Towards Understanding the Role of Gender in Deploying Social Media-Based Mental Health Surveillance Models (2021)

Abstract: Spurred by advances in machine learning and natural language processing, developing social media-based mental health surveillance models has received substantial recent attention. For these models to be maximally useful, it is necessary to understand how they perform on various subgroups, especially those defined in terms of protected characteristics. In this paper we study the relationship between user demographics – focusing on gender – and depression. Considering a population of Reddit users with known genders and depression statuses, we analyze the degree to which depression predictions are subject to biases along gender lines using domain-informed classifiers. We then study our models’ parameters to gain qualitative insight into the differences in posting behavior across genders.

## introduction the united states centers for disease control and prevention estimates that 8% of american adults suffer from major depression at a given time @xcite . this represents a critical public health threat, as depression is associated with downstream physical health complications @xcite and an increased risk of suicide @xcite . among the many efforts to address this crisis is a line of research at the intersection of language modeling, social media analysis, and mental health. seminal papers by de @xcite and @xcite demonstrated the general feasibility of predicting mental health status from social media data. a major obstacle to the practical use of mental health surveillance models is differential performance for different subgroups of the population. this behavior can arise either because the training data is not sufficiently representative of the population, or because some groups are simply harder to predict given the same data. the former case is well-studied in the machine learning literature and can be addressed by careful data collection and training regimes. the latter case, however, is often more subtle and harder to address. not identifying and addressing these differences in performance degrades the utility of the models. in particular, if the performance is worse for historically marginalized populations it can reinforce existing inequities such as under-diagnosis of depression @xcite . in this work we aim to assess the scope of the differential performance problem by studying the relationship between gender and predictions of depression. the most useful insight we could gain would be determining whether or not gender is a confounder for depression predictions; that is, whether gender both causally affects the way in which users post on reddit and causally affects our predictions of the user's depression status. unfortunately, testing whether this causal dynamic is true is very difficult with the purely observational data available to us. towards testing this phenomena, we will instead test the slightly weaker hypotheses i) that depression predictions exhibit gender bias (i.e., there are differences in performance across genders) and ii) that these differences are due, at least in part, to differing uses of language between men and women in talking about their mental state. together these hypotheses serve as a sort of associational version of the causal phenomenon we'd like to study. they can tell us whether depression predictions are correlated with gender and whether certain terms are likely to have different meanings based on the gender of the author. we test hypothesis (i) quantitatively by fitting depression prediction models to a novel data set collected from reddit with ground truth genders, derived from self-disclosures, and comparing the performances across genders. we test hypothesis (ii) qualitatively by looking at features strongly predictive of depression for each gender. we identify themes that are concordant across genders and consistent with the literature @xcite as well as themes that are discordant across genders and support our hypothesis that men and women use many terms differently to talk about (non-) depression. we follow these analyses with a discussion of open questions that follow from this work. in particular, we discuss the use of causal methodologies to assess our stronger hypothesis that gender confounds depression prediction. we highlight the types of methods that could be used and the data that is necessary to test the causal hypothesis. we conclude with a discussion of limitations and the ethical implications of this work. ## related work several existing papers have considered the role of demographics in mental health prediction. @xcite demonstrated that demographics are implicitly encoded in text data. @xcite and @xcite both studied differing language use across cultures. the former used a twitter data set with inferred demographic labels, while the latter used a carefully-curated proprietary data set from 7 cups of tea. @xcite explored the role of cohort selection in assessing mental health disorder prevalence. @xcite is the closest to the present work. the authors characterized the biases present in depression prediction models by showing there are differences in performance for different demographic subgroups. this work studied biases that arise due to the specific data set used for training,focusing on the popular, publicly available data sets clpsych @xcite and multitask @xcite . the present work differs from those cited in that we seek to quantify demographic bias in depression prediction using self-disclosures in a publicly available data set. this approach improves scalability and reproducibility compared to hand-labeled and proprietary data sets. additionally, while self-disclosures are not perfect, they are not subject to the same degree of noise and error that is induced when using genders inferred by using a pre-trained model, trained on an auxiliary data source. our estimates of the depression prediction performance across genders are therefore likely to be of a higher quality. moreover, our analyses of features that are predictive of depression for each gender are also likely to be less noisy than they would be if we were also inferring genders from those same features. ## data collection to obtain a dataset with ground truth gender, we mined all posts and comments from the r/askmen and r/askwomen subreddits between january 1, 2019 and december 31, 2019 using the pushshift api @xcite . in total, we collected 251,487 original submissions and 4,481,354 comments. for each post, we consider the flair -an optional tag users can apply to their posts to reveal information about themselves or the content of their post -to determine the ground truth gender of the post author. we considered the author of a post to be true-male if they used one of 'male', 'male', 'dude', or ♂ for their flair, and truefemale if they used one of 'female', 'female', ♀, or ♀♥. of the mined posts, 1,002,079 had some sort of flair, while 660,684 had one of the male or female indicator flairs. this process yielded a data set of 15,140 unique male and 11,241 unique female users, as well as 59 users whose gender-related flair use was inconsistent (i.e. at least one post each with a male-and female-indicating flair). while people who identify as non-binary are known to have higher rates of depression @xcite and thus could benefit from the studies like this one, we did not have a reliable method for identifying non-binary users beyond the list of inconsistent users and the sub-population in our cohort was too small to yield meaningful analysis. for the remainder of the paper we restrict attention to binary genders under the folk conception of gender @xcite . for each of the 26,381 gender-binary users, we collected the user's entire reddit posting and commenting history from january 1, 2019 to december 31, 2019, totaling 1,035,782 original submissions and 19,029,981 comments across 64,162 subreddits. following the literature on social media-driven mental health surveillance @xcite , we defined a user as true-depressed if they authored an original submission or comment in r/depression during the study period and true-control otherwise. the breakdown of gender and depression classes is 721 and 713 depressed males and females respectively, and 14,416 and 10,526 control males and females respectively. replication data for this study can be found at https://github.com/esherma/ clpsych2021_gender_and_depression and is available under a data usage agreement. ## methods we fit user-level models to predict depression status from our harvested reddit data. to enable analysis of the impact of gender as a confounder, we fit separate models on two separate data sets: a random sample of the true-men users in our data set, and a random sample of the true-women users. to reduce noise induced by 'throwaway' or 'lurker' accounts, we excluded users who made fewer than 5 posts (submissions + comments) during the study period. this decision could reduce our results' generalizability since throwaway accounts may be owned by users with separate primary accounts and post with the throwaway differently (e.g. posting more personal information). because depression is a rare outcome in our data, our initial train and test sets had very few depressed individuals (109 train, 26 test). this proved too few to draw meaningful conclusions about the role of gender in depression prediction. we therefore report the performance of our models trained on data sets constructed by performing balanced sampling from the full data. the resulting class breakdowns are: 721 and 613 depressed males and females respectively, and 820 and 712 control males and females respectively. we split each of these sampled data sets 80-20 into train and test sets, stratifying by user. we then constructed a bag-of-words (bow) vocabulary from the submissions and comments for each user in the training sets. we included 1-, 2-, and 3-grams, as well as liwc @xcite and tf-idf (jones, 1972) features. we imposed that features must be used by a minimum of 25 users to be included in the vocabulary. we also removed posts from the r/depression subreddit from each user's bow vector and filtered out terms and subreddits commonly associated with self-disclosure of mental health disorders using the smhd dataset @xcite . to model depression, we used the scikit-learn implementation of regularized logistic regression @xcite . at the end of training, we discarded all but the top 100,000 features using the pairwise mutual information criterion as an additional regularization step. ## discussion in this paper we showed that depression predictions do indeed exhibit gender bias. this was evidenced by a substantially better performance when predicting depression among males than when predicting among females. we also identified terms that are used differently between men and women, providing insight into the manifestations of depression beyond modeling dynamics.