Pareto Optimal Re-ranking with Semi-Automated Content Credibility Detection

2026-06-16

AI Summary

This work addresses the prevalence of misleading content in social media feeds, which undermines overall information credibility. The authors propose a Pareto-optimal reranking framework that simultaneously maximizes credibility enhancement while preserving fidelity to the original ranking. The approach integrates a bi-objective optimization model based on Spearman footrule distance with a semi-automated credibility scoring mechanism, which combines human fact-checking, community notes, and retrieval-augmented generation techniques. Experiments on real-world data from the X platform demonstrate that the reranked results deviate by no more than 7% from the Pareto front and support flexible adaptation to diverse credibility signals, making the framework readily applicable across different platform requirements.

Abstract

Social media posts often include misinformative or misleading content, diminishing the expected credibility of content feeds. We present an optimization-based method to improve the credibility of news content on social media feeds by refining existing content rankings. This method is based on a dual-objective optimization approach that minimizes the Spearman's footrule distance to the original ranking to maintain the original content order while incorporating an additional linear cost objective to elevate the expected credibility of the content feed. Additionally, we propose a robust semi-automated pipeline for assigning credibility scores to content based on a mixture of retrieval-augmented score assignments and human-generated fact-checks. This semi-automated pipeline helps ground the credibility assignment using human-generated labels while ensuring the algorithm extends to posts with few or no human-generated labels. We showcase our approach through an experimental setup using real-world data collected over X (Twitter), where we assign the credibility scores based on a mixture of user-generated community notes and retrieval augmented generation. The method we present leads to at most 7% deviation in both optimization objectives from the Pareto optimal front with known initial ranking values. Additionally, the algorithm allows for incorporating different measures for source credibility, making it applicable across various social media platforms.

Problem

• content credibility
• misinformation
• social media
• ranking optimization
• Pareto optimality

Innovation

• Pareto optimal re-ranking
• content credibility
• semi-automated fact-checking
• retrieval-augmented generation
• multi-objective optimization