[Paper Review] Mitigating the Backfire Effect Using Pacing and Leading

Qi Yang, Khizar Qureshi+1 more|arXiv (Cornell University)|Jul 31, 2020

Social Media and Politics14 references4 citations

TL;DR

This study tests persuasion strategies to reduce the backfire effect in online political discourse by using Twitter bots to expose anti-immigration users to opposing views. The most effective approach—pacing and leading with social contact (liking users' posts)—significantly reduced extreme language use, while standard arguing with contact worsened polarization.

ABSTRACT

Online social networks create echo-chambers where people are infrequently exposed to opposing opinions. Even if such exposure occurs, the persuasive effect may be minimal or nonexistent. Recent studies have shown that exposure to opposing opinions causes a backfire effect, where people become more steadfast in their original beliefs. We conducted a longitudinal field experiment on Twitter to test methods that mitigate the backfire effect while exposing people to opposing opinions. Our subjects were Twitter users with anti-immigration sentiment. The backfire effect was defined as an increase in the usage frequency of extreme anti-immigration language in the subjects' posts. We used automated Twitter accounts, or bots, to apply different treatments to the subjects. One bot posted only pro-immigration content, which we refer to as arguing. Another bot initially posted anti-immigration content, then gradually posted more pro-immigration content, which we refer to as pacing and leading. We also applied a contact treatment in conjunction with the messaging based methods, where the bots liked the subjects' posts. We found that the most effective treatment was a combination of pacing and leading with contact. The least effective treatment was arguing with contact. In fact, arguing with contact consistently showed a backfire effect relative to a control group. These findings have many limitations, but they still have important implications for the study of political polarization, the backfire effect, and persuasion in online social networks.

Motivation & Objective

• To investigate whether pacing and leading—gradually shifting from agreement to persuasion—can mitigate the backfire effect in online political discourse.
• To examine whether social contact, such as liking users’ posts, enhances persuasion effectiveness in online settings.
• To evaluate the combined impact of messaging strategies and interpersonal interaction on reducing extreme language use in anti-immigration discourse.
• To test whether in-group alignment through opinion pacing improves persuasion outcomes compared to direct argumentation.
• To explore the long-term effects of sustained, nuanced messaging on users' language patterns in social media environments.

Proposed method

• Conducted a longitudinal field experiment on Twitter using automated bots to deliver treatments to users with anti-immigration sentiment.
• Implemented 'arguing'—direct messaging with pro-immigration content—without any social interaction.
• Applied 'pacing and leading'—initially posting anti-immigration content to build rapport, then gradually shifting to pro-immigration messaging.
• Introduced 'contact'—bots liked subjects’ tweets to simulate social connection and rapport-building.
• Combined pacing and leading with contact to test synergistic effects on persuasion outcomes.
• Measured changes in extreme anti-immigration language use as a proxy for the backfire effect across four experimental phases.

Experimental results

Research questions

• RQ1 Does pacing and leading reduce the backfire effect compared to direct arguing in online political discourse?
• RQ2 Does adding social contact (e.g., liking users’ posts) enhance the effectiveness of persuasion strategies?
• RQ3 Is there a significant interaction effect between pacing and leading and contact in mitigating the backfire effect?
• RQ4 How do the effects of these treatments evolve over time across different experimental phases?
• RQ5 Can sustained, gradual messaging with social engagement reduce the use of extreme language in politically charged online discourse?

Key findings

• The combination of pacing and leading with contact (PC) was the most effective treatment, significantly reducing the use of extreme anti-immigration language in phases two and three.
• Arguing with contact (AC) consistently produced a backfire effect, increasing extreme language use compared to the control group.
• Pacing and leading without contact (P) showed moderate effectiveness, but was outperformed by the PC condition.
• Arguing without contact (A) had no significant effect on reducing extreme language use.
• In phase four, all treatments showed no significant differences from each other, suggesting no persistent long-term divergence after treatment termination.
• The backfire effect was observed across all treatments in phase four, indicating a possible rebound or normalization of extreme language use post-intervention.