research-article

Susceptibility to Unreliable Information Sources: Swift Adoption with Minimal Exposure

Authors:

Emilio FerraraAuthors Info & Claims

WWW '24: Proceedings of the ACM Web Conference 2024

Pages 4674 - 4685

https://doi.org/10.1145/3589334.3648154

Published: 13 May 2024 Publication History

Abstract

Misinformation proliferation on social media platforms is a pervasive threat to the integrity of online public discourse. Genuine users, susceptible to others' influence, often unknowingly engage with, endorse, and re-share questionable pieces of information, collectively amplifying the spread of misinformation. In this study, we introduce an empirical framework to investigate users' susceptibility to influence when exposed to unreliable and reliable information sources. Leveraging two datasets on political and public health discussions on Twitter, we analyze the impact of exposure on the adoption of information sources, examining how the reliability of the source modulates this relationship. Our findings provide evidence that increased exposure augments the likelihood of adoption. Users tend to adopt low-credibility sources with fewer exposures than high-credibility sources, a trend that persists even among non-partisan users. Furthermore, the number of exposures needed for adoption varies based on the source credibility, with extreme ends of the spectrum (very high or low credibility) requiring fewer exposures for adoption. Additionally, we reveal that the adoption of information sources often mirrors users' prior exposure to sources with comparable credibility levels. Our research offers critical insights for mitigating the endorsement of misinformation by vulnerable users, offering a framework to study the dynamics of content exposure and adoption on social media platforms.

Supplemental Material

MP4 File

Video presentation

Download
1519.46 MB

MP4 File

Supplemental video

Download
44.67 MB

References

[1]

Sinan Aral and Dylan Walker. 2012. Identifying Influential and Susceptible Members of Social Networks. Science, Vol. 337, 6092 (2012), 337--341.

[2]

Eytan Bakshy, Itamar Rosenn, Cameron Marlow, and Lada Adamic. 2012. The Role of Social Networks in Information Diffusion. In WWW 2012. 519--528.

[3]

Paul C Bauer and Bernhard Clemm von Hohenberg. 2021. Believing and Sharing Information by Fake Sources: An Experiment. Political Communication, Vol. 38, 6 (2021), 647--671.

[4]

Julia Baum and Rasha Abdel Rahman. 2021. Emotional News Affects Social Judgments Independent of Perceived Media Credibility. Social Cognitive and Affective Neuroscience, Vol. 16, 3 (2021), 280--291.

[5]

Dustin P Calvillo, Abraham M Rutchick, and Ryan JB Garcia. 2021. Individual Differences in Belief in Fake News About Election Fraud After the 2020 US Election. Behavioral Sciences, Vol. 11, 12 (2021), 175.

[6]

Tatiana Celadin, Valerio Capraro, Gordon Pennycook, David G Rand, et al. 2023. Displaying News Source Trustworthiness Ratings Reduces Sharing Intentions for False News Posts. Journal of Online Trust and Safety, Vol. 1, 5 (2023).

[7]

Ho-Chun Herbert Chang and Emilio Ferrara. 2022. Comparative analysis of social bots and humans during the COVID-19 pandemic. Journal of Computational Social Science, Vol. 5, 2 (2022), 1409--1425.

[8]

Emily Chen, Herbert Chang, Ashwin Rao, Kristina Lerman, Geoffrey Cowan, and Emilio Ferrara. 2021a. COVID-19 Misinformation and the 2020 US Presidential Election. The Harvard Kennedy School Misinformation Review, Vol. 1, 7 (2021).

[9]

Emily Chen, Ashok Deb, and Emilio Ferrara. 2021b. #Election2020: The First Public Twitter Dataset on the 2020 US Presidential Election. Journal of Computational Social Science, Vol. 5 (2021), 1--18.

[10]

Emily Chen, Julie Jiang, Ho-Chun Herbert Chang, Goran Muric, and Emilio Ferrara. 2022. Charting the Information and Misinformation Landscape to Characterize Misinfodemics on Social Media: COVID-19 Infodemiology Study at a Planetary Scale. JMIR Infodemiology, Vol. 2, 1 (2022), e32378.

[11]

Emily Chen, Kristina Lerman, and Emilio Ferrara. 2020. Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set. JMIR Public Health and Surveillance, Vol. 6, 2 (2020), e19273.

[12]

Wen-Ying Sylvia Chou and Alexandra Budenz. 2020. Considering Emotion in COVID-19 Vaccine Communication: Addressing Vaccine Hesitancy and Fostering Vaccine Confidence. Health Communication, Vol. 35, 14 (2020), 1718--1722.

[13]

Alice Dechêne, Christoph Stahl, Jochim Hansen, and Michaela W"anke. 2010. The Truth About the Truth: A Meta-Analytic Review of the Truth Effect. Personality and Social Psychology Review, Vol. 14, 2 (2010), 238--257.

[14]

Ullrich KH Ecker, Stephan Lewandowsky, John Cook, Philipp Schmid, Lisa K Fazio, Nadia Brashier, Panayiota Kendeou, Emily K Vraga, and Michelle A Amazeen. 2022. The Psychological Drivers of Misinformation Belief and its Resistance to Correction. Nature Reviews Psychology, Vol. 1, 1 (2022), 13--29.

[15]

Emilio Ferrara. 2018. Measuring Social Spam and the Effect of Bots on Information Diffusion in Social Media. Complex Spreading Phenomena in Social Systems: Influence and Contagion in Real-world Social Networks (2018), 229--255.

[16]

Emilio Ferrara. 2022. Twitter Spam and False Accounts Prevalence, Detection and Characterization: A Survey. First Monday, Vol. 27, 12 (2022).

[17]

Emilio Ferrara and Zeyao Yang. 2015. Measuring Emotional Contagion in Social Media. PloS one, Vol. 10, 11 (2015), e0142390.

[18]

Amit Goyal, Francesco Bonchi, and Laks VS Lakshmanan. 2010. Learning Influence Probabilities in Social Networks. In WSDM 2010. 241--250.

[19]

Andrew Guess, Kevin Munger, Jonathan Nagler, and Joshua Tucker. 2019. How Accurate are Survey Responses on Social Media and Politics? Political Communication, Vol. 36, 2 (2019), 241--258.

[20]

Andrew Guess, Brendan Nyhan, and Jason Reifler. 2018. Selective Exposure to Misinformation: Evidence from the Consumption of Fake News during the 2016 US Presidential Campaign. (2018).

[21]

Andrew M Guess, Brendan Nyhan, and Jason Reifler. 2020. Exposure to Untrustworthy Websites in the 2016 US Election. Nature Human Behaviour, Vol. 4, 5 (2020), 472--480.

[22]

Jeff Hemsley, Jennifer Stromer-Galley, Bryan Semaan, and Sikana Tanupabrungsun. 2018. Tweeting to the Target: Candidates' Use of Strategic Messages and @Mentions on Twitter. Journal of Information Technology & Politics, Vol. 15, 1 (2018), 3--18.

[23]

Tuan-Anh Hoang and Ee-Peng Lim. 2016. Tracking Virality and Susceptibility in Social Media. In CIKM 2016. 1059--1068.

[24]

Nathan O Hodas and Kristina Lerman. 2014. The Simple Rules of Social Contagion. Scientific Reports, Vol. 4, 1 (2014), 4343.

[25]

Kokil Jaidka, Subhayan Mukerjee, and Yphtach Lelkes. 2023. Silenced on Social Media: the Gatekeeping Functions of Shadowbans in the American Twitterverse. Journal of Communication, Vol. 73, 2 (2023), 163--178.

[26]

Julie Jiang, Xiang Ren, Emilio Ferrara, et al. 2021. Social Media Polarization and Echo Chambers in the Context of COVID-19: Case Study. JMIRx Med, Vol. 2, 3 (2021), e29570.

[27]

Hollyn M Johnson and Colleen M Seifert. 1994. Sources of the Continued Influence Effect: When Misinformation in Memory Affects Later Inferences. Journal of Experimental Psychology: Learning, Memory, and Cognition, Vol. 20, 6 (1994), 1420.

[28]

Brett Laursen and Sharon Faur. 2022. What Does It Mean To Be Susceptible To Influence? A Brief Primer on Peer Conformity and Developmental Changes That Affect It. International Journal of Behavioral Development, Vol. 46, 3 (2022), 222--237.

[29]

Luca Luceri, Torsten Braun, and Silvia Giordano. 2018a. Social Influence (Deep) Learning for Human Behavior Prediction. In Complex Networks IX: Proceedings of the 9th Conference on Complex Networks CompleNet 2018 9. Springer, 261--269.

[30]

Luca Luceri, Ashok Deb, Adam Badawy, and Emilio Ferrara. 2019. Red Bots Do It Better: Comparative Analysis of Social Bot Partisan Behavior. In Companion Proceedings of the 2019 World Wide Web Conference. 1007--1012.

[31]

Luca Luceri, Valeria Pantè, Keith Burghardt, and Emilio Ferrara. [n.,d.]. Unmasking the Web of Deceit: Uncovering Coordinated Activity to Expose Information Operations on Twitter. In WWW 2024.

[32]

Luca Luceri, Alberto Vancheri, Torsten Braun, and Silvia Giordano. 2018b. On the Social Influence in Human Behavior: Physical, Homophily, and Social Communities. In Complex Networks & Their Applications VI: Proceedings of Complex Networks 2017 (The Sixth International Conference on Complex Networks and Their Applications). Springer, 856--868.

[33]

Joseph Marks, Eloise Copland, Eleanor Loh, Cass R Sunstein, and Tali Sharot. 2019. Epistemic Spillovers: Learning Others' Political Views Reduces the Ability to Assess and Use Their Expertise in Nonpolitical Domains. Cognition, Vol. 188 (2019), 74--84.

[34]

Elizabeth J Marsh and Brenda W Yang. 2018. Believing Things That Are Not True: A Cognitive Science Perspective on Misinformation. In Misinformation and Mass Audiences. University of Texas Press, 15--34.

[35]

Ryan C Moore, Ross Dahlke, and Jeffrey T Hancock. 2023. Exposure to Untrustworthy Websites in the 2020 US Election. Nature Human Behaviour, Vol. 7 (2023), 1--10.

[36]

Patricia Moravec, Randall Minas, and Alan R Dennis. 2018. Fake News on Social Media: People Believe What They Want To Believe When It Makes No Sense At All. Kelley School of Business Research Paper (2018), 18--87.

[37]

Mehdi Moussa"id, Juliane E K"ammer, Pantelis P Analytis, and Hansjörg Neth. 2013. Social Influence and the Collective Dynamics of Opinion Formation. PloS One, Vol. 8, 11 (2013), e78433.

[38]

Christof Naumzik and Stefan Feuerriegel. 2022. Detecting False Rumors From Retweet Dynamics on Social Media. In WWW 2022. 2798--2809.

[39]

D Nikolov, A Flammini, and F Menczer. 2021. Right and Left, Partisanship Predicts (Asymmetric) Vulnerability to Misinformation. The Harvard Kennedy School Misinformation Review 7 (2021).

[40]

Gianluca Nogara, Padinjaredath Suresh Vishnuprasad, Felipe Cardoso, Omran Ayoub, Silvia Giordano, and Luca Luceri. [n.,d.]. The Disinformation Dozen: An Exploratory Analysis of Covid-19 Disinformation Proliferation on Twitter. In WWW 2022. 348--358.

[41]

Mathias Osmundsen, Alexander Bor, Peter Bjerregaard Vahlstrup, Anja Bechmann, and Michael Bang Petersen. 2021. Partisan Polarization is the Primary Psychological Motivation Behind Political Fake News Sharing on Twitter. American Political Science Review, Vol. 115, 3 (2021), 999--1015.

[42]

Gordon Pennycook, Tyrone D Cannon, and David G Rand. 2018. Prior Exposure Increases Perceived Accuracy of Fake News. Journal of Experimental Psychology: General, Vol. 147, 12 (2018), 1865.

[43]

Gordon Pennycook, Jonathon McPhetres, Yunhao Zhang, Jackson G Lu, and David G Rand. 2020. Fighting COVID-19 Misinformation on Social Media: Experimental Evidence for a Scalable Accuracy-Nudge Intervention. Psychological Science, Vol. 31, 7 (2020), 770--780.

[44]

Gordon Pennycook and David G Rand. 2019. Lazy, Not Biased: Susceptibility to Partisan Fake News Is Better Explained by Lack of Reasoning Than by Motivated Reasoning. Cognition, Vol. 188 (2019), 39--50.

[45]

Gordon Pennycook and David G Rand. 2021. The Psychology of Fake News. Trends in Cognitive Sciences, Vol. 25, 5 (2021), 388--402.

[46]

Francesco Pierri, Luca Luceri, Nikhil Jindal, and Emilio Ferrara. 2023. Propaganda and Misinformation on Facebook and Twitter during the Russian Invasion of Ukraine. In Proceedings of the 15th ACM Web Science Conference 2023. 65--74.

Digital Library

[47]

Ashwin Rao, Fred Morstatter, and Kristina Lerman. 2022a. Partisan Asymmetries in Exposure to Misinformation. Scientific Reports, Vol. 12, 1 (2022), 15671.

[48]

Ashwin Rao, Fred Morstatter, and Kristina Lerman. 2022b. Retweets Distort Exposure to Polarized Information. arXiv preprint arXiv:2211.16480 (2022).

[49]

Byron Reeves, Nilam Ram, Thomas N Robinson, James J Cummings, C Lee Giles, Jennifer Pan, Agnese Chiatti, MJ Cho, Katie Roehrick, Xiao Yang, et al. 2021. Screenomics: A Framework To Capture and Analyze Personal Life Experiences and the Ways That Technology Shapes Them. Human-Computer Interaction, Vol. 36, 2 (2021), 150--201.

[50]

Daniel M Romero, Brendan Meeder, and Jon Kleinberg. 2011. Differences in the Mechanics of Information Diffusion Across Topics: Idioms, Political Hashtags, and Complex Contagion on Twitter. In WWW 2011. 695--704.

Digital Library

[51]

Nir Rosenfeld, Aron Szanto, and David C Parkes. 2020. A Kernel of Truth: Determining Rumor Veracity on Twitter by Diffusion Pattern Alone. In WWW 2020. 1018--1028.

Digital Library

[52]

Kazumi Saito, Ryohei Nakano, and Masahiro Kimura. 2008. Prediction of Information Diffusion Probabilities for Independent Cascade Model. In International Conference on Knowledge-Based and Intelligent Information and Engineering Systems. Springer, 67--75.

[53]

Lauren L Saling, Devi Mallal, Falk Scholer, Russell Skelton, and Damiano Spina. 2021. No One is Immune to Misinformation: An Investigation of Misinformation Sharing by Subscribers to a Fact-Checking Newsletter. PLoS One, Vol. 16, 8 (2021), e0255702.

[54]

Kazutoshi Sasahara, Wen Chen, Hao Peng, Giovanni Luca Ciampaglia, Alessandro Flammini, and Filippo Menczer. 2021. Social Influence and Unfollowing Accelerate the Emergence of Echo Chambers. Journal of Computational Social Science, Vol. 4, 1 (2021), 381--402.

[55]

Karandeep Singh, Gabriel Lima, Meeyoung Cha, Chiyoung Cha, Juhi Kulshrestha, Yong-Yeol Ahn, and Onur Varol. 2022. Misinformation, Believability, and Vaccine Acceptance Over 40 Countries: Takeaways From the Initial Phase of the COVID-19 Infodemic. PLoS ONE, Vol. 17, 2 (2022), e0263381.

[56]

Kirill Solovev and Nicolas Pröllochs. 2022. Moral Emotions Shape the Virality of COVID-19 Misinformation on Social Media. In WWW 2022. 3706--3717.

[57]

Galen Stocking, Amy Mitchell, Katerina Eva Matsa, Regina Widjaya, Mark Jurkowitz, Shreenita Ghosh, Aaron Smith, Sarah Naseer, and Christopher St Aubin. 2022. The Role of Alternative Social Media in the News and Information Environment. Pew Research Center (2022).

[58]

Xian Teng, Yu-Ru Lin, Wen-Ting Chung, Ang Li, and Adriana Kovashka. 2022. Characterizing User Susceptibility to COVID-19 Misinformation on Twitter. In ICWSM 2022. 1005--1016.

[59]

Cecilie Steenbuch Traberg and Sander van der Linden. 2022. Birds of a Feather are Persuaded Together: Perceived Source Credibility Mediates the Effect of Political Bias on Misinformation Susceptibility. Personality and Individual Differences, Vol. 185 (2022), 111269.

[60]

Sander Van Der Linden. 2022. Misinformation: Susceptibility, Spread, and Interventions to Immunize the Public. Nature Medicine, Vol. 28, 3 (2022), 460--467.

[61]

Soroush Vosoughi, Deb Roy, and Sinan Aral. 2018. The Spread of True and False News Online. Science, Vol. 359, 6380 (2018), 1146--1151.

[62]

Nathan Walter and Riva Tukachinsky. 2020. A Meta-Analytic Examination of the Continued Influence of Misinformation in the Face of Correction: How Powerful Is It, Why Does It Happen, and How To Stop It? Communication Research, Vol. 47, 2 (2020), 155--177.

[63]

Emily L Wang, Luca Luceri, Francesco Pierri, and Emilio Ferrara. 2023. Identifying and Characterizing Behavioral Classes of Radicalization Within the QAnon Conspiracy on Twitter. In ICWSM 2023. 890--901.

[64]

Wei-Chun Wang, Nadia M Brashier, Erik A Wing, Elizabeth J Marsh, and Roberto Cabeza. 2016. On Known Unknowns: Fluency and the Neural Mechanisms of Illusory Truth. Journal of Cognitive Neuroscience, Vol. 28, 5 (2016), 739--746.

Digital Library

[65]

Waheeb Yaqub, Otari Kakhidze, Morgan L Brockman, Nasir Memon, and Sameer Patil. 2020. Effects of Credibility Indicators on Social Media News Sharing Intent. In CHI 2020. 1--14.

[66]

Xinyi Zhou and Reza Zafarani. 2019. Network-based Fake News Detection: A Pattern-Driven Approach. ACM SIGKDD Explorations Newsletter, Vol. 21, 2 (2019), 48--60. io

Digital Library

Cited By

Luceri LBoniardi EFerrara EChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Leveraging Large Language Models to Detect Influence Campaigns on Social MediaCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651912(1459-1467)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651912
Luceri LPantè VBurghardt KFerrara EChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Unmasking the Web of Deceit: Uncovering Coordinated Activity to Expose Information Operations on TwitterProceedings of the ACM Web Conference 202410.1145/3589334.3645529(2530-2541)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645529

Index Terms

Susceptibility to Unreliable Information Sources: Swift Adoption with Minimal Exposure
1. Human-centered computing
  1. Collaborative and social computing
    1. Empirical studies in collaborative and social computing
2. Information systems
  1. World Wide Web
    1. Web applications
      1. Social networks

Recommendations

In Twitter we trust(ed): How perceptions of Twitter's helpfulness influence news post credibility perceptions and news engagement
Abstract
This study examines the impacts of news veracity, source credibility, collective social endorsement, and perceptions of Twitter's utility on perceived credibility of and engagement with social media news posts. We conducted a 2 (Veracity: real ...
Highlights
- News veracity and source credibility influence assessments of post credibility.
- Social endorsement does not affect post credibility perceptions or behavioral intent.
- Twitter believers more likely perceive posts as credible and ...
The Dynamics of (Not) Unfollowing Misinformation Spreaders
WWW '24: Proceedings of the ACM Web Conference 2024

Many studies explore how people "come into" misinformation exposure. But much less is known about how people "come out of" misinformation exposure. Do people organically sever ties to misinformation spreaders? And what predicts doing so? Over six months, ...
Are Mutated Misinformation More Contagious? A Case Study of COVID-19 Misinformation on Twitter
WebSci '22: Proceedings of the 14th ACM Web Science Conference 2022

The spread of online misinformation has become a major global risk. Understanding how misinformation propagates on social media is vital. While prior studies suggest that the content factors, such as emotion and topic in texts, are closely related to the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

WWW '24: Proceedings of the ACM Web Conference 2024

May 2024

4826 pages

ISBN:9798400701719

DOI:10.1145/3589334

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Chong-Wah Ngo
Singapore Management University
,
Proceedings Chair:
Roy Ka-Wei Lee
Singapore University of Technology and Design
,
Program Chairs:
Ravi Kumar
Google
,
Hady W. Lauw
Singapore Management University

Copyright © 2024 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].

Sponsors

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2024

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

DARPA

Conference

WWW '24

Sponsor:

SIGWEB

WWW '24: The ACM Web Conference 2024

May 13 - 17, 2024

Singapore, Singapore

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
123
Total Downloads

Downloads (Last 12 months)123
Downloads (Last 6 weeks)23

Reflects downloads up to 22 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Luceri LBoniardi EFerrara EChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Leveraging Large Language Models to Detect Influence Campaigns on Social MediaCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651912(1459-1467)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651912
Luceri LPantè VBurghardt KFerrara EChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Unmasking the Web of Deceit: Uncovering Coordinated Activity to Expose Information Operations on TwitterProceedings of the ACM Web Conference 202410.1145/3589334.3645529(2530-2541)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645529

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents