Overview
A dual-objective optimization method has been developed to enhance the credibility of news content within social media feeds by modifying existing content rankings. This method aims to minimize the Spearman's footrule distance to the original ranking, thereby preserving the initial content sequence, while simultaneously incorporating a linear cost objective to elevate the expected credibility of the content feed. A semi-automated pipeline for assigning credibility scores is integrated, leveraging a combination of retrieval-augmented score assignments and human-generated fact-checks.
Research Context
Social media platforms frequently present users with misinformative or misleading content, which can reduce the perceived credibility of content feeds. The research addresses this issue by proposing an optimization-based approach to refine content rankings. The objective is to produce content feeds that are more credible while maintaining a similarity to the initial ranking order.
Approach
The methodology consists of two primary components: an optimization-based re-ranking technique and a semi-automated content credibility detection pipeline.
Optimization-Based Re-ranking
The re-ranking process is formulated as a dual-objective optimization problem. This optimization seeks to achieve two goals concurrently:
- Minimize the Spearman's footrule distance from the re-ranked output to the original content ranking. This objective is designed to preserve the initial ordering of content, ensuring that the re-ranking does not drastically alter the feed's structure.
- Maximize the expected credibility of the content within the feed. This is achieved through an additional linear cost objective that biases the ranking towards more credible content.
Semi-Automated Content Credibility Detection
A robust semi-automated pipeline has been developed for assigning credibility scores to individual pieces of content. This pipeline incorporates two mechanisms:
- Retrieval-augmented score assignments: This component uses retrieval-augmented generation (RAG) to assign scores.
- Human-generated fact-checks: Human-generated labels and fact-checks are utilized to ground the credibility assignments. This integration allows the algorithm to extend to posts that may have limited or no human-generated labels, while still benefiting from human insights.
Experimental Setup
The proposed approach was evaluated through an experimental setup using real-world data. This data was collected from the X (Twitter) platform. Within this setup, credibility scores were assigned through a combination of user-generated community notes and retrieval-augmented generation.
Findings
The experimental evaluation indicated that the developed method produces results where deviations from the Pareto optimal front are at most 7% for both optimization objectives. This observation is based on scenarios where initial ranking values are known. The algorithm's design also allows for the integration of various measures for source credibility, suggesting its applicability across different social media platforms.
Potential Applications
The flexibility in incorporating diverse source credibility measures indicates that this algorithm could be applied across various social media platforms. Its ability to refine existing content rankings while incorporating credibility scores could be used to improve the overall information quality presented to users on such platforms.
Key Limitations Mentioned by Researchers
The source does not explicitly mention any key limitations of the research.