Fairness and Diversity in the Recommendation and Ranking of Participatory Media Content

TL;DR

This paper presents a model for fair and diverse recommendation of user-generated content in participatory media platforms, focusing on rural India, and evaluates its effectiveness against manual editorial processes.

Contribution

It introduces a novel model for fair and diverse content ranking in participatory media, adaptable to various platforms and contexts.

Findings

Model improves exposure of diverse viewpoints

Outperforms manual editorial processes in fairness and diversity

Applicable to low-resource, rural settings

Abstract

Online participatory media platforms that enable one-to-many communication among users, see a significant amount of user generated content and consequently face a problem of being able to recommend a subset of this content to its users. We address the problem of recommending and ranking this content such that different viewpoints about a topic get exposure in a fair and diverse manner. We build our model in the context of a voice-based participatory media platform running in rural central India, for low-income and less-literate communities, that plays audio messages in a ranked list to users over a phone call and allows them to contribute their own messages. In this paper, we describe our model and evaluate it using call-logs from the platform, to compare the fairness and diversity performance of our model with the manual editorial processes currently being followed. Our models are generic and can be adapted and applied to other participatory media platforms as well.