Machine Learning Evaluation of the Echo-Chamber Effect in Medical Forums

TL;DR

This paper introduces a machine learning approach to evaluate the echo-chamber effect in medical forums by analyzing sentiments in discussion messages using multiple models and algorithms.

Contribution

It presents a novel framework for assessing echo-chamber effects in online medical discussions through sentiment analysis with 14 models and multiple machine learning algorithms.

Findings

Effective sentiment classification models identified

Assessment models successfully evaluated echo-chamber presence

Framework applicable to other online discussion platforms

Abstract

We propose the Echo-Chamber Effect assessment of an online forum. Sentiments perceived by the forum readers are at the core of the analysis; a complete message is the unit of the study. We build 14 models and apply those to represent discussions gathered from an online medical forum. We use four multi-class sentiment classification applications and two Machine Learning algorithms to evaluate prowess of the assessment models.