# Public Sphere 2.0: Targeted Commenting in Online News Media

## TL;DR

This paper presents a deep learning system that classifies comments by relevance to specific sections of news articles, enhancing targeted reader engagement and potentially increasing website revenue.

## Contribution

It introduces a neural network-based method for section-specific comment classification and a paragraph-wise commenting interface for online news media.

## Key findings

- Deep neural network effectively classifies comments by article section.
- Section-specific comments improve reader engagement.
- System can be integrated into news websites to enhance user interaction.

## Abstract

With the increase in online news consumption, to maximize advertisement revenue, news media websites try to attract and retain their readers on their sites. One of the most effective tools for reader engagement is commenting, where news readers post their views as comments against the news articles. Traditionally, it has been assumed that the comments are mostly made against the full article. In this work, we show that present commenting landscape is far from this assumption. Because the readers lack the time to go over an entire article, most of the comments are relevant to only particular sections of an article. In this paper, we build a system which can automatically classify comments against relevant sections of an article. To implement that, we develop a deep neural network based mechanism to find comments relevant to any section and a paragraph wise commenting interface to showcase them. We believe that such a data driven commenting system can help news websites to further increase reader engagement.