Analysing loneliness forum posts, the comments they elicit, and the responses to these comments

TL;DR

This paper analyzes interactions on an online loneliness forum to understand what types of comments encourage posters to respond, using a dataset of posts and comments from Reddit.

Contribution

The paper introduces a dataset of online loneliness forum interactions and identifies linguistic patterns in comments that elicit responses from posters.

Findings

Comments on topics like relationships, empathy, and mental health are more likely to receive responses from posters.

Psycholinguistic features such as second person pronouns and social processes are associated with comments that elicit responses.

Responses from posters are often associated with themes of appreciation.

Abstract

In May 2023, the United States surgeon general put out an advisory suggesting that loneliness and social isolation should be tackled and prioritized like health conditions such as substance abuse. Online loneliness forums provide a platform for users experiencing loneliness to engage with, share experiences/concerns, and seek support from others. On these forums, users (posters) publish support seeking posts and other users respond by either reacting to these posts and/or writing comments in which they provide support. In some cases, the posters respond to and engage with the comments that their posts elicit; understanding these interactions between posters and the comments their posts elicit can inform helpful communication strategies on online loneliness forums and online health forums, in general. Prior work on analyzing data from online loneliness forums did not study the interactions between posters and the comments their posts elicit. To address this, we present a dataset of posts and comments published in a 4 year time period i.e. from January 1 2019 to December 31 2022 on an online loneliness forum on Reddit. This dataset consists of (a) posts, (b) all the comments associated with these posts, (c) the comments that elicited responses from the posters, and (d) the responses of the posters to these comments. With this dataset, we conduct analysis using a topic modeling algorithm called BERTopic and a psycholinguistic dictionary called Linguistic Inquiry and word count (LIWC) to gain insights and elucidate the language markers associated with comments (to posts) that elicit responses from the posters. We find that as it relates to comments that received responses from posters, the following topic themes were associated with these comments i.e. relationships, empathy, and mental health concerns and the LIWC categories on second person pronoun, social processes, and present focus, were associated with these comments. We also find that the topic themes on appreciation was associated with the responses by posters to comments their posts received. We discuss these findings in the discussion section.

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