Venue: NAACL 2024

# Mapping Long-term Causalities in Psychiatric Symptomology and Life Events from Social Media

Siyuan Chen\({}^{1}\), Meilin Wang\({}^{1}\), Minghao Lv\({}^{1}\), Zhiling Zhang\({}^{2}\), Qianqian Ju\({}^{3}\), Dejiyangla\({}^{3}\), Yujia Peng\({}^{3,4}\), Kenny Q. Zhu\({}^{5*}\), Mengyu Wu\({}^{1}\)\({}^{*}\)

###### 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.

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Footnote †: _Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)_, pages 5472–5487 June 16-21, 2024 ©2024 Association for Computational Linguistics

**Keywords**: Neural network, cognitive model,

tions, facilitating early interventions when signs of mental health issues emerge.

Previous studies (Shen and Rudzicz, 2017; Zhang et al., 2022) 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 (Chancellor and De Choudhury, 2020; Chen et al., 2023), 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 Garg et al. (2022); Saxena et al. (2023) 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 Luo et al. (2016), 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 (Agirman et al., 2021; Shah et al., 2023) and the potential for stressful life events to cause symptoms Radell et al. (2021); Funegorn et al. (2021), we endeavor to explore both "_symptom-to-symptum_" and "_life-event-to-symptum_" causal relationships in this research.

We conduct our analysis on a large-scale dataset of Reddit posts with users diagnosed with various mental disorders (Chen et al., 2023). Our initial step involves training models to identify psychiatric symptoms and life events1 from our text dataset. Then, we employ the classical causal discovery method, propensity score matching (PSM) Rosenbaum and Rubin (1983), 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 integrate these identified causal relationships as additional features into two chronological disease detection tasks, namely diagnosis point detection and early risk detection of depression. The enhanced detection performance also underscores the importance of causality. The main contributions of this work are:

Footnote 1: We identify 38 symptoms, such as depressed mood, poor memory, etc., and 11 life events, including financial challenges Noone (2017); Zhang et al. (2022). The complete list is provided in Appendix A.

* We propose to mine implicit, subtle, and long-term causal relationships between factors related to mental disorders from the enormous and evolving social media stream, which can overcome the limitation of single text extraction methods Garg et al. (2022).
* We discover various reliable _"symptom-to-symptom"_ and _"life event-to-symptom"_ causal effects with Propensity Score Matching, which can be supported by existing clinical evidence.
* We achieve a significant performance boost in the Early Risk Detection and Diagnosis Point Detection task by applying these extracted causal relationships, which further verify their reliability and efficacy.

## 2 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.

### Symptom and Life Event Identification

#### Psychiatric Symptom

Building upon prior research that thoughtfully outlined 38 psychiatric symptoms across 7 mental disorders, as well as proposed a symptom identification dataset named PsySym containing 83K annotated sentences from Reddit posts (Zhang et al., 2022). We adopt their symptom definition2 and leverage their supervised symptom identification model, trained on this annotated dataset. The model incorporates a Mental BERT-based encoder Ji et al. (2022) and a linear classifier.

Footnote 2: These symptoms (e.g., anxious mood, sleep disturbance, poor memory) are carefully extracted from DSM-5 American Psychiatric Association et al. (2013), so that there is as little semantic overlap as possible between them. We list all the symptoms in Table 6 (Appendix A).

Life EventGiven the relatively limited scope of previous research on detecting life events, we refer to the Holmes-Ra 

2017), which encompasses 43 stressful life events. While the inventory is comprehensive, the multitude of categories poses challenges for annotation and model training. Hence, we consolidate these 43 life events into 11 groups based on similarity3. Then, we annotated a life event identification dataset4 using the same procedure as Zhang et al. (2022b) and trained a supervised model on this dataset using the same model architecture (i.e., Mental BERT with linear classifier).

Footnote 3: The corresponding relationship between the original definition and our merged grouping is detailed in Appendix A.

Utilizing these two classifiers, we can deduce a 38-dimensional symptom vector and an 11-dimensional life event vector for each post, where each dimension signifies the probability of a specific symptom or life event. We present the detailed identification results of these models in Appendix C, to show that using these classifiers can help us automatically and accurately extract psychiatric symptoms and life events on Reddit corpus.

### Causality Inference

In this section, we first provide formal definition of our task, followed by the specified approach we used to extract causal relations.

#### 2.2.1 Preliminaries

Exploring causal relationships involves a primary question about:

What would the _outcome_ be if the _treatment_ is given5? Footnote 5: We use the term “treatment” in accordance with the causal inference terminology, which means a binary variable that may affect the outcome.

Therefore, if we want to find out the causal relationship between symptom \(s_{o}\) and \(s_{t}\), the question becomes "What would \(s_{o}\) (outcome) progresses if a person has \(s_{t}\) (treatment)?" Intuitively, we can measure this "progression" by calculating the difference in outcomes between the treated and untreated (i.e., control) groups. To quantify this difference and assess the causal relationship between treatment and outcome, the Average Treatment Effect (ATE) Rosenbaum and Rubin (1983) is introduced:

\[ATE=E[Y(1)-Y(0)] \tag{1}\]

Here, \(Y(0)\) represents the outcome for a unit without the treatment, and \(Y(1)\) denotes the outcome for the same unit with the treatment.

However, the relationship between \(s_{o}\) and \(s_{t}\) might be a spurious correlation rather than a causal one, induced by other variables, known as _confounders_, which are correlated with both the treatment and the outcome Feder et al. (2022). Therefore, to establish trustworthy causal relationships, it is crucial to minimize the impact of confounding effects. This can be achieved by thoughtfully selecting treated and control groups, ensuring their similarity on other attributes apart from the treatment variable.

#### 2.2.2 Propensity Score Matching

To enhance the selection of treated and control groups, we apply Propensity Score Matching (PSM) Rosenbaum and Rubin (1983), which is widely used in observational studies to reduce bias and the influence of confounding variables Imbens and Rubin (2015). The main idea of PSM is to find groups of Treatment and Control posts whose covariates are statistically similar to one another, where the former group has received treatment and the latter has not. The PSM model matches posts based on their _likelihood_ of receiving the treatment, represented as the propensity score. The PSM methodology entails two key stages:

* **Estimating Propensity Scores:** We build logistic regression model to predict a post's treatment likelihood based on their covariates vector \(X\). The estimated propensity score is given by: \[e(X)=\frac{1}{1+e^{-X\beta}}\]
* **Matching:** Then, treated and control groups are paired 1:1 based on similar propensity scores using a nearest-neighbor matching technique.

Causality between SymptomsTo measure the causality between symptom \(s_{o}\) and \(s_{t}\), we apply PSM to compute the propensity score for each post. In this process, we consider symptoms other than \(s_{o}\) and \(s_{t}\) as the covariates, ensuring that the matched Treatment and Control pairs exhibit high similarity in these other symptoms. Subsequently, the posts are classified into two groups: Treatment group and Control group, based on whether the post has referenced symptom \(s_{t}\). Then, we can measure the difference in the outcome of these two groups. For a post \(i\) mentioning symptom \(s_{t}\), if there exists another post mentioning symptom \(s_{o}\) within 

a certain time window \(w\)6, we consider outcome \(Y_{i}=1\) for this post. With matched pairs established, we estimate the average treatment effect as the difference in means of the matched pairs:

Footnote 6: For the sake of clarity, we will refer to this time window as “**causal window****”** in the following part.

\[ATE(s_{t},s_{o})=\frac{1}{N_{m}}\sum_{(i,j)}\left(Y_{i}-Y_{j}\right)\]

where \(N_{m}\) symbolizes the number of matched pairs, (\(i\), \(j\)) is a matched pair of posts.

**Causality between Life events and Symptoms** Similar to assessing causality between symptoms, we use PSM to match the Treatment group (users with posts mentioning life event \(l_{t}\)) with the Control group (users without posts mentioning \(l_{t}\)). Covariates, in this case, include other life events except for the treatment life event \(l_{t}\). The outcome symptom of a life event \(l_{t}\) is determined by whether symptom \(s_{o}\) is mentioned within the time window \(w\).

## 3 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).

### Inferred Causal Relationships

We can automatically discover various causal relationships from social media with the method mentioned above. To validate their reliability, we also conduct literature reviews, and find that many of them can be supported by existing studies.

We show some examples in Table 1. We can see that some of the conclusions are intuitive, like breakdown will increase the risk of future depression. However, others are more subtle, such as irritability can cause weight change. We then find that, according to Vanzhula et al. (2019), the relationship between eating and irritability stands out a crucial pathway influencing comorbidity between Post-Traumatic Stress Disorder (PTSD) and Eating Disorders (EDs).

To highlight discrepancies between our findings and existing literature, we further examine the top eight causal relationships with the highest ATE scores in Table 2. Among these, five relationships are supported by existing literature. Two relationships show disagreement to some extent, proposing alternative associations. An example is Seinsche et al. (2023)'s finding suggesting a connection between social anxiety and a clearer memory about distasteful social situations, which is contradict to our causal link between fear of being negatively evaluated and poor memory. Moreover, one finding got mixed results, depending on studies and samples. Overall, existing literature mostly focuses on symptom-disease causality, providing limited evidence to direct causal examinations between pairs of symptoms. Our current findings have the promise of inspiring future studies focusing on direct causal relationship examinations on pairs of symptoms.

To make the intricate relationships more intuitive, Figure 2 illustrates a visual representation of the causal relationships between life events (LEs) and symptoms, shedding light on the intricate connections within the context of anxiety. Note that previous works predominantly focus on establishing connections between diseases and life events, while the association between symptoms and LEs is less explored in existing literature. Therefore, our examination of these relationships can provide valuable insights into the intricate web of factors contributing to mental health outcomes.

Moreover, our method can not only find casual relationships qualitatively, but also measure their effects quantitatively with ATE. This enables us to incorporate these findings as numerical features for mental disorder detection algorithms.

Figure 2: Visualization results for the causality between life events and symptoms with the time window of 1 year. The size of LEs nodes reflects the number of related symptoms, and the thickness of the lines indicates the strength of the causal relationship (ATE).

 



### Causality as Feature

Given the chronological nature of causal relationships, we leverage them in two temporal tasks: Diagnosis Point Detection and Early Risk Detection, both of which involve the detection of mental disorders along a continuum. The two tasks are illustrated in Figure 3, and we briefly introduce them here:

Task 1. Diagnosis Point Detection (DPD)refers to the identification of the specific time window when a mental disorder is diagnosed in an individual according to their social medial posts. This temporal insight into the diagnosis is valuable because one's mental health state is not static. For this task, MacAvaney et al. (2018) proposed a dataset named RSDD-Time, which includes 598 manually annotated self-reported depression diagnosis Reddit posts that include temporal information about the diagnosis. The complete posting history can be found in the original RSDD dataset (Yates et al., 2017).

#### Task 2. Early Risk Detection (ERD)

aims to detect mentor disorders in early stage Losada and Crestani (2016); Zhang et al. (2022a). It can enable early interventions to half the effort and double the results. Here we focus on the ERD of Depression.

In the ERD setting, for a user \(U_{i}\) with posts \([P_{i,1},P_{i,2},...,P_{i,n}]\) in their posting history (where \(n\) is the total number of posts, and \(P_{i,j}\) is the \(j\)-th user-generated post of \(U_{i}\)), posts come one by one. Therefore, only \([P_{i,1},P_{i,2},...,P_{i,t}]\) is available to the model at the \(t\)-th time. The model can make an early prediction of \(y_{i}\) at \(t(t\leq n)\) once it is confident enough, such that the prediction can make a good tradeoff between accuracy and earliness.

The ERD task doesn't require additional temporal annotations in the dataset, as we care about earliness rather than the exact diagnosis point. Thus, experiments can be conducted using any self-reported depression diagnosis dataset, such as RSDD.

Method of applying causalityTo incorporate causal relationships into these two tasks, our primary motivation can be summarized as "constructing a more comprehensive daily symptom sequence". For user \(U\), their daily symptom sequence

Figure 3: Illustration of the DPD and ERD task. For DPD, we want the predicted time to fall in a window \(w\) of the actual diagnosis point \(T\). Prediction with in \([T-w,T+w]\) will be considered positive, otherwise negative. For ERD, we want to predict the risk of a patient as soon as possible (the lower latency, the better), while not making false alert for non-patients.

\begin{table}
\begin{tabular}{l|c|c|c|c} \hline Cause Symptom/Life Events & Result Symptom & Causal Window & ATE & Support \\ \hline Anger Irritability & Weight and appetite change & 30 & 0.686 & Vanzhuia et al. (2019) \\ Fear of gaining weight & Sleep disturbance & 30 & 0.692 & Vanzhuia et al. (2019) \\ Hyperactivity agitation & Depressed Mood & 90 & 0.761 & Boschloo et al. (2015) \\ Relationship Conflicts and Breakdown & Depressed Mood & 365 & 0.527 & Konac et al. (2021) \\ \hline \end{tabular}
\end{table}
Table 1: Example of discovered causal relationships and their corresponding literature supports.



can be denoted as \([S_{1},S_{2},...,S_{n_{d}}]\), where \(n_{d}\) is the total number of days during the posting history, and \(S_{i}\) means the symptom vectors inferred from the \(i\)-th day's user-generated posts of \(U\).

As Figure 4 shows, social media posts may not capture the entirety of an individual's symptom evolution, as users may not share their experiences at all times when symptoms occur. Therefore, the symptom sequence identified from a single post will be incomplete. However, the extracted causal relationships can serve as a universal feature to bridge the gaps in these incomplete symptom sequences. As the example shown in Figure 4, when we have obtained several "life-event-to-symptom" causal relationships (e.g., "Relationship Conflicts and Breakdown" causing "Indecisiveness" with an ATE value of 0.593), we can infer that the user is likely to experience the symptom of "Indecisiveness" even when the user posts nothing. Now we can formalize the method of applying these causal relationships, taking "symptom-to-symptom" relationships as an example.

Let \(S_{D}\) denote the original symptom vector of a user on day \(D\). The adjusted symptom vector \(S^{\prime}_{D}\), considering the causal relationship, is calculated as follows:

\[S^{\prime}_{D}=S_{D}+\frac{1}{W}C^{T}_{symp}\sum_{i=1}^{W}S_{D-i} \tag{2}\]

Here, \(C_{symp}\) represents the causal matrix, with \(C_{symp}[i|j]\) indicating the Average Treatment Effect (ATE) of the \(i\)-th symptom causing the \(j\)-th symptom. The variable \(W\) represents the time window. Therefore, the causal matrix can help us predict how much the probabilities of other symptoms will increase or decrease based on the symptom sequences \([S_{D-W},...,S_{D-1}]\) from the previous \(W\) days,

Similarly, we can adjust the original symptom vector using "life-events-to-symptom" causal relationship:

\[S^{\prime}_{D}=S_{D}+\frac{1}{W}C^{T}_{LE}\sum_{i=1}^{W}L_{D-i} \tag{3}\]

Here, \(C_{LE}\) represents the causal matrix, with \(C_{LE}[i][j]\) indicating the ATE of the \(i\)-th life event causing the \(j\)-th symptom. \(L_{D}\) denotes the life event vector on day \(D\).

## 4 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.

### Early Risk Detection of Depression (ERD) Dataset

We utilize an ERD dataset proposed by Chen et al. (2023). Users and posts were extracted from a publicly available Reddit corpus. The dataset select depression users by detection patterns which consist of two components: one that matches a self-reported diagnosis (e.g., "diagnosed with"), and another that maps relevant keywords to the depression (e.g., major depressive disorder). Control users (i.e., healthy persons) are randomly sampled from those who never posted or commented in mental health related subreddits. The dataset consists of 3,105 users with depression and 17,209 control users.

BaselineWe employ PsySymZhang et al. (2022b) as baseline, which utilizes CNN of various kernel sizes as backbone, and the inputs are extracted psychiatric symptom features the same as this work. This symptom-based baseline can outperform lots of pure-text methods including BERT-based ones Nguyen et al. (2022).

Evaluation MetricWe use the official metrics \(ERDE_{5}\) and \(ERDE_{50}\) for Early Detection task proposed by Losada and Crestani (2016). The lower \(ERDE_{5}\) and \(ERDE_{50}\), the better model performs early detection, and \(ERDE_{5}\) has a higher penalty than \(ERDE_{50}\) for late detection. Detailed introduction of these metrics can be found in Appendix E.

Experiment ResultsWe conduct three runs for each method using different seed values, and the

Figure 4: Illustration of incomplete symptom sequence in social media posts, while causal relationships can help to replenish missing symptoms.

 

results of ERD task is demonstrated in Table 3. We implement _+symp_ by adjusting users' original symptom sequences based on "symptom-to-symptom" causality, and _+symp&LE_ incorporates both types of causal relationships. Generally, we can see that the early detection result can benefit from our methods that applies causality to fulfill the incomplete symptom sequences.

For the two model variants, they perform comparably on \(ERDE_{50}\), while _+symp&LE_ perform better on the more latency-sensitive metric \(ERDE_{5}\). For different causal window size, shorter ones (e.g., 30, 90) are more effective in this task emphasizing low latency. This may be attributed to the fact that causality inferred from a shorter window size is more immediate, enabling the timely identification of potential indicators for early detection.

Case StudyTo assess the effectiveness of incorporating the causal relationships in ERD, we visualize the predicted risk score of one user before and after the inclusion of both two type of causal relationships in Figure 5. It clearly demonstrates that after incorporation the causal relations, the predicted risk score surpass the detection threshold (0.5) at an earlier stage compared to the baseline model, which means the model can recognize the depression risk earlier. The reason is that the proposed method can recognize indicative life events before symptom manifestations. For example, before point A, the user posted:

"Recently, I left my retail job."

which matches the "_Work and Career Challenges_" life event. The LE-symptom causal relationship will indicate higher risk of depression in the future, facilitating earlier detection. Therefore, even when users do not explicitly express depressive symptoms, our approach, leveraging the association between life events and symptoms, enables us to sensitively capture latent signs of depression.

### Diagnosis Point Detection (DPD)

DatasetThe dataset of this task is RSDDTime MacAvaney et al. (2018), which has been mentioned in Section 3. The dataset is an annotated corpus sourced from Reddit. It encompasses two kinds of text spans: diagnoses (e.g., "I was diagnosed") and temporal expressions associated with the diagnosis (e.g., "today"). Consequently, the diagnosis point label can be derived through the utilization of these annotations.

BaselineThe essence of our DPD task is the conventional Change Point Detection Problem Truong et al. (2020), which aims to identify points in a time series indicative of a significant shift in the underlying data distribution. Therefore, we employ **RuLSIF**Liu et al. (2013), a well-established CPD model widely recognized for its performance Hushchyn and Ustyuzhanin (2021), as our baseline. RuLSIF7 utilizes a least-squares fitting approach to gauge the dissimilarity between the distributions of successive segments within a time series. When a substantial difference is observed in the distribution between two consecutive segments, a change point is identified. The method offers a non-parametric solution, making minimal assumptions about the underlying data.

Figure 5: Comparison of the predicted risk along time from two methods on a depression patient in Early Risk Detection. The user has no explicit symptom expression before A, but the symp&LE method can capture earlier sign from LE.

\begin{table}
\begin{tabular}{l|c|l|l} \hline Method & CW & \(ERDE_{5}\) & \(ERDE_{50}\) \\ \hline baseline & - & 13.62\(\pm\).006 & 6.72\(\pm\).105 \\ \hline  & 30 days & 13.49\(\pm\).013 & **6.07\(\pm\)**.092\({}^{*}\) \\ +_symp_ & 90 days & 13.29\(\pm\).020\({}^{**}\) & 6.36\(\pm\).220 \\  & 180 days & 13.58\(\pm\).028 & 7.41\(\pm\).126 \\ \hline  & 30 days & 13.42\(\pm\).014\({}^{*}\) & 6.27\(\pm\).158 \\ +_symp\&LE_ & 90 days & **13.20\(\pm\)**.020\({}^{**}\) & 6.60\(\pm\).273 \\  & 180 days & 13.63\(\pm\).047 & 6.72\(\pm\).137 \\ \hline \end{tabular}
\end{table}
Table 3: Results of ERD Task. “CW” means “causal window”, whose definition6 can be found in Section 2.2.2. _symp_ is short for “symptom”, and _LE_ is short for “life event”. The p-values indicating the significance of the differences between the baseline and our method is demonstrated as (*):p<0.1, (*):p<0.05 

Evaluation MetricThe DPD task aims to identify the exact diagnosis time of an individual, which is quite challenging by analysing the symptom sequences in the posting history. Consequently, applying strict metrics like accuracy and F1 score directly is impractical in this context. To address this, we calculate the F1 score using a smooth time window, defining true positive (TP) samples as those within a specific temporal proximity to the actual diagnosis time. In this study, we set the time window to 30 days, and we refer to this metric as \(F_{1}(w=30)\).

Experiment ResultsFigure 6 illustrates the experiment results of DPD task. We compare the results of baseline (red line) with causality-enhanced methods in various settings. "symp" denotes the inclusion of "symptom-to-symptom" causality, "LE" indicates the inclusion of "life-event-tox Symptom" causality, and "sympe&LE" encompasses both causality. We can find that causal relationships can significantly improve the detection accuracy of diagnosis point. Interestingly, the "LE" causality generally outperforms "symp" across all window sizes, and the combination of both causalities shows a substantial improvement, especially with a causal window of 180.

## 5 Related Work

In this section, we present relevant literature on causality analysis in the context of social media and mental health.

Causal Analysis MethodsGenerally, there are two ways to establish causal relationships. The first is Randomized Controlled Trial (RCT), typically applied in strict clinical settings Cipriani et al. (2018); the second one is observational Gianiciolo et al. (2020), including methods like Propensity Score Matching (PSM) Rosenbaum and Rubin (1983) and Regression Discontinuity Design (RDD) Cattaneo et al. (2019)--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 Kennedy-Martin et al. (2015), 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 Caliendo and Kopeinig (2005), and they can provide complementary advantages since the analysis is conducted in large population.

Mental-health-related Causality Inference on Social MedialExploring mental-health-related causal relationships is common in clinical studies (i.e. the theoretical and mechanism research in psychiatric diseases). For example, the Network Theory suggests that mental disorders arise from the causal interplay between symptoms Borsboom and Cramer (2013). Additionally, research on the network analysis of depression and anxiety symptoms has revealed potential causal relationships among them, with findings empirically supporting the idea that certain symptoms may act as central hubs, influencing the dynamics of the entire network Beard et al. (2016).

However, there is a scarcity of prior research utilizing computational methods to infer the causes of specific psychiatric symptoms or mental disorders on social media. Some prior works, such as Saha et al. (2019), utilize PSM to infer causal relationships between psychiatric medication use and symptom outcomes on Reddit Corpus. Additionally, Yuan et al. (2023) also utilize similar method to mine the causality between mental health coping and the severity of mental disorders. However, these studies mainly made qualitative conclusions about the inferred causality, while our work makes

Figure 6: Results of DPD task. The definition of “causal window”[6] can be found in Section 2.2.2. _symp_ is short for “symptom”, and _LE_ is short for “life event”.

 

further exploration by utilizing them quantitatively as features for downstream tasks like mental disease detection. What's more, other works (Garg et al., 2022; Saxena et al., 2023) use information retrieval methods to extract causal relationships between stressful events and mental disorders from social media posts. However, these studies focus on extracting direct causal relationships from the semantic information within one post, which may overlook the various long-term, subtle causal relationships that may be absent in a single post.

## 6 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 _supportm-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.

## 7 Ethical Statement

In this work, we make every effort to minimize the risk of personal privacy leakage during the data collection process. We replaced usernames with random identifiers to prevent identification of users without external information. All datasets used in our study are either publicly available or adhere to their respective licenses. We sign and comply with the data use agreement to prevent privacy infringement or other potential misuses. All posts in examples were de-identified and paraphrased for anonymity. What's more, we carefully considered the application of social media for the detection of mental illnesses. The purpose of this work is not to replace psychiatrists. Instead, we hope our model will be used as an effective auxiliary tool by experienced psychiatists in the future.

## 8 Limitations

In our study, there are some limitations that could be addressed in future research:

1. Although the causal relationships between life events and symptoms we identified achieved good results in downstream tasks, and we considered as many common and impactful life events as possible, the 11 categories life events we selected might not cover all events that could potentially affect mental health in life.
2. In addition to studying the causal relationships between life events and symptoms, as well as between symptoms themselves, we could also consider other factors and their causal relationships with mental disorders and symptoms.
3. Exploration of other downstream tasks involving temporal analysis of mental disorders is necessary. We identified diagnosis point detection and early risk detection here while more tasks can benefit from causal relations.

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## Appendix A Symptom and Life Event Definition

We consider a total of 7 mental disorders and 38 symptoms following Zhang et al. (2022b), which are deduced and integrated from DSM-5 (American Psychiatric Association et al., 2013). The 7 disorders and their representative symptoms are listed in the Table 4. And all of the 38 symptoms are listed in the Table 6. Additionally, we merge the 43 life events in Holmes-Rahe Stress Inventory (Noone, 2017) into 11 classes of life events, as illustrated in Table 7.

## Appendix B Life Events Dataset

We adopted an annotation similar to that of previous work (Zhang et al., 2022b). Our life event dataset contains 2643 posts related to one or more life events, alongside 5000 control posts (i.e., posts unrelated to any life event). We engaged three experienced annotator for the task, and their agreement (Fleiss' Kappa) is showed in Table5.

## Appendix C Detailed Symptom and Life Event Identification Results

The detailed identification results of Symptom and Life Event Identification Models are illustrated in Figure 7 and Figure 8 respectively. The highauc and F1 scores show that using these classifiers can help us automatically and accurately extract psychiatric symptoms and life events on Reddit corpus.

 



## Appendix E Evaluation Metrics

The following will introduce evaluation metrics for two downstream tasks.

\[Precision=\frac{TP}{TP+FP}\]

\[Recall=\frac{TP}{TP+FN}\]

where TP represents true positives (samples correctly predicted as positive),TN represents true negatives (samples correctly predicted as negative), FP represents false positives (samples incorrectly predicted as positive), and FN represents false negatives (samples incorrectly predicted as negative). Diagnosis Point Detection uses the F1 score as a metric, which balances precision and recall.

\[F1-Score=2\times\frac{Precision\times Recall}{Precision+Recall}\]

The metric used for early detection of depression, Early Risk Detection Error (ERDE)measure, is defined as follows:

\[ERDE_{o}(d,k)=\left\{\begin{array}{ll}c_{fp}&for~{}FP\\ c_{fn}&for~{}FN\\ lc_{o}(k)\cdot ct_{p}&for~{}TP\\ 0&for~{}TN\end{array}\right.\]

\(c_{fp}\) and \(c_{fn}\) are used to adjust the severity of false positives (FP) and false negatives (FN). \(c_{fn}\) was set to 1, while \(c_{fp}\) is set to the ratio of the number of positive cases in the data to the total number of users. \(lc_{o}(k)\) (\(\in[0,1]\)) encodes the cost of delaying the detection of true positives (TP), and \(c_{tp}\) defines the level of penalty for delaying TP. Setting \(c_{tp}\) to 1 and it means that delaying detection is equivalent to not detecting the case. The function \(lc_{o}(k)\) determines after how many posts k the cost of true positives starts to increase and is defined as follows:

\[lc_{o}(k)=1-\frac{1}{1+e^{k-o}}\]

Figure 8: Identification performance of each life event. The blue bar shows the AUC while the orange bar shows F1, and Life Event ID follows the order of Table 7.

Figure 7: Identification performance of each symptom. The blue bar shows the AUC while the orange bar shows F1, and Symptom ID follows the order of Table 6.



The parameter \(o\) controls the position on the x-axis where the cost increases more rapidly.We use \(ERDE_{5}\) and \(ERDE_{50}\) as evaluation metrics for the results of early detection of depression,shown in Figure 9.

## Appendix F Experiment Setting

In our ERD experiment, we trained the baseline model with the dataset proposed by Zhang et al. (2022b) that originated from a publicly available Reddit corpus. The training process employs a batch size of 64 and learning rate of 0.01. We used symptom features only and the posting list will be limited to a maximum of 256. To prevent over-fitting, we implement early-stopping based on validation performance, with a patience of 4 epochs.

Figure 10: The causal matrix between symptoms with the time window of 180 days. Symptom ID follows the order of Table 6.

 

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\begin{table}
\begin{tabular}{l l p{142.3pt}} \hline id & Life Event Categories & Original Life Events \\ \hline
1 & Loss and Bereavement & Death of a spouse; Death of a close family member; Death of a close friend \\ \hline
2 & Marriage and Commitment & Marriage; Marital reconciliation \\ \hline
3 & Relationship Conflicts and Breakdown & Divorce; Marital separation; Change in number of arguments with spouse; Trouble with in-laws \\ \hline
4 & Family Additions and Departures & Son or daughter leaving home; Gain of new family member \\ \hline
5 & Health and Well-being & Personal injury or illness; Sex difficulties; Pregnancy; Change in health of family member \\ \hline  & & Fixed at work;Retirement; Change in responsibilities at work; Change to a different line of work; Spouse begins or stops work; Trouble with boss; Change in work hours or conditions; Business readjustment \\ \hline
6 & Work and Career Challenges & Change in financial state; A large mortgage or loan; Foreclosure of mortgage or loan; A moderate loan or mortgage \\ \hline
8 & Education Transitions & Begin or end school/college; Change in school/college \\ \hline  & & Change in living conditions; Revision of personal habits; Change in sleeping habits; Change in eating habits; Change in eating habits; Change in church activities; Change in residence;Change in recreation; Change in social activities; Change in number of family get-togherst \\ \hline
10 & Vacations and Holidays & Vacation; Christmas \\ \hline
11 & Legal Matters & Jail term; Minor violations of the law \\ \hline \end{tabular}
\end{table}
Table 7: All life events and the 11 major categories