CSCW '14 Paper Acceptance Rate 134 of 497 submissions, 27%;

Overall Acceptance Rate 2,235 of 8,521 submissions, 26%

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Cited By

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https://doi.org/10.1109/TNSM.2018.2877819
Han ZLi SCui CHan DSong H(2019)Geosocial Media as a Proxy for Security: A ReviewIEEE Access10.1109/ACCESS.2019.29491157(154224-154238)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2949115
Kulshrestha JEslami MMessias JZafar MGhosh SGummadi KKarahalios K(2019)Search bias quantificationInformation Retrieval10.1007/s10791-018-9341-222:1-2(188-227)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s10791-018-9341-2
Lu HNiu WCaverlee JCollins-Thompson KMei QDavison BLiu YYilmaz E(2018)Learning Geo-Social User Topical Profiles with Bayesian Hierarchical User FactorizationThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3210044(205-214)Online publication date: 27-Jun-2018
https://dl.acm.org/doi/10.1145/3209978.3210044
Mullick AGoyal PGanguly NGupta M(2018)Harnessing Twitter for Answering Opinion List QueriesIEEE Transactions on Computational Social Systems10.1109/TCSS.2018.28811865:4(1083-1095)Online publication date: Dec-2018
https://doi.org/10.1109/TCSS.2018.2881186
Huang XXu CXiang LSang J(2018)Social Relationship Labeling Based on Multimodal Behaviors and Social InteractionsIEEE MultiMedia10.1109/MMUL.2018.287349825:4(68-78)Online publication date: 1-Oct-2018
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Kaur KBansal D(2018)Techniques to Extract Topical Experts in Twitter: A SurveyInformation and Communication Technology for Intelligent Systems10.1007/978-981-13-1742-2_38(391-399)Online publication date: 30-Dec-2018
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Rizos GPapadopoulos SKompatsiaris Y(2017)Multilabel user classification using the community structure of online networksPLOS ONE10.1371/journal.pone.017334712:3(e0173347)Online publication date: 9-Mar-2017
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Kavanaugh ASong Z(2017)Reflecting Community Events and Social Interactions through Archived Social MediaProceedings of the 18th Annual International Conference on Digital Government Research10.1145/3085228.3085282(223-231)Online publication date: 7-Jun-2017
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