Machine Learning Evaluation of the Echo-Chamber Effect in Medical Forums
This addresses the issue of echo-chambers in medical forums for researchers and practitioners, but it appears incremental as it applies existing methods to a new domain without clear breakthroughs.
The study tackled the problem of assessing the echo-chamber effect in online medical forums by analyzing sentiments in complete messages, using 14 models, four multi-class sentiment classification applications, and two machine learning algorithms to evaluate the models' prowess.
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.