"Hang in There": Lexical and Visual Analysis to Identify Posts Warranting Empathetic Responses
This work addresses the need to automatically detect posts requiring empathetic responses on social media, which is incremental as it builds on existing sentiment and feature-based methods.
The paper tackled the problem of identifying social media posts about abuse or mental health that warrant empathetic responses by analyzing both text and images, achieving 80% accuracy in tagging such posts.
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.