"Hang in There": Lexical and Visual Analysis to Identify Posts Warranting Empathetic Responses

TL;DR

This paper presents a method combining lexical and visual features to identify social media posts that warrant empathetic responses, achieving 80% accuracy across diverse online platforms.

Contribution

It introduces a novel approach using handcrafted features to detect posts needing empathy, integrating both text and image analysis.

Findings

Achieved 80% accuracy in identifying posts requiring empathy

Utilized features from captions and images across multiple social media sites

Demonstrated effectiveness of combined lexical and visual analysis

Abstract

In the past few years, social media has risen as a platform where people express and share personal incidences about abuse, violence and mental health issues. There is a need to pinpoint such posts and learn the kind of response expected. For this purpose, we understand the sentiment that a personal story elicits on different posts present on different social media sites, on the topics of abuse or mental health. In this paper, we propose a method supported by hand-crafted features to judge if the post requires an empathetic response. The model is trained upon posts from various web-pages and corresponding comments, on both the captions and the images. We were able to obtain 80% accuracy in tagging posts requiring empathetic responses.