The Effect of Message Repetition on Information Diffusion on Twitter: An Agent-Based Approach

Manuela López; Carmen Hidalgo-Alcázar; Paul Leger

doi:10.1109/tpc.2023.3260449

IEEE Transactions on Professional Communication Jun 2023

The Effect of Message Repetition on Information Diffusion on Twitter: An Agent-Based Approach

Manuela López Universidad de Murcia ; Carmen Hidalgo-Alcázar ; Paul Leger

Abstract

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background: Twitter offers tools that facilitate the diffusion of information by which companies can engage consumers to share their messages. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Literature review: Communication professionals are using platforms such as Twitter to disseminate information; however, the strategies that they should use to achieve high information diffusion are not clear. This article proposes message repetition as a strategy. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Research questions: 1. What is the wear-out point of Twitter? 2. How many times should a company repeat a tweet written on its brand page to maximize the diffusion for seeds? 3. How many times should a company repeat a tweet written on its brand page to maximize the diffusion while minimizing the number of consumers reaching their wear-out point for seeds? 4. How many times should a company repeat a tweet written on its brand page to maximize the diffusion for nonseeds? 5. How many times should a company repeat a tweet written on its brand page to maximize the diffusion while minimizing the number of consumers reaching their wear-out point for both seeds and nonseeds? <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Research methodology: An agent-based simulation model for information diffusion is proposed as an approach to measure the diffusion of a tweet that has been repeated. The model considers that consumers can reach their wear-out point when they read a tweet several times. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results: The results of the model indicate the number of times a company should send the same tweet to achieve high information diffusion before this action has negative effects on consumers. Brand followers are key to achieving high information diffusion; however, consumers begin to feel bothered by the tweet by the sixth repetition. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusions: To the best of our knowledge, this is the first study to examine tweet repetition as a strategy to achieve higher information diffusion on Twitter. In addition, it extends the information diffusion literature by controlling the wear-out effect. It contributes to both communication and computational science literature by analyzing a communication problem using an agent-based approach. Finally, this article contributes to the field of technical and professional communication by testing a strategy to reach great information diffusion, and by creating a tool that any company can use to anticipate the results of a communication campaign created in Twitter before launching it.

Journal: IEEE Transactions on Professional Communication
Published: 2023-06-01
DOI: 10.1109/tpc.2023.3260449
CompPile
Open Access: Closed
Topics: professional writing social media
Export: BibTeX RIS

Citation Context

Cited by in this index (1)

Lambrecht (2025)

“What People Are Saying” About Extreme Weather: A Comparison of Public Commenta…

IEEE Transactions on Professional Communication

References (79) · 4 in this index

srivastava (2020)

Journal of Digital & Social Media Marketing
10.1080/01633392.1988.10504936

↗
mcmillan (2019)

Best practices in using Twitter for business
10.4324/9781315806099

↗
10.1086/376800

↗

Show all 79 →

10.4135/9781452229379

↗
10.1080/00913367.2015.1018460

↗
sawyer (1981)

Repetition, cognitive responses, and persuasion

Cognitive Responses in Persuasion
10.3758/BF03212593

↗
costolo (2014)

Twitter CEO Dick Cistolo, Jack Dorsey interview
(2010)

Replies and retweets on Twitter
10.1007/s11747-014-0414-5

↗
10.1016/j.intmar.2012.01.003

↗
10.1037/h0076165

↗
10.2753/MIS0742-1222290408

↗
10.1080/1369118X.2013.853817

↗
10.1080/02650487.2015.1037708

↗
10.1080/15252019.2015.1016636

↗
Zhang et al. (2017)

Integrating Ego, Homophily, and Structural Factors to Measure User Influence in…

IEEE Transactions on Professional Communication
10.1016/j.tele.2021.101623

↗
10.1016/j.jbusres.2014.11.027

↗
10.1177/2329488415572788

↗
10.1016/j.chb.2014.11.008

↗
10.1109/SocialCom.2010.33

↗
10.1609/icwsm.v4i1.14033

↗
10.1016/j.bushor.2014.02.001

↗
10.1109/HICSS.2010.412

↗
10.1016/j.chb.2014.04.020

↗
10.1007/s11573- 020-01022-9

↗
10.31449/inf.v45i6.3575

↗
10.1177/0165551514565318

↗
10.1509/jm.74.2.71

↗
(2022)

Company info
10.1509/jm.10.0088

↗
10.1108/IntR-07-2016-0223

↗
10.1007/s11747-014-0388-3

↗
10.1080/00913367.2013.827606

↗
10.1007/s42979-021-00836-w

↗
10.1145/2872518.2889393

↗
10.1145/2487788.2488110

↗
10.1007/s12525-011-0065-z

↗
10.1016/j.pubrev.2010.12.001

↗
Smith et al. (2018)

Framing Controversy on Social Media: #NoDAPL and the Debate About the Dakota Ac…

IEEE Transactions on Professional Communication
10.1111/jcc4.12074

↗
10.1145/2187836.2187907

↗
10.3390/info13010013

↗
10.4135/9781412983327

↗
10.1007/978-3-030-36525-7_9

↗
10.1108/IMDS-07-2016-0290

↗
wang (2012)

A system for real-time Twitter sentiment analysis of 2012 U.S. presidential election cycle

Proc ACL Syst Demonstrations
(2014)

Should you repeat your tweets? Facts and figures from 1M tweets
10.1080/1206212X.2020.1758430

↗
Sun et al. (2022)

Corporations' Owned Social Media Narrative

IEEE Transactions on Professional Communication
10.1016/j.dss.2012.12.022

↗
10.1080/02650487.2016.1173765

↗
(2020)

How small business are using social media
10.1080/10641734.1998.10505073

↗
rosenstiel (2015)

How people use Twitter in general
(2013)

By the numbers a few amazing Twitter stats
10.1177/0047287520951639

↗
10.1108/EJM-03-2017-0182

↗
10.1509/jmr.15.0443

↗
fang (2009)

Simulation of new service product diffusion on cellular automata

China Market
vie (2015)

What's going on?: Challenges and opportunities for social media use in the writing classroom

J Fac Dev
lam (2016)

Connecting programmatic research with social media: Using data from Twitter to inform pro…

Programmatic Perspectives
leigon (2011)

Grape/wine marketing with new media and return of the Boomer

Practical Winery & Vineyard
10.1016/j.ijresmar.2011.04.002

↗
10.1080/02650487.2016.1240469

↗
10.2501/JAR-51-1-258-275

↗
10.1509/jmr.11.0305

↗
frels (2006)

Modeling the impact of consumer interactions in technology markets

J Cellular Automata
10.1023/A:1011122126881

↗
ma (2008)

Simulation of market diffusion of new product based on cellular automata

Proc Glob Market Conf Market Dynamic Global World
daer (2014)

Teaching and learning with social media

Programmatic Perspectives
Ferro et al. (2014)

Technical Communication Unbound: Knowledge Work, Social Media, and Emergent Com…

Technical Communication Quarterly
10.1108/OIR-02-2018-0065

↗
10.1016/j.chb.2015.01.008

↗
10.1007/978-3-642-02774-1_16

↗
10.1007/s11129-015-9159-9

↗

CrossRef global citation count: 2 View in citation network → Build reading path →

The Effect of Message Repetition on Information Diffusion on Twitter: An Agent-Based Approach

Abstract

Citation Context

Cited by in this index (1)

References (79) · 4 in this index

Related Articles