Interventions for Softening Can Lead to Hardening of Opinions: Evidence from a Randomized Controlled Trial

Spitz, Andreas; Abu‐Akel, Ahmad; West, Robert

doi:10.1145/3442381.3450019

Cited by 5 publications

(3 citation statements)

References 54 publications

(35 reference statements)

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“…1.25) for Human-Human debates. This trend is consistent with previous literature describing a hardening of pre-treatment opinions when people express their ideas [75] or are exposed to disagreeing views [76], or finding opinion change to be highly affected by argument order [77].…”

Section: Should Every Citizen Receive a Basic Income From The Governm...supporting

confidence: 92%

On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trial

Salvi,

Ribeiro,

Gallotti

et al. 2024

Preprint

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Can large language models (LLMs) create tailor-made, convincing arguments to promote false or misleading narratives online? Early work has found that LLMs can generate content perceived on par with, or even more persuasive than, human-written messages. However, there is still limited evidence regarding LLMs' persuasive capabilities in direct conversations with humans—the scenario these models are usually deployed at. In this pre-registered study, we analyze the power of AI-driven persuasion in a controlled, harmless setting. To this end, we created a web-based platform where human participants engaged in short, multi-round debates with either human or LLM opponents. Each participant was randomly assigned to one of four treatment conditions in a two-by-two factorial design: (1) the conversation partner was either another human or an LLM; (2) the conversation partner either had or did not have access to basic sociodemographic information about their opponent (and thus arguments could be personalized). We find that 64.4% of the time, personalized LLM debaters were more persuasive than humans, given that they were not equally persuasive (81.2% relative increase in the odds of higher post-debate agreement; p < 0.01; N=900). Without personalization, GPT-4 still outperformed humans, but the effect was lower and not statistically significant (p=0.30). Further, our analysis suggests that LLMs use different strategies from human debaters: their texts are harder to read and have more markers associated with logical and analytical reasoning. Overall, our results suggest that concerns around LLM-based persuasion are meaningful and have important implications for social media governance and the design of new online environments.

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Section: Should Every Citizen Receive a Basic Income From The Governm...supporting

confidence: 92%

On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trial

Salvi,

Ribeiro,

Gallotti

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…In contrast, Guess and Coppock (2020) run a series of survey experiments exposing participants to opposing views but find no evidence of "backfire" effects. A recent study by Spitz et al (2021) that focuses on the identity of the source of opposing views provides some insights on when "backfire" effects are more likely to occur.…”

Section: Randomized Experiments To Understand Polarizationmentioning

confidence: 99%

Perspective-Taking to Reduce Affective Polarization on Social Media

Saveski

Gillani

Yuan

et al. 2022

ICWSM

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The intensification of affective polarization worldwide has raised new questions about how social media platforms might be further fracturing an already-divided public sphere. As opposed to ideological polarization, affective polarization is defined less by divergent policy preferences and more by strong negative emotions towards opposing political groups, and thus arguably poses a formidable threat to rational democratic discourse. We explore if prompting perspective-taking on social media platforms can help enhance empathy between opposing groups as a first step towards reducing affective polarization. Specifically, we deploy a randomized field experiment through a browser extension to 1,611 participants on Twitter, which enables participants to randomly replace their feeds with those belonging to accounts whose political views either agree with or diverge from their own. We find that simply exposing participants to "outgroup" feeds enhances engagement, but not an understanding of why others hold their political views. On the other hand, framing the experience in familiar, empathic terms by prompting participants to recall a disagreement with a friend does not affect engagement, but does increase their ability to understand opposing views. Our findings illustrate how social media platforms might take simple steps that align with business objectives to reduce affective polarization.

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“…The results obtained in [16] showed confirmation bias in combination with the backfire effect to be strong drivers of polarization. The authors in [133] showed evidence of polarization development in another combination of PSOC constructs: parasocial relationships and HOMY. It was shown that people became further entrenched in their preconceived beliefs in the case of a contradicting opinion from a subject matter expert celebrity.…”

Section: Polarization Dimension As Shown In Tablementioning

confidence: 99%

Online Fake News Opinion Spread and Belief Change: A Systematic Review

Altoe,

Moreira,

Pinto

et al. 2024

Human Behavior and Emerging Technologies

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Fake news has been linked to the rise of psychological disorders, the increased disbelief in science, and the erosion of democracy and freedom of speech. Online social networks are arguably the main vehicle of fake news spread. Educating online users with explanations is one way of preventing this spread. Understanding how online belief is formed and changed may offer a roadmap for such education. The literature includes surveys addressing online opinion formation and polarization; however, they usually address a single domain, such as politics, online marketing, health, and education, and do not make online belief change their primary focus. Unlike other studies, this work is the first to present a cross-domain systematic literature review of user studies, methodologies, and opinion model dimensions. It also includes the orthogonal polarization dimension, focusing on online belief change. We include peer-reviewed works published in 2020 and later found in four relevant scientific databases, excluding theoretical publications that did not offer validation through dataset experimentation or simulation. Bibliometric networks were constructed for better visualization, leading to the organization of the papers that passed the review criteria into a comprehensive taxonomy. Our findings show that a person’s individuality is the most significant influential force in online belief change. We show that online arguments that balance facts with emotionally evoking content are more efficient in changing their beliefs. Polarization was shown to be cross-correlated among multiple subjects, with politics being the central polarization pole. Polarized online networks start as networks with high opinion segregation, evolve into subnetworks of consensus, and achieve polarization around social network influencers. Trust in the information source was demonstrated to be the chief psychological construct that drives online users to polarization. This shows that changing the beliefs of influencers may create a positive snowball effect in changing the beliefs of polarized online social network users. These findings lay the groundwork for further research on using personalized explanations to reduce the harmful effects of online fake news on social networks.

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Interventions for Softening Can Lead to Hardening of Opinions: Evidence from a Randomized Controlled Trial

Cited by 5 publications

References 54 publications

On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trial

On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trial

Perspective-Taking to Reduce Affective Polarization on Social Media

Online Fake News Opinion Spread and Belief Change: A Systematic Review

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