Inferring Mental Burnout Discourse Across R eddit Communities (2024)

Abstract: Mental burnout refers to a psychological syndrome induced by chronic stress that negatively impacts the emotional and physical well-being of individuals. From the occupational context to personal hobbies, burnout is pervasive across domains and therefore affects the morale and productivity of society as a whole. Currently, no linguistic resources are available for the analysis or detection of burnout language. We address this gap by introducing a dataset annotated for burnout language. Given that social media is a platform for sharing life experiences and mental health struggles, our work examines the manifestation of burnout language in Reddit posts. We introduce a contextual word sense disambiguation approach to identify the specific meaning or context in which the word “burnout” is used, distinguishing between its application in mental health (e.g., job-related stress leading to burnout) and non-mental health contexts (e.g., engine burnout in a mechanical context). We create a dataset of 2,330 manually labeled Reddit posts for this task, as well as annotating the reason the poster associates with their burnout (e.g., professional, personal, non-traditional). We train machine learning models on this dataset achieving a minimum F1 score of 0.84 on the different tasks. We make our dataset of annotated Reddit post IDs publicly available to help advance future research in this field.

## introduction as a response to chronic interpersonal stressors, burnout syndromes develop through latent psychological erosion. at the individual level, burnout manifests in cardiovascular, mental, and physical problems, such as headaches, chronic fatigue, gastrointestinal disorders, and more @xcite . within workplaces, burnout can create interpersonal conflict and disrupt team productivity, resulting in the propagation of burnout and a greater rate of job turnover @xcite . by conservative estimates, burnout among physicians alone costs the united states $4.6 billion annually @xcite . considering the negative affects of burnout, the world health organization has recognized it as an occupational phenomenon in the international classification of diseases (icd-11) @xcite . although it was initially believed that burnout only occurs in human-centered jobs @xcite , it has since been shown to develop among all professional @xcite , non-professional (e.g., parental @xcite , esports athlete @xcite ) and historically marginalized groups (e.g., individuals with autism @xcite ). while questionnaires (e.g., maslach burnout inventory (mbi) @xcite )) are commonly used for the detection of burnout, they have limitations in terms of accessibility and scalability. thus the lack of large-scale studies on burnout is a gap in the literature. we address this gap by creating a linguistic resource for the study of burnout. developing linguistic resources for the language of burnout is crucial for several reasons. firstly, it enables the creation of more accurate nlp models that can identify and assess burnoutrelated language, facilitating early detection and intervention, which in turn provides better support for those experiencing burnout. additionally, these resources allow for a deeper analysis of how burnout is discussed across different contexts helping to understand its broader impact. by supporting cross-disciplinary research, linguistic resources also bridge fields like psychology, medicine, and occupational studies, leading to more comprehensive insights into burnout. linguistic resources allow for the systematic collection and analysis of burnout-related language, leading to data-driven insights. these insights can inform public policy, workplace practices, and mental health in-terventions, ultimately contributing to better societal outcomes. however, if we attempt to collect data using burnout-related keywords, the resulting dataset will likely include instances from both mental health contexts (e.g., emotional exhaustion in the workplace) and non-mental health contexts (e.g., burnout in a physical or mechanical sense), due to the term's varied usage across different domains. this emphasizes the need for effective word sense disambiguation to accurately interpret the context in which "burnout" is used. our paper leverages discourse on reddit surrounding mental burnout to infer instances of selfdisclosure of burnout and the context they occur in. we collect reddit posts across all subreddits that contain burnout-related keywords 1 for a nine year period @xcite @xcite @xcite @xcite @xcite . after cleaning 2 , our dataset consists of 297,623 posts from across 23,519 subreddits. we then annotate 2,330 randomly sampled posts for disclosure and context 3 of burnout. 4 following the annotation of posts, we build and evaluate models to detect whether burnout keywords are used in a mental health setting, and the context of burnout. our best models for both classification tasks achieve an f1 score of 0.84 or higher. our dataset will be made publicly available for use by the research community. 5 deploying our trained models on the full reddit collection, we observe an overall increase in the online disclosure of burnout. we also find that burnout attributed to issues outside of work makes up a considerable portion of the online discourse, indicating a need for more studies in non-professional settings. ## related work burnout is defined by its three dimensions of exhaustion, cynicism, and professional inefficacy @xcite . while majority of psychological studies have focused on occupational burnout, emerging research has also evaluated burnout in other populations such as parents @xcite . to the best of our knowledge, only one more study has investigated burnout using social media data @xcite . studying posts by 1,532 burnt-out weibo users, @xcite predicted 1 "burnout", "burn out", "burnt out", and "burned out" 2 explained in appendix a. 1. 3 classes: professional, personal, non-traditional 4 definitions of both tasks are provided in closely related to our work, saha and de choudhury (2017), @xcite , and cascalheira, hamdi, scheer, saha, boubrahimi, and choudhury (2022) assess the self-disclosure of stress to develop a greater understanding of stigmatized topics within online discourse. more specifically, @xcite developed a machine learning classifier to scalably identify social media posts describing minority stress experiences, achieving an auc of 0.80. @xcite is another example which examined expressions of stress from survivors of gun violence on college campuses within online discourse. our work contributes to the body of research on inferring mental health from language by developing a dataset and models for the detection of burnout and its context. although chronic stress precedes and contributes to burnout syndrome @xcite , we note that the general form of burnout is defined as a psychological reaction triggered by perceived environmental demands, while stress is more unspecific in origin. prior work has specifically cautioned against mixing stress and burnout @xcite . ## methodology data collection & cleaning. the social media platform reddit affords users a degree of pseudoanonymity and allows longer posts relative to other platforms. as a result, individuals from around the world often use reddit as a medium for discussing sensitive topics, such as mental health @xcite . with these considerations in mind, we collected all posts from reddit written in english from january 1, 2014 to june 26, 2022 that contain at least one burnout-related keyword through the pushshift api @xcite and google bigquery to use as our dataset foot_0 . we used different lexical variations of the term burnout (i.e., "burnout", "burnt out", "burned out", and "burn out") to collect our dataset. the initial collection included 379,371 posts. we performed a round of cleaning on the dataset, the details of which are explained in appendix a.1. after cleaning, our dataset is comprised of 297,623 posts. these posts are written by 241,392 unique accounts across 23,519 subreddits. qualitative data annotation. after a qualitative inspection of the dataset, we found that many posts utilized a burnout keyword in a manner that is irrelevant to our mental burnout. for instance, posts used burnout-related keywords to describe electrical hardware damage. to distinguish between these different use cases, we employed a systematic annotation task, with the goal of distinguishing between mental burnout and non-mental burnout discourse. two members of the research team labeled 2,330 randomly sampled posts. our corpus size is in line with prior textual mental health corpora foot_1 . 1,455 posts (62.4%) were annotated as discussing burnout in a mental health context. having identified posts that discussed mental burnout, we wanted to understand the context in which each individual was experiencing burnout. in other words, we annotated the positively labeled posts (i.e., posts discussing mental burnout) with one of the three context classes: personal, professional, and non-traditional ## results to study burnout at scale we train nlp models on our annotated dataset. the model that achieved the best performance in both cases was the fine-tuned distilbert-base @xcite model (@xmath0.86 and 0.84). we then applied this model to the entire dataset to label the 297,623 posts we had collected from reddit. the posts were automatically labeled to indicate whether the use of burnout was to discuss mental health issues. 185,129 (62%) posts were identified as using burnout in a mental health related context. we visualize the trend of the number of posts we further labeled our collection with the context burnout occurred in. the number of posts classified into each context is displayed in ## conclusion in this paper, we examined burnout language through computational techniques for the detection and characterization of reddit posts containing burnout-related keywords. our dataset consisted of 297,623 reddit posts with at least one occurrence of a burnout-related keyword collected from across 23,519 different subreddits. following the annotation process, our best classification models perform at an minimum f1 of 0.84. we showcase high-level trends of burnout in our nine-year collection of online discourse on reddit. implications & future work. through our work, we developed a burnout word sense disambiguation model. this model could be utilized for personalized interventions, public health monitoring, and policy development. for instance, these models could be used to provide early intervention for individuals experiencing mental burnout. by identifying relevant posts, support systems can reach out to those in need, offering assistance and resources. additionally, the model can assist the research community in gaining insights into the prevalence and trends of burnout in various contexts. our results demonstrated a growth in the number of individuals struggling with burnout over time. the increasing prevalence of this psychological phenomenon signals a need for a deeper understanding of the causes and manifestations of this issue at scale, which our classification model could assist in. future work could look into identifying and characterizing the language of exhaustion, cynicism, and inefficacy which are the dimensions of mental burnout. they could also examine how the manifestation of these dimensions differ across contexts. limitations. our dataset is limited to reddit posts with self-identified and ecologically valid self-expressions of burnout. while we believe this data is valuable to analyze, this limitation could influence the effectiveness of our models on textual instances in which the author does not explicitly disclose burnout. for instance, posts that discuss signs and symptoms of burnout without explicitly using the term "burnout" would not be detectable using our models. moreover, not all individuals have the same level of comfort when it comes to sharing their mental health struggles online. as a result, the analyzed discourse could be more representative of this subset of the public. additionally, our approach and classification models should not be used to make diagnostic claims, as questionnaires, such as the maslach burnout inventory (mbi), are the only clinically validated means of assessing burnout. rather, our work seeks to broadly capture burnout discourse across different domains. finally, it is worth noting that social media platforms can act as "echo chambers" @xcite , where specific viewpoints could be amplified. it is important to keep this effect in mind when analyzing experiences of burnout using social media data.