Leveraging the Power of AI and Social Interactions to Restore Trust in Public Polls

TL;DR

This paper demonstrates that analyzing social interaction structures with AI can effectively identify ineligible participation in crowdsourced polls, thereby enhancing the credibility of social data collected via social networks.

Contribution

It introduces an AI-based graph analysis method that detects ineligible participants in social network polls without analyzing shared content, improving data reliability.

Findings

Achieved over 90% accuracy in detecting ineligible participants.

Effective across various social network datasets and participation patterns.

Structural graph features can reliably indicate dishonest behavior.

Abstract

The emergence of crowdsourced data has significantly reshaped social science, enabling extensive exploration of collective human actions, viewpoints, and societal dynamics. However, ensuring safe, fair, and reliable participation remains a persistent challenge. Traditional polling methods have seen a notable decline in engagement over recent decades, raising concerns about the credibility of collected data. Meanwhile, social and peer-to-peer networks have become increasingly widespread, but data from these platforms can suffer from credibility issues due to fraudulent or ineligible participation. In this paper, we explore how social interactions can help restore credibility in crowdsourced data collected over social networks. We present an empirical study to detect ineligible participation in a polling task through AI-based graph analysis of social interactions among imperfect participants composed of honest and dishonest actors. Our approach focuses solely on the structure of social interaction graphs, without relying on the content being shared. We simulate different levels and types of dishonest behavior among participants who attempt to propagate the task within their social networks. We conduct experiments on real-world social network datasets, using different eligibility criteria and modeling diverse participation patterns. Although structural differences in social interaction graphs introduce some performance variability, our study achieves promising results in detecting ineligibility across diverse social and behavioral profiles, with accuracy exceeding 90% in some configurations.