Identifying pandemic-related stress factors from social-media posts – E ffects on students and young-adults (2020)

Abstract: The COVID-19 pandemic has thrown natural life out of gear across the globe. Strict measures are deployed to curb the spread of the virus that is causing it, and the most effective of them have been social isolation. This has led to wide-spread gloom and depression across society but more so among the young and the elderly. There are currently more than 200 million college students in 186 countries worldwide, affected due to the pandemic. The mode of education has changed suddenly, with the rapid adaptation of e-learning, whereby teaching is undertaken remotely and on digital platforms. This study presents insights gathered from social media posts that were posted by students and young adults during the COVID times. Using statistical and NLP techniques, we analyzed the behavioural issues reported by users themselves in their posts in depression related communities on Reddit. We present methodologies to systematically analyze content using linguistic techniques to find out the stress-inducing factors. Online education, losing jobs, isolation from friends and abusive families emerge as key stress factors

## introduction the outbreak of novel coronavirus disease 2019 (covid-19) has affected the day to day life of people across the world and is slowing down the global economy @xcite . along with serious health problems leading to an alarming number of fatalities over a very short time, the world is also seeing a staggering rise in covid-19 linked depression, anxiety, violence, and suicidal tendencies across different sections of * sachi.2@tcs.com † suyash.sangwan@tcs.com ‡ arnab.chatterjee4@tcs.com § lipika.dey@tcs.com the society. our study of a few communities in the reddit platform which discuss depression related issues was initiated to analyze social media content related to pandemic-induced stress markers. we chose this platform since a large number of people who suffer from depression and/or related symptoms express themselves freely here. however, during analysis, we encountered an alarmingly large number of posts that discussed student-related issues. hence we narrowed down our study to focus primarily on this community. our study revealed that this community contained high school students, college-goers as well as young adults on the verge of graduating from college, most of whom suffer from mental health issues. we intend to apply natural language processing (nlp) and text mining techniques to analyze such content and generate insights about the different types of concerns expressed by student members of these communities. several surveys have also reported a phenomenal rise in the number of depression-related cases among college students between the end of march through may 2020. a research project based at the university of michigan, boston university, and the university of california, los angeles have reported over a 5% rise in depression cases when compared with the data of last fall. suicide risks are also reported to have gone up by over 2% as per a survey taken in april by active minds-which has chapters in more than 500 colleges in the usa @xcite . another recent, survey-based study on university students in bangladesh @xcite revealed that students experienced pronounced depression and anxiety, with around 15% reportedly had moderately severe depression, while 18.1% were severely suffering from anxiety. our work differs substantially from the above since we analyze social media content and not surveys or feed-backs. social media data is informal and often much more expressive, giving us cues about possible causes along with the effects. an important aspect of social media is that it is interactive in nature -users can reply, discuss, debate, and possibly agree or disagree on opinions, which bring out rich perspective to their points of view, which is not possible in a survey that usually collects oneshot answers to queries. with social isolation and distancing from physical friends, social media also saw a huge rise in footfalls during the pandemic. in this paper, we have presented a detailed analysis of depression-related posts from specific reddit communities posted during the covid-19 period. starting with the community at large, we systematically identified content related to students or fresh graduates who have reported depression or mental health problems suffer and derived some novel insights about covid-induced stress factors. the key contributions of the paper are as follows : ## related work since the outbreak of this pandemic, researchers have been extensively studying its impact on the mental health of people. one of the studies, where researchers have examined the sentiment dynamics of people @xcite , showed that various government policies and the events did affect people's overall sentiment from positive to negative. researchers also studied that whether engaging with online community support initiatives (ocsis) generated a positive or negative mood in those who engaged with them and whether or not different reasons for this engagement play different roles @xcite . they showed that people were in a better mood after engaging with an ocsi than before. another study conducted on twitter discussions, identified popular unigrams, bigrams, salient topics and themes, and sentiments in collocated tweets @xcite . another study about the impact of covid-19 on students with disabilities or health concerns @xcite showed that people with disabilities and health concerns are more worried about classes going online than their peers without disabilities. they argued that these people need more confidence in the accessibility of the online learning tools that are becoming more prevalent in these times. a study using google trends to see the impact of lockdown on the mental health of people @xcite has shown that during lockdown there is a significant increase in google searches on boredom, loneliness, worry, and sadness. another study focuses on the things that are we depressed about during covid-19 @xcite . they trained deep models that classify tweets into emotions like anger, anticipation, disgust, fear, joy, sadness, surprise, and trust, and have also built an emoct dataset for classifying these tweets. they also applied methods to understand why the public may feel sad or fearful during these times. there is one study that highlights differences in topics concerning emotional responses and worries based on gender (van der vegt and kleinberg, 2020). they used real world worry dataset (rwwd) which is a text data collected from 2500 people in which each participant was asked to report their emotions and worries @xcite . they reported that women worried mainly about loved ones and expressed negative emotions such as anxiety and fear whereas men were more concerned with the societal impacts of the virus. a study conducted using reddit platform (murray et al., 2020) used r/covid19positive subreddit in which patients who are covid-positive talk about their experiences and struggles with the virus. while providing a perspective of the patient experience during there course of treatment, they also suggest that there is a need for other supports such as mental health support during these times. our work is focused on studying the effects of the pandemic on students who suffer from mental health issues. ## creating a focused dataset for the study since the reddit platform is well organized around communities or subreddits with a shared interest, our first objective was to identify proper communities that contained data appropriate for mental health analysis. based on the community descriptions, we found 47 subreddits whose members discuss mental health-related issues. however, many of them are not active. further, some of these groups focused on specific neurotic disorders like autism or bi-polar disease, etc. these were also not considered for our study. finally, we have considered only those posts from the depression and mental health subreddits were combined to create a focused data set. we used the pushshift api (pushshift.io, 2020), provided by the platform, to crawl the data for the period from july 2019 to april 2020. detailed statistics about the datasets are presented in the the posts themselves do not have any geotagging. however, to assess the geographical spread, we looked for location names referred to in the content itself. there are 7% of posts that have mentions of location in our data. after doing appropriate resolution of the location names, identified using the spacy named entity recognition tool, we observed that the data predominantly refers to the americas and europe, with 49% and 29% representation respectively(figure 1 ). 17% refer to asian countries and the rest are from other parts of the world. ## finding language markers for depression-related text content to determine the distinct markers, if any, for depression, we did the following. we obtained data from two popular subreddits r/askreddit and r/worldnews, where large numbers of users engage in discussing news and other contemporary topics, for the same time period to create a control set. we did a numerical and linguistic comparison of this data with the depression-related posts in the focused set. to compare the statistics, we divided the time-period into two phases -the pre-covid phase ranging from july to november 2019 while the other phase lasted from december 2019 to april 2020. for the linguistic comparison of the two sets of posts, we analyzed the relative usage of different categories words in the two data sets for the entire periods. we have combined the posts from the control set c 1 and control set c 2 and compared with the combined posts from focused set f 1 and focused set f 2 . this is done with the help of a linguis- @xcite . it uses a dictionary created by psychological experts for categorization of words (tausczik and pennebaker, 2010) into 64 categories that fall under different groups like psychological processes, personal concerns, social processes, linguistic properties, etc. liwc categories were created for and by the psychologists to assess various attributes like attentional focus, emotionality, social relationships, thinking styles, and individual differences of authors, based on text content created by them. using the liwc tool, we transform each post into a 64-dimensional vector that represents its liwc category concentrations. using these category concentrations as features, we performed the two-sample kolmogorov-smirnov (ks) test to find out the distinguishing features, if any, from the two sets. the two-sample k-s test is one of the most useful and general nonparametric methods for comparing two samples since it is sensitive to differences in both location and shape of the empirical cumulative distribution functions of the two samples. the null hypothesis for the ks test is that both groups were sampled from populations with identical distributions. the ks distance metric d is the maximum distance between the cumulative distributive functions of the corresponding features from both the sets. for each of 64 liwc category we have computed cumulative distribution in each set. denoting the cumulative distribution of focused set for k-th liwc category as f k 1,n (x) and cumulative distribution for the control set for k-th liwc category with f k 2,m (x) where n and m are the cardinalities for the two sets respectively. then the ks distance metric d is computed as for the null hypothesis to be rejected d n,@xmath0.m . we rejected the null hypothesis at level α = 0.001. our analysis revealed that the two sets significantly differed in word usage for 32 of the 64 categories. the most significant differences are summarized as follows. the above findings indicate that there are clear linguistic markers to differentiate depressionrelated posts from the general talk. the posts in the focused set are mostly about stress, healthrelated concerns, negative thoughts shared by people, which are known indicators of depression. this reinforces the fact that the chosen subreddits indeed have the right content to gather depression-related information. in the next section, we present how pandemic specific issues were identified by comparing the content from the two different phases. in order to do the comparison, first the rapid automatic keyword extraction (rake) algorithm @xcite was used to extract the significant terms from each post and thereafter use these terms to represent the content. rake is a well known key term extraction method that uses statistical analysis of words and phrases in the collection to determine the key-terms. after removing the stop words like a, an, the, etc. from the content, it detects phrase delimiters in each post. using a co-occurrence matrix of words built from the entire repository, it then assigns a score to each pair based on its relative co-occurrence frequency in the collection. the algorithm then creates a matrix of word co-occurrences and assigns a score to each word as a function of its individual occurrence and co-occurrence with other words. this is continued for all words and phrases. higher the score of a term, higher is its representation in the corpus. figure 2 shows the tag clouds generated by the key-terms of each sub-set. the lower tag-cloud clearly shows that terms like social distancing, self isolation, corona virus, etc. had significant presence in the covid-time content. since these terms occurred in conjunction with the earlier mentioned liwc categories like health, affect, etc. these can be considered as the stress inducers for this period. a significant aspect that we noticed in the covid-time content, was the presence of the terms online education, online school, online classes, assignments, etc. thus, we decided to dig deeper into this aspect. however, rather than only looking at online-education related content, we extended our analysis towards analyzing content that could be discussing issues about students, in general. this is discussed in detail in the next sub-section. ## conclusion depression and other mental health issues are persistent with many people, and the social media harbors content where users express their state of mind, discuss problems, and seek counseling. the covid-19 period presents possibly new factors that stimulate depression (and anxiety) and is the main focus of our study. specifically, we identified concerns posted by the student community within