Understanding Social Support Expressed in a COVID -19 Online Forum (2021)

Abstract: In online forums focused on health and wellbeing, individuals tend to seek and give the following social support: emotional and informational support. Understanding the expressions of these social supports in an online COVID- 19 forum is important for: (a) the forum and its members to provide the right type of support to individuals and (b) determining the long term effects of the COVID-19 pandemic on the well-being of the public, thereby informing interventions. In this work, we build four machine learning models to measure the extent of the following social supports expressed in each post in a COVID-19 online forum: (a) emotional support given (b) emotional support sought (c) informational support given, and (d) informational support sought. Using these models, we aim to: (i) determine if there is a correlation between the different social supports expressed in posts e.g. when members of the forum give emotional support in posts, do they also tend to give or seek informational support in the same post? (ii) determine how these social supports sought and given changes over time in published posts. We find that (i) there is a positive correlation between the informational support given in posts and the emotional support given and emotional support sought, respectively, in these posts and (ii) over time, users tended to seek more emotional support and give less emotional support.

## introduction globally, millions of individuals have contracted covid-19 and more than 2 million people have died from the pandemic as of january 2021 foot_0 . individuals are turning to online forums focused on discussions around covid-19 to seek and give support @xcite . in online health and well-being forums, individuals tend to seek and give two forms of social support: emotional and informational support @xcite ; where: (a) emotional support sought seeks understanding, affirmation and encouragement, (b) emotional support given includes providing encouragement, (c) informational support sought seeks advice or information, and (d) informational support given provides advice and information. below are examples (rephrased) of posts that express these social supports in a covid-19 related online forum: in online forums, some posts may express multiple social supports; hence, understanding if there is a correlation between multiple social supports expressed in posts is important for better understanding the social support needs of members of the forum. we conduct an analysis to determine the correlation between different social supports expressed in posts published in the covid-19 online forum. these social supports sought or given may change over time. understanding how these social support needs change over time in a covid-19 online forum is important for several reasons; first, it can help the forum moderators and members provide adequate and meaningful support to users, and secondly, it can provide insights about the long term effects of the pandemic on the well-being of the public, thereby providing the necessary authorities with sufficient and timely information on the changes in social support needs of the public so that they can make resources available to address these needs. we carry out an analysis to determine the gaps in social support supply and demand in posts published on an online covid-19 forum over time. we conduct additional analysis to identify different time periods in which there are clear differences in the types of social support sought or given and determine language feature differences in posts published in these different time periods. ## related work a lot of prior work has been done to gain insights and respond to the covid-19 pandemic. in order to identify treatments and gain insights about covid-19, the allen institute for ai in partnership with the white house and other collaborators, created a resource to use various artificial intelligence and natural language techniques to access thousands of research papers and articles related to covid-19 to retrieve pertinent information about published research work about covid-19 @xcite . using this resource, several research work has been done: (i) in @xcite ), an automated system was built in which given a query, it retrieves relevant publications related to the query and ranks them, (ii) in @xcite , a system was developed to help medical researchers easily find research articles relevant to @xcite in @xcite , a system was built which identifies the relationships between researchers working on covid-19 related projects and their networks with the aim to help researchers interested in covid-19 related projects to easily identify potential collaborators and research ideas to investigate. in @xcite , a dataset of more than 1,500 questions from several sources were obtained and annotated to different categories and a model was built to classify questions into these categories. a resource made up of a dashboard was created by researchers at johns hopkins university for tracking the number of cases, information about contact tracing, testing, and information about work on the vaccine for covid-19 foot_1 . in @xcite , using twitter data, a dashboard was created to track (over time) the changes in language use (related to mental health and covid-19 symptoms), in different states in the united states. similarly, the world health organisation created a dashboard for monitoring the number of covid-19 cases globally foot_2 . some prior work used social media data to gain insights into peoples reactions to the covid-19 pandemic. posts from a reddit forum focused on discussions around covid-19 were analyzed to understand the patterns in topics discussed and how they change over time in the forum @xcite . in @xcite , two reddit forums focused on discussions around covid-19 were analyzed to gain insights about peoples responses to the pandemic. in @xcite , twitter data related to covid-19 events such as positive and negative covid-19 tests were labelled and models were developed to automatically identify these events. in @xcite , a multilingual covid-19 twitter dataset was created with the aim to aid researchers better understand what people are conversing about on social media, as it relates to the pandemic and enable researchers study covid-19 related misinformation. in (ayers et al., 2020), internet searches during the time period of the pandemic were studied and it was determined that peoples expression of anxiety increased. in @xcite , a method was created for detecting covid-19 related misinformation in youtube videos by using the comments to the videos. in @xcite , the differences in language use in covid-19 related reddit discussions between female and male users was studied. the main difference between our work and prior work is that in this work we build 4 machine learning models to measure different social supports expressed in posts in an online covid-19 forum with the aim to better understand the types of social support people were expressing in these posts and how they changed over time. ## dataset our dataset consists of posts published on a reddit forum, /r/coronavirus -which is focused on discussions around covid-19 and is the covid-19 subreddit with the most number of members (i.e. 2.4 million members as of january 2021). specifically, we collected 64,074 posts published daily in the "daily discussion post" thread in the /r/coronavirus subreddit between march 3 and april 30 2020 @xcite . ## social support the importance of social support expressed in online health forums has been demonstrated @xcite . similar to prior work @xcite , we measure the extent of social support expressed in posts. we had 3 medical students rate a randomly selected sample of 1,000 posts from our dataset; we extracted the following features from each post @xcite : ## lda we identified topics associated with posts published between march 03 2020 -march 31 2020 and april 01 -april 30 2020 in our datast by generating 20 lda topics @xcite from posts in our dataset; we tried different numbers of top-ics and 20 topics obtained the most coherent topic themes when examined by one of the co-authors. tables 10 and 11 show the lda topics associated with posts published in march and april, respectively. ## liwc in this section, using liwc @xcite , we identify the liwc categories associated with posts published in march compared to those published in april in our dataset. tables 12 and 13 show the liwc categories associated with posts published in march and april. ## discussion and future work this work has several findings; in this section we summarize the results from our analysis. we find that in posts from our dataset, the emotional support given has a positive correlation with the informational support given; the same applies to the emotional support sought. this implies that when expressing emotional support (giving or seeking), users tend to give informational support as well. knowing this is important in that it can help the forum moderators and members of the forum ## conclusion in this work, we build four machine learning models to measure the extent of the emotional support given, emotional support sought, informational support given, and informational support sought, in posts published on an online forum focused on discussions around covid-19. we carried out analyses to: (i) determine the correlations between these social supports expressed in posts, (2) determine how these social supports change over time, and (3) using language features, we conducted an analysis to gain insights as to why there was a change in the social supports expressed. the findings from this work show that data from social media can help gauge how people are feeling and responding to the covid-19 pandemic and track and understand the social support needs of people as it relates to the pandemic. potentially, these findings can help public health agencies and other agencies design and implement online interventions around covid-19.