Identifying Early Opinion Leaders on COVID-19 on Twitter

Zahra Hatami; Margeret Hall; Neil Thorne

doi:10.1007/978-3-030-90238-4_20

Identifying Early Opinion Leaders on COVID-19 on Twitter

Zahra Hatami, Margeret Hall, Neil Thorne

School of Interdisciplinary Informatics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Scopus citations

Abstract

This study aims to empirically identify opinion leaders on Twitter from the lens of Innovation Diffusion theory. We analyzed pandemic-specific tweets from casual users as well as from the US President to map their conversation for the purpose of finding opinion leaders over a three month period at the onset of the pandemic. By applying network analysis following with cluster enrichment as well as sentiment analysis, we recognize potential thought leaders, but we could not find strong evidence for opinion leaders according to the Innovation Diffusion theory. We interpret that users tweet for two different purposes - tweets to elicit agreement and tweets to elicit debate.

Original language	English (US)
Title of host publication	HCI International 2021 - Late Breaking Papers
Subtitle of host publication	Design and User Experience - 23rd HCI International Conference, HCII 2021, Proceedings
Editors	Constantine Stephanidis, Marcelo M. Soares, Marcelo M. Soares, Elizabeth Rosenzweig, Aaron Marcus, Sakae Yamamoto, Hirohiko Mori, Pei-Luen Patrick Rau, Gabriele Meiselwitz, Xiaowen Fang, Abbas Moallem
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	280-297
Number of pages	18
ISBN (Print)	9783030902377
DOIs	https://doi.org/10.1007/978-3-030-90238-4_20
State	Published - 2021
Event	23rd International Conference on Human-Computer Interaction , HCII 2021 - Virtual, online Duration: Jul 24 2021 → Jul 29 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13094 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	23rd International Conference on Human-Computer Interaction , HCII 2021
City	Virtual, online
Period	7/24/21 → 7/29/21

Keywords

Network analysis
Opinion leaders
Sentiment analysis

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-90238-4_20

Cite this

Hatami, Z., Hall, M., & Thorne, N. (2021). Identifying Early Opinion Leaders on COVID-19 on Twitter. In C. Stephanidis, M. M. Soares, M. M. Soares, E. Rosenzweig, A. Marcus, S. Yamamoto, H. Mori, P.-L. P. Rau, G. Meiselwitz, X. Fang, & A. Moallem (Eds.), HCI International 2021 - Late Breaking Papers: Design and User Experience - 23rd HCI International Conference, HCII 2021, Proceedings (pp. 280-297). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13094 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-90238-4_20

Identifying Early Opinion Leaders on COVID-19 on Twitter. / Hatami, Zahra; Hall, Margeret; Thorne, Neil.
HCI International 2021 - Late Breaking Papers: Design and User Experience - 23rd HCI International Conference, HCII 2021, Proceedings. ed. / Constantine Stephanidis; Marcelo M. Soares; Marcelo M. Soares; Elizabeth Rosenzweig; Aaron Marcus; Sakae Yamamoto; Hirohiko Mori; Pei-Luen Patrick Rau; Gabriele Meiselwitz; Xiaowen Fang; Abbas Moallem. Springer Science and Business Media Deutschland GmbH, 2021. p. 280-297 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13094 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Hatami, Z, Hall, M & Thorne, N 2021, Identifying Early Opinion Leaders on COVID-19 on Twitter. in C Stephanidis, MM Soares, MM Soares, E Rosenzweig, A Marcus, S Yamamoto, H Mori, P-LP Rau, G Meiselwitz, X Fang & A Moallem (eds), HCI International 2021 - Late Breaking Papers: Design and User Experience - 23rd HCI International Conference, HCII 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13094 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 280-297, 23rd International Conference on Human-Computer Interaction , HCII 2021, Virtual, online, 7/24/21. https://doi.org/10.1007/978-3-030-90238-4_20

Hatami Z, Hall M, Thorne N. Identifying Early Opinion Leaders on COVID-19 on Twitter. In Stephanidis C, Soares MM, Soares MM, Rosenzweig E, Marcus A, Yamamoto S, Mori H, Rau PLP, Meiselwitz G, Fang X, Moallem A, editors, HCI International 2021 - Late Breaking Papers: Design and User Experience - 23rd HCI International Conference, HCII 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 280-297. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-90238-4_20

Hatami, Zahra ; Hall, Margeret ; Thorne, Neil. / Identifying Early Opinion Leaders on COVID-19 on Twitter. HCI International 2021 - Late Breaking Papers: Design and User Experience - 23rd HCI International Conference, HCII 2021, Proceedings. editor / Constantine Stephanidis ; Marcelo M. Soares ; Marcelo M. Soares ; Elizabeth Rosenzweig ; Aaron Marcus ; Sakae Yamamoto ; Hirohiko Mori ; Pei-Luen Patrick Rau ; Gabriele Meiselwitz ; Xiaowen Fang ; Abbas Moallem. Springer Science and Business Media Deutschland GmbH, 2021. pp. 280-297 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{f3bc45d65159462fa6aa285ea0f5bcec,

title = "Identifying Early Opinion Leaders on COVID-19 on Twitter",

abstract = "This study aims to empirically identify opinion leaders on Twitter from the lens of Innovation Diffusion theory. We analyzed pandemic-specific tweets from casual users as well as from the US President to map their conversation for the purpose of finding opinion leaders over a three month period at the onset of the pandemic. By applying network analysis following with cluster enrichment as well as sentiment analysis, we recognize potential thought leaders, but we could not find strong evidence for opinion leaders according to the Innovation Diffusion theory. We interpret that users tweet for two different purposes - tweets to elicit agreement and tweets to elicit debate.",

keywords = "Network analysis, Opinion leaders, Sentiment analysis",

author = "Zahra Hatami and Margeret Hall and Neil Thorne",

note = "Funding Information: A big thanks to Rui Yang from The University of Nebraska at Omaha for providing a dataset of English-speaking lay users? conversations which were extracted from the Twitter platform. For citations of references, we prefer the use of square brackets and consecutive numbers. Citations using labels or the author/year convention are also acceptable. Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 23rd International Conference on Human-Computer Interaction , HCII 2021 ; Conference date: 24-07-2021 Through 29-07-2021",

year = "2021",

doi = "10.1007/978-3-030-90238-4_20",

language = "English (US)",

isbn = "9783030902377",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "280--297",

editor = "Constantine Stephanidis and Soares, {Marcelo M.} and Soares, {Marcelo M.} and Elizabeth Rosenzweig and Aaron Marcus and Sakae Yamamoto and Hirohiko Mori and Rau, {Pei-Luen Patrick} and Gabriele Meiselwitz and Xiaowen Fang and Abbas Moallem",

booktitle = "HCI International 2021 - Late Breaking Papers",

address = "Germany",

}

TY - GEN

T1 - Identifying Early Opinion Leaders on COVID-19 on Twitter

AU - Hatami, Zahra

AU - Hall, Margeret

AU - Thorne, Neil

N1 - Funding Information: A big thanks to Rui Yang from The University of Nebraska at Omaha for providing a dataset of English-speaking lay users? conversations which were extracted from the Twitter platform. For citations of references, we prefer the use of square brackets and consecutive numbers. Citations using labels or the author/year convention are also acceptable. Publisher Copyright: © 2021, Springer Nature Switzerland AG.

PY - 2021

Y1 - 2021

N2 - This study aims to empirically identify opinion leaders on Twitter from the lens of Innovation Diffusion theory. We analyzed pandemic-specific tweets from casual users as well as from the US President to map their conversation for the purpose of finding opinion leaders over a three month period at the onset of the pandemic. By applying network analysis following with cluster enrichment as well as sentiment analysis, we recognize potential thought leaders, but we could not find strong evidence for opinion leaders according to the Innovation Diffusion theory. We interpret that users tweet for two different purposes - tweets to elicit agreement and tweets to elicit debate.

AB - This study aims to empirically identify opinion leaders on Twitter from the lens of Innovation Diffusion theory. We analyzed pandemic-specific tweets from casual users as well as from the US President to map their conversation for the purpose of finding opinion leaders over a three month period at the onset of the pandemic. By applying network analysis following with cluster enrichment as well as sentiment analysis, we recognize potential thought leaders, but we could not find strong evidence for opinion leaders according to the Innovation Diffusion theory. We interpret that users tweet for two different purposes - tweets to elicit agreement and tweets to elicit debate.

KW - Network analysis

KW - Opinion leaders

KW - Sentiment analysis

UR - http://www.scopus.com/inward/record.url?scp=85120656833&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85120656833&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-90238-4_20

DO - 10.1007/978-3-030-90238-4_20

M3 - Conference contribution

AN - SCOPUS:85120656833

SN - 9783030902377

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 280

EP - 297

BT - HCI International 2021 - Late Breaking Papers

A2 - Stephanidis, Constantine

A2 - Soares, Marcelo M.

A2 - Rosenzweig, Elizabeth

A2 - Marcus, Aaron

A2 - Yamamoto, Sakae

A2 - Mori, Hirohiko

A2 - Rau, Pei-Luen Patrick

A2 - Meiselwitz, Gabriele

A2 - Fang, Xiaowen

A2 - Moallem, Abbas

PB - Springer Science and Business Media Deutschland GmbH

T2 - 23rd International Conference on Human-Computer Interaction , HCII 2021

Y2 - 24 July 2021 through 29 July 2021

ER -