research-article

Abstractions, Expressions and Online Collectives

Authors:

Nirmal Kumar Sivaraman,

Srinath SrinivasaAuthors Info & Claims

WebSci '15: Proceedings of the ACM Web Science Conference

Article No.: 58, Pages 1 - 2

https://doi.org/10.1145/2786451.2786499

Published: 28 June 2015 Publication History

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Abstract

Groups of people or collectives, possess a number of interesting properties even in the online world. While there are associated with positive connotations like "The Wisdom of the Crowd," not all collectives are wise. In this paper, we analyze collectives in terms of two cognitive dimensions called abstraction and expression. Based on the extent of "coagulation" of abstractions and expressions in the collective, we identify four extreme points that we call: crowds, herds, mobs and gangs respectively. We also propose and compare two computational models to score collectives along the above characterization.

References

[1]

A. Bozzon, M. Brambilla, S. Ceri, M. Silvestri, and G. Vesci. Choosing the right crowd: expert finding in social networks. In Proceedings of the 16th International Conference on Extending Database Technology, pages 637--648. ACM, 2013.

Digital Library

Google Scholar

[2]

A. Doan, R. Ramakrishnan, and A. Y. Halevy. Crowdsourcing systems on the world-wide web. Communications of the ACM, 54(4):86--96, 2011.

Digital Library

Google Scholar

[3]

S. D. Reicher, R. Spears, and T. Postmes. A social identity model of deindividuation phenomena. European review of social psychology, 6(1):161--198, 1995.

Google Scholar

[4]

N. K. Sivaraman and S. Srinivasa. Classification of online collectives based on abstractions and expressions. Technical Report 1502, Web Sciences Lab, International Institute of Information Technology Bengaluru, India, 560100, 2015.

Google Scholar

[5]

J. Suler. The online disinhibition effect. Cyberpsychology & behavior, 7(3):321--326, 2004.

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Cited By

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Subbanarasimha RSrinivasa SMandyam S(2020)Invisible Stories That Drive Online Social CognitionIEEE Transactions on Computational Social Systems10.1109/TCSS.2020.30094747:5(1264-1277)Online publication date: Oct-2020
https://doi.org/10.1109/TCSS.2020.3009474
Pavagada Subbanarasimha RCulpepper JMoffat ABennett PLerman K(2019)Designing the Cogno-Web ObservatoryProceedings of the Twelfth ACM International Conference on Web Search and Data Mining10.1145/3289600.3291600(814-815)Online publication date: 30-Jan-2019
https://dl.acm.org/doi/10.1145/3289600.3291600
Srinivasa SSubbanarasimha R(2018)Design of the Cogno Web Observatory for Characterizing Online Social CognitionBig Data Analytics10.1007/978-3-030-04780-1_10(139-154)Online publication date: 18-Dec-2018
https://dl.acm.org/doi/10.1007/978-3-030-04780-1_10
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Index Terms

Abstractions, Expressions and Online Collectives
1. Information systems
  1. Information systems applications

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Information

Published In

WebSci '15: Proceedings of the ACM Web Science Conference

June 2015

366 pages

ISBN:9781450336727

DOI:10.1145/2786451

Conference Chair:
David De Roure,
Program Chairs:
Pete Burnap,
Susan Halford

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 June 2015

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WebSci '15: ACM Web Science Conference

June 28 - July 1, 2015

Oxford, United Kingdom

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Overall Acceptance Rate 245 of 933 submissions, 26%

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Cited By

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Subbanarasimha RSrinivasa SMandyam S(2020)Invisible Stories That Drive Online Social CognitionIEEE Transactions on Computational Social Systems10.1109/TCSS.2020.30094747:5(1264-1277)Online publication date: Oct-2020
https://doi.org/10.1109/TCSS.2020.3009474
Pavagada Subbanarasimha RCulpepper JMoffat ABennett PLerman K(2019)Designing the Cogno-Web ObservatoryProceedings of the Twelfth ACM International Conference on Web Search and Data Mining10.1145/3289600.3291600(814-815)Online publication date: 30-Jan-2019
https://dl.acm.org/doi/10.1145/3289600.3291600
Srinivasa SSubbanarasimha R(2018)Design of the Cogno Web Observatory for Characterizing Online Social CognitionBig Data Analytics10.1007/978-3-030-04780-1_10(139-154)Online publication date: 18-Dec-2018
https://dl.acm.org/doi/10.1007/978-3-030-04780-1_10
Madaan ANurse Jde Roure DO'Hara KHall WCreese S(undefined)A Storm in an IoT Cup: The Emergence of Cyber-Physical Social MachinesSSRN Electronic Journal10.2139/ssrn.3250383
https://doi.org/10.2139/ssrn.3250383

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