Personalized multi-faceted trust modeling to determine trust links in social media and its potential for misinformation management

TL;DR

This paper introduces a personalized, multi-faceted trust modeling approach using clustering to improve trust link prediction in social media, aiding misinformation detection and targeted recommendations.

Contribution

It presents a novel data-driven trust modeling framework that incorporates multiple features and user clustering for personalized trust prediction in social media.

Findings

Enhanced trust link prediction accuracy demonstrated in Yelp dataset

Clustering improves personalization and prediction quality

Potential to support misinformation management and targeted recommendations

Abstract

In this paper, we present an approach for predicting trust links between peers in social media, one that is grounded in the artificial intelligence area of multiagent trust modeling. In particular, we propose a data-driven multi-faceted trust modeling which incorporates many distinct features for a comprehensive analysis. We focus on demonstrating how clustering of similar users enables a critical new functionality: supporting more personalized, and thus more accurate predictions for users. Illustrated in a trust-aware item recommendation task, we evaluate the proposed framework in the context of a large Yelp dataset. We then discuss how improving the detection of trusted relationships in social media can assist in supporting online users in their battle against the spread of misinformation and rumours, within a social networking environment which has recently exploded in popularity. We conclude with a reflection on a particularly vulnerable user base, older adults, in order to illustrate the value of reasoning about groups of users, looking to some future directions for integrating known preferences with insights gained through data analysis.