Exploration of Gender Differences in COVID-19 Discourse on R eddit (2020)

Abstract: Decades of research on differences in the language of men and women have established postulates about the nature of lexical, topical, and emotional preferences between the two genders, along with their sociological underpinnings. Using a novel dataset of male and female linguistic productions collected from the Reddit discussion platform, we further confirm existing assumptions about gender-linked affective distinctions, and demonstrate that these distinctions are amplified in social media postings involving emotionally-charged discourse related to COVID-19. Our analysis also confirms considerable differences in topical preferences between male and female authors in pandemic-related discussions.

## introduction research on gender differences in language has a long history spanning psychology, gender studies, sociolinguistics, and, more recently, computational linguistics. a considerable body of linguistic studies highlights the differences between the language of men and women in topical, lexical, and syntactic aspects @xcite , and such differences have proven to be accurately detectable by automatic classification tools @xcite @xcite . here, we study the differences in male (m) and female (f) language in discussions of covid-19 foot_0 on the reddit foot_1 discussion platform. responses to the virus on social media have been heavily emotionally-charged, accompanied by feelings of anxiety, grief, and fear, and have discussed far-ranging concerns regarding personal and public health, the economy, and social aspects of life. in this work, we explore how established emotional and topical cross-gender differences are carried over into this pandemic-related discourse. insights regrading these distinctions will advance our understanding of gender-linked linguistic traits, and may further help to inform public policy and communications around the pandemic. research has considered the emotional content of social media on the topic of the covid pandemic (e.g., @xcite ), but little work has looked specifically at the impact of gender on affective expression (van der vegt and @xcite . gender-linked linguistic distinctions across emotional dimensions have been a subject of prolific research @xcite @xcite , with findings suggesting that women are more likely than men to express positive emotions, while men exhibit higher tendency to dominance, engagement, and control (although see @xcite for an alternative finding). van der vegt and kleinberg (2020) compared the self-reported emotional state of male vs. female crowdsourced workers who contributed to the real world worry dataset (rwwd, @xcite , in which they were also asked to write about their feelings around covid. however, because van der vegt and @xcite restricted the affective analysis to the workers' emotional ratings, it remains an open question regarding whether, and how, the natural linguistic productions of males and females about covid will exhibit detectably different patterns of emotion. topical analysis of social media during the pandemic has also been a focus of recent work (e.g., @xcite , again with few studies devoted to gender differences @xcite . much prior work has found distinctions in topical preferences in spontaneous productions of the two genders (e.g., @xcite @xcite , showing that men were more likely to discuss money-and occupation-related topics, focused on objects and impersonal matters, while women preferred discussion on family and social life, topics related to psychological and social processes. in the recent context, @xcite found these observations persisted in covid-19 tweets, with a male focus on sports and politics, and female focus on family and caring. in the prompted texts of the rwwd, van der vegt and kleinberg (2020) also found the expected m vs. f topical differences, with men talking more about the international impact of the pandemic, as well as governmental policy, and women more commonly discussing social aspects -family, friends, and solidarity. moreover, van der vegt and kleinberg (2020) further found differences between the elicited short (tweet-sized) and longer essays, revealing the impact of the goal and size of the text on such analyses. again, an open question remains concerning the topical distinctions between m and f authors in spontaneous productions without artificial restrictions on length. here, we aim to address the above gaps in the literature, by performing a comprehensive analysis of the similarities and differences between male and female language collected from the reddit discussion platform. our main corpus is a large collection of spontaneous covid-related utterances by (selfreported) m and f authors. importantly, we also collect productions on a wide variety of topics by the same set of authors as a 'baseline' dataset. first, using a multidimensional affective framework from psychology (bradley and lang, 1994), we draw on a recently-released dataset of human affective ratings of words @xcite to support the emotional assessment of male and female posts in our datasets. through this approach, we corroborate existing assumptions on differences in the emotional aspects of linguistic productions of men and women in the covid corpus. moreover, our use of a baseline dataset enables us to further show that these distinctions are amplified in the emotionallyintensive setting of covid discussions compared to productions on other topics. second, we take a topic modeling approach to demonstrate detectable distinctions in the range of topics discussed by the two genders in our covid corpus, reinforcing (to some extent) assumptions on gender-related topical preferences, in this natural discourse in an emotionally-charged context. 3 as noted above, our goal is to analyze emotions and topics in spontaneous utterances that are relatively unconstrained by length. to that end, our main dataset comprises a large collection of spontaneous, covid-related english utterances by male and female authors from the reddit discussion platforms. as of may 2020, reddit had over 430m active users, 1.2m topical threads (subreddits), and over 70% of its user base coming from englishspeaking countries. subreddits often encourage their subscribers to specify a meta-property (called a 'flair', a textual tag), projecting a small glimpse about themselves (e.g., political association, country of origin, age), thereby customizing their presence within a subreddit. we identified a set of subreddits, such as 'r/askmen' and 'r/askwomen', where authors commonly self-report their gender, and extracted a set of unique user-ids of authors who specified male or female gender as a flair. 4 this process yielded the user-ids for 10, 421 males and 5, 630 females (as self-reported). using this extracted set of ids, we collected covid-related submissions and comments foot_4 from across the reddit discussion platform for a period of 15 weeks, from february 1st through june 1st. covid-related posts were identified as those containing one or more of a set of predefined keywords: 'covid', 'covid-19', 'covid19', 'corona', 'coronavirus', 'the virus', 'pandemic'. this process resulted in over 70k male and 35k female posts spanning 7, 583 topical threads; the male subcorpus contains 5.3m tokens and the female subcorpus 2.8m tokens. figure 1 presents the weekly amount of covid-related posts in the combined corpus, showing a peak in early-mid march (weeks 5-6). aiming at a comparative analysis between virusrelated and 'neutral' (baseline) linguistic productions by men and women, we collected an additional dataset comprising a randomly sampled 10k posts per week by the same set of authors, totalling 150k posts for each gender. the baseline dataset contains 6.8m tokens in the male subcorpus and 5.3m tokens in the female subcorpus. we use our covid and baseline datasets for analysis of emotional differences as well as topical preferences in spontaneous productions by male and female authors on reddit. the ample size of the corpora facilitates analysis of distinctions in these two aspects between the two genders in their discourse on the pandemic, and as compared to non-covid discussion. ## analysis of topical distinctions we study topical distinctions in male vs. female covid-related discussions with two complementary analyses: (1) comparison of topics found by topic modelling over each of the m and f subcorpora separately, and (2) comparison of the distribution of dominant topics in m vs. f posts as derived from a topic model over the entire m+f dataset. for each analysis, we used a publicly-available topic modeling tool @xcite . each topic is represented by a probability distribution over the entire vocabulary, where terms more characteristic of a topic are assigned a higher probability. 9 a common way to evaluate a topic learned from a set of documents is by computing its coherence score -a measure reflecting its overall quality @xcite . we assess the quality of a learned model by averaging the scores of its individual topics -the model coherence score. ## conclusions a large body of studies spanning a range of disciplines has suggested (and corroborated) assumptions regarding the differences in linguistic productions of male and female speakers. using a large dataset of covid-related utterances by men and women on the reddit discussion platforms, we show clear distinctions along emotional dimensions between the two genders, and demonstrate that these differences are amplified in emotionallyintensive discourse on the pandemic. our analysis of topic modeling further highlights distinctions in topical preferences between men and women.