Mapping Long-term Causalities in Psychiatric Symptomatology and Life Events from Social Media (2024)

Abstract: Social media is a valuable data source for exploring mental health issues. However, previous studies have predominantly focused on the semantic content of these posts, overlooking the importance of their temporal attributes, as well as the evolving nature of mental disorders and symptoms.In this paper, we study the causality between psychiatric symptoms and life events, as well as among different symptoms from social media posts, which leads to better understanding of the underlying mechanisms of mental disorders. By applying these extracted causality features to tasks such as diagnosis point detection and early risk detection of depression, we notice considerable performance enhancement. This indicates that causality information extracted from social media data can boost the efficacy of mental disorder diagnosis and treatment planning.

## introduction mental health, a critical facet of overall well-being, remains a global challenge affecting individuals from diverse backgrounds @xcite . traditional methods for studying mental health have often relied on clinical assessments, surveys, and interviews @xcite , providing valuable but limited snapshots of an individual's mental state. mental disorders often manifest as long-term conditions, unfolding gradually over extended periods @xcite . this intrinsic characteristic of mental health issues presents a challenge to current clinical diagnosis and assessment methods, which predominantly concentrate on symptom duration within a narrow two-week window @xcite . such an approach may not fully capture the nuanced and evolving nature of mental illnesses, potentially overlooking the progressive development of symptoms and the broader impact on an individual's life. this discrepancy highlights the need for a more longitudinal perspective in mental health assessment, one that considers the full spectrum of symptom development and progression over time, thereby providing a more accurate and holistic view of an individual's mental health state. to this end, social media platforms serve as a valuable resource for tracking mental health, with users frequently documenting their thoughts and emotions over extended periods. in contrast to traditional clinical methods that focus on short-term symptoms, social media posts offer a continuous, candid narrative of an individual's mental state. this user-generated content provides insights into the evolving nature of mental health, capturing subtle changes and patterns over time that might be overlooked in brief clinical assessments. discussions about symptoms and life stressors on social media are closely tied to mental well-being @xcite . monitoring the evolution of users' posts over time enables a more comprehensive analysis of the origins and progression of their condi-tions, facilitating early interventions when signs of mental health issues emerge. previous studies @xcite have consistently focused on detecting mental disorders through the textual content of social media posts, neglecting the analysis of chronological attributes. while achieving high detection accuracy @xcite , we argue that a singular outcome is insufficient for a profound comprehension of mental disorder development and the relationships among various factors (e.g., psychiatric symptoms). recently, some pioneering studies @xcite began to explore the causes of mental health issues in social media posts. nevertheless, their focus remains solely on extracting direct causal relationships from the semantic information within one post @xcite , as illustrated in figure 1 . this approach can only capture limited causality, as a substantial number of long-term, latent causal relationships may not necessarily manifest within a single post. therefore, our work seeks to address these limitations by revealing latent causes behind psychiatric symptoms through a computational method encompassing users' entire posting history. building upon existing literature that indicates reciprocal influences among symptoms @xcite and the potential for stressful life events to cause symptoms @xcite , we endeavor to explore both "symptom-to-symptom" and "life-event-to-symptom" causal relationships in this research. we conduct our analysis on a large-scale dataset of reddit posts with users diagnosed with various mental disorders @xcite . our initial step involves training models to identify psychiatric symptoms and life events 1 from our text dataset. then, we employ the classical causal discovery method, propensity score matching (psm) @xcite , to unveil the causal relationships between the past symptoms or life events and the future symptoms. to illustrate the efficacy of causality unveiled from social media posts, we anchor our findings in authoritative psychiatry literature, and find that our results aligns with many clinically controlled experiments. furthermore, we 1 we identify 38 symptoms, such as depressed mood, poor memory, etc., and 11 life events, including financial challenges @xcite . the complete list is provided in appendix a. ## approach our objective is to elucidate the causal relationships between symptoms and life events, so it is imperative to respectively identify these elements from social media data. ## applications of causality in this section, we show that we can discover reliable causal relationships that can also be supported with established clinical findings (section 3.1), and how these causal features can be effectively utilized for mental disorder detection (section 3.2). ## experiments in this section, we conducted experiments to evaluate the effectiveness of applying causal relationships to two tasks: early detection of depression and diagnosis point detection, comparing the results with baseline models. ## related work in this section, we present relevant literature on causality analysis in the context of social media and mental health. causal analysis methods generally, there are two ways to establish causal relationships. the first is randomized controlled trial (rct), typ-ically applied in strict clinical settings @xcite ; the second one is observational (gianicolo et al., 2020), including methods like propensity score matching (psm) @xcite ) and regression discontinuity design (rdd) @xcite -strategies that mimic rct conditions by meticulously controlling numerous covariates. rct provides controlled and stringent conditions, ensuring a high level of internal validity. however, their drawback lies in potential limited generalizability @xcite , as the strict conditions and carefully selected participants may not fully mirror real-world diversity. moreover, rcts can be resource-intensive and ethically challenging in specific situations, constraining their feasibility for certain research inquiries. in this study, we adopt the observational method to infer causality from social media data. we do acknowledge that observational studies are weaker than rcts in making conclusive causal claims, but their validity is supported by statistical literature @xcite , and they can provide complementary advantages since the analysis is conducted in large population. ## conclusion mental disorders, situated as chronologically evolving diseases, highlight the importance of considering the entire spectrum of symptom development and progression over time. in our study, we delved into such chronological aspects of social media posts, uncovering significant causal relationships between symptoms and life events through an observational causal method, the propensity scoring matching analysis. we identified causal links among 38 symptoms and their connections to 11 life event categories. by corroborating our results on symptom-to-symptom and life event-to-symptom with existing clinical literature, we provided a direct analysis of the causal relationships identified. these findings were then applied to two practical tasks, namely the diagnosis point detection and early risk detection of depression. enhanced performance when incorporating causal features on both tasks suggested the effectiveness and necessity of long-term causing relations. our research underscores the critical importance of causal relations in understanding the complex interplay between symptoms, life events and mental disorders, thus advancing the science of mental disorder prevention and early detection.