The Emerging Science of Content Labeling: Contextualizing Social Media Content Moderation

Morrow, Garrett; Swire‐Thompson, Briony; Polny, Jessica; Kopec, Matthew; Wihbey, John

doi:10.2139/ssrn.3742120

Cited by 9 publications

(5 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The research area of misinformation labeling is rapidly evolving, as academic and industry researchers are eager to understand the full implications of design decisions being made on social media platforms (Morrow et al 2020). Clayton et al (2019) investigate the effects of the locality and severity of misinformation warnings.…”

Section: Labeling Misnformation and Credibility Indicatorsmentioning

confidence: 99%

Do Explanations Increase the Effectiveness of AI-Crowd Generated Fake News Warnings?

Epstein

Foppiani

Hilgard

et al. 2022

ICWSM

View full text Add to dashboard Cite

Social media platforms are increasingly deploying complex interventions to help users detect false news. Labeling false news using techniques that combine crowd-sourcing with artificial intelligence (AI) offers a promising way to inform users about potentially low-quality information without censoring content, but also can be hard for users to understand. In this study, we examine how users respond in their sharing intentions to information they are provided about a hypothetical human-AI hybrid system. We ask i) if these warnings increase discernment in social media sharing intentions and ii) if explaining how the labeling system works can boost the effectiveness of the warnings. To do so, we conduct a study (N=1473 Americans) in which participants indicated their likelihood of sharing content. Participants were randomly assigned to a control, a treatment where false content was labeled, or a treatment where the warning labels came with an explanation of how they were generated. We find clear evidence that both treatments increase sharing discernment, and directional evidence that explanations increase the warnings' effectiveness. Interestingly, we do not find that the explanations increase self-reported trust in the warning labels, although we do find some evidence that participants found the warnings with the explanations to be more informative. Together, these results have important implications for designing and deploying transparent misinformation warning labels, and AI-mediated systems more broadly.

show abstract

Section: Labeling Misnformation and Credibility Indicatorsmentioning

confidence: 99%

Do Explanations Increase the Effectiveness of AI-Crowd Generated Fake News Warnings?

Epstein

Foppiani

Hilgard

et al. 2022

ICWSM

View full text Add to dashboard Cite

show abstract

“…Labeling individual posts is one common approach within this family of mechanisms. ‘Veracity’ labels are direct corrections to inaccurate posts, informing readers that information is disputed, whereas ‘contextual’ labels provide additional information about posts, such as their sources or who paid for them ( Morrow et al, 2020 ). For example, in 2020, Twitter introduced a new practice to label ‘information’ that had been ‘significantly and deceptively altered or fabricated’ ( Roth and Acuthan, 2020 ).…”

Section: Mechanisms That Direct User Attentionmentioning

confidence: 99%

“…These educational approaches have increased in lockstep with the widespread criticism of the harmful societal impact of social media platforms. Such approaches can be costly for founders of social media platforms to implement and sustain, and the efficacy of specific practices is currently unclear ( Morrow et al, 2020 ). That said, entrepreneurs concerned with striking a balance between enabling free speech and protecting users from misinformation and other harmful content are likely to need to invest in educating users about the content posted on their platform.…”

Section: Mechanisms That Direct User Attentionmentioning

confidence: 99%

Relying on the engagement of others: A review of the governance choices facing social media platform start-ups

Reuber

Fischer

2021

International Small Business Journal: Researching Entrepreneurs

View full text Add to dashboard Cite

We are grateful to Professors Rebecca Reuber and Eileen Fischer for contributing our 2022 annual review article. This insightful review explores an issue of great contemporary importance regarding the relationship between entrepreneurial activities and social media platforms. Whilst there is much popular and media commentary regarding the opportunities such platforms offer for entrepreneurship, we lack informed, academic reflection upon the role and influence of such platforms for both good and ill. Hence, this review article is timely in identifying current practices and raising important issues for future research. Our thanks to the authors for their valuable contribution to the ISBJ. Entrepreneurs create digital platforms which, in turn, facilitate entrepreneurial behaviours of others, the platform users. An important start-up activity is developing the mechanisms to govern user participation. While prior literature has provided insights on the governance of innovation platforms and exchange platforms, it has shed little light on the governance of social media platforms. In this review, we synthesize the emerging literature on diverse social media platforms, focussing on four types of governance mechanisms: those that regulate user behaviour, those related to user identification and stature, those that structure relationships among users and those that direct user attention. We highlight the implications of this body of literature for entrepreneurship scholars.

show abstract

“…Accurate information is insufficient to counteract codified false beliefs and attitudes ( Roozenbeek & van der Linden, 2022 ; van der Linden, 2022 ; Walter & Murphy, 2018 ), especially when those beliefs are intertwined with someone's identity ( Kahan, 2017b ). Existing research has tested a range of intervention techniques designed to influence opinion in online spaces, including: inoculation ( van der Linden, 2022 ), warnings/labels ( Morrow et al, 2022 ), and promoting critical thinking or media literacy ( Ecker et al, 2022 ), as well as debunking, refuting or rebutting misinformation ( Chan et al, 2017 ; Walter & Murphy, 2018 ). However, the effect sizes of successful online interventions are often modest and rapidly decay ( Maertens et al, 2021 ; Mourali & Drake, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Community-oriented Motivational Interviewing (MI): A novel framework extending MI to address COVID-19 vaccine misinformation in online social media platforms

Scales

Gorman

DiCaprio

et al. 2023

Computers in Human Behavior

View full text Add to dashboard Cite

The Emerging Science of Content Labeling: Contextualizing Social Media Content Moderation

Cited by 9 publications

References 63 publications

Do Explanations Increase the Effectiveness of AI-Crowd Generated Fake News Warnings?

Do Explanations Increase the Effectiveness of AI-Crowd Generated Fake News Warnings?

Relying on the engagement of others: A review of the governance choices facing social media platform start-ups

Community-oriented Motivational Interviewing (MI): A novel framework extending MI to address COVID-19 vaccine misinformation in online social media platforms

Contact Info

Product

Resources

About