Tracking Life’s Ups and Downs: Mining Life Events from Social Media Posts for Mental Health Analysis
Authors: Minghao Lv, Siyuan Chen, Haoan Jin, Minghao Yuan, Qianqian Ju, Kenny Q. Zhu, Yujia Peng, Mengyue Wu
Accepted at: acl25

Tracking Life’s Ups and Downs: Mining Life Events from Social Media Posts for Mental Health Analysis

Minghao Lv1,2, Siyuan Chen1,2, Haoan Jin1,2, Minghao Yuan3, Qianqian Ju3, Yujia Peng3,4,5, Kenny Q. Zhu6, Mengyue Wu1,2*
1 X-LANCE Lab, School of Computer Science, Shanghai Jiao Tong University, China
2 MoE Key Lab of Artificial Intelligence, Jiangsu Key Lab of Language Computing, China
3 School of Psychological and Cognitive Sciences, Peking University, China
4 Institute for Artificial Intelligence, Peking University, China
5 State Key Laboratory of General Artificial Intelligence, BIGAI, China
6 University of Texas at Arlington, USA

Abstract

Social media platforms possess considerable potential in the realm of exploring mental health. Previous research has indicated that major life events can greatly impact individuals’ mental health. However, due to the complexity and ambiguity nature of life events, shedding its light on social media data is quite challenging. In this paper, we are dedicated to uncovering life events mentioned in posts on social media. We hereby provide a carefully-annotated social media event dataset, PsyEvent, which encompasses 12 major life event categories that are likely to occur in everyday life. This dataset is human-annotated under iterative procedure and boasts a high level of quality. Furthermore, by applying the life events extracted from posts to downstream tasks such as early risk detection of depression and suicide risk prediction, we have observed a considerable improvement in performance. This suggests that extracting life events from social media can be beneficial for the analysis of individuals’ mental health.

1 Introduction

Mental health constitutes a fundamental component of overall wellbeing, with mental disorders contributing significantly to the global disease burden (Prince et al., 2007). The National Institute of Mental Health reports that 23.1% of U.S. adults (59.3 million) experienced Any Mental Illness in 2022, including 6.0% (15.4 million) with Serious Mental Illness, yet only half received treatment1. This treatment gap underscores the critical need for automated mental state inference through life event analysis, which can reveal developmental trajectories of mental conditions. Social media platforms emerge as particularly valuable resources in this context (Cohan et al., 2018), offering sequential,

<table>
  <tr>
    <th>User Post Example</th>
    <th></th>
  </tr>
  <tr>
    <td>Posts</td>
    <td></td>
  </tr>
  <tr>
    <td>My roommate and I had to move apartments at the beginning of the semester ... but he was out of a job and just partying the whole time ... I actually had to call an ambulance for him because he ODed in front of our apartment complex.</td>
    <td>Career</td>
  </tr>
  <tr>
    <td>When we got kicked out of our apartment, we were only given 10 days to move out and find a new place.</td>
    <td>Relocation</td>
  </tr>
  <tr>
    <td>At the same time, my laptop died on me ... I decided to get another laptop instead ... I had no access to the internet or a computer during this time. This put me behind several weeks in my courses ... The final grades came in ... and sure enough I failed every course.</td>
    <td>Financial</td>
  </tr>
  <tr>
    <td>Final diagnosed with: Depression, ADHD</td>
    <td>Education</td>
  </tr>
</table>

Figure 1: One Example of Life Events Contained in Social Media Posts from SMHD (Cohan et al., 2018). Various life events are mentioned, the user is later diagnosed with Depression and ADHD.

temporally rich data from users - including substantial populations with mental health conditions - who document life changes and internal states through continuous self-disclosure.

Life events - major incidents altering identity, resources, and social position - have long been recognized in psychological research as critical determinants of mental health trajectories (Mandemakers, 2023; Rabkin and Struening, 1976). These transitions (e.g., marriage, career changes, relocation) profoundly influence psychological well-being, social functioning, and physical health (Luhmann et al., 2012; Marc et al., 2024), with particular relevance to depressive episodes and suicidal risk: meta-analyses demonstrate stressful events increase suicidal outcomes by 37%-45%, disproportionately affecting males and youth (Howarth et al., 2020; Liu and Miller, 2014).

However, applying life events from social media to analyze mental health trajectory data faces numerous challenges. Firstly, applying life events to mental health analysis requires a clear definition and taxonomy for life events. The definition of life events should be as comprehensive as possible, covering the major life events that individuals might experience and could impact mental health. Secondly, the vast amount of less critical information
and noise in social media can negatively affect the performance of event detection models (Atefah and Khreich, 2015). Despite the abundance of data on social media, the amount of annotated data available for mental health analysis research is quite limited (Cohan et al., 2018). Finally, determining life events requires extensive context. In other words, within a single post, the poster might use multiple sentences or even several discrete phrases to describe one life event. This poses difficulties for the annotation of life events. As illustrated in the example presented in Figure 1, the poster described multiple life events using several discrete sentences within the same post, which complicates the annotation of life events.

In this work, we adjusted a stress life event taxonomy for social media research and proposed an annotation system for 12 major life event categories. Our definition of life events, based on Haimson et al. (2021), modifies and supplements to constitute 12 categories with 127 distinct life events. Then, we leverage innovative annotation methods and the bespoke annotation system to propose a multi-life event classification dataset, PsyEvent (Psychologically-related Life Events) based on social media posts, encompassing a total of 7,965 Reddit post sentences labeled with 12 life event categories. We recruit annotators and provide them with rigorous training. The annotation approach we adopt is complex and incorporates numerous quality assurance measures. We also develop a web-based annotation system tailored to life events to implement these annotation methods and quality assurance techniques (For more details, please refer to the appendix A.). Furthermore, to filter posts more relevant to life events for annotation, we employ an embedding-based search method (Reimers and Gurevych, 2019) instead of traditional keyword-matching techniques to identify candidate posts. This approach allows annotators to focus more on posts related to life events, thereby enhancing annotation efficiency. We train two life event classification models using the aforementioned annotated dataset.

Finally, we utilize the trained life event classification models on two downstream tasks: early risk detection of depression and suicide risk prediction. We employ two different approaches to apply life events, and the downstream tasks with life events show performance improvements, demonstrating the effectiveness of life events in mental health analysis. Our contributions are three-fold:

• We are the first to formalize life events computationally by adopting and adapting an existing taxonomy of stress-related life events for social media analysis. Our dataset, PsyEvent, includes detailed annotations for 12 life event categories, covering 127 specific events. This comprehensive approach enables the development of a life event detection model with over 95% AUC.

• Our life event detection model can effectively trace a user’s post history to identify and tag major life events that are critical to their mental health status. We hence innovatively adopt life events as a novel feature for mental health-related tasks, enhancing model performance, interpretability and clinical relevance.

• We demonstrate the effectiveness of life events by applying them to two critical downstream tasks: early risk detection and suicide risk prediction. Our results show significant performance improvements, validating the value of life events in enhancing predictive models.

2 Related Work

2.1 Events Extraction in Social Media

Social media event detection has evolved through two methodological paradigms. Early approaches predominantly employed clustering techniques: Weng and Lee (2011) proposed wavelet-based signal clustering (EDCoW) for noise reduction and event identification, while Li et al. (2012) developed Twevent using bursty segment detection and content-based clustering. Ritter et al. (2012) advanced open-domain categorization through latent type discovery in TwiCal.

The deep learning revolution introduced neural architectures addressing three key limitations of clustering methods: context sensitivity, feature engineering dependency, and cross-domain adaptability. Nguyen et al. (2017) pioneered hybrid CNN-RNN frameworks for noise-resilient detection in Twitter streams. Syntactic hierarchy modeling saw breakthroughs with Yan et al. (2019)’s MOGANED, integrating multi-order graph attention networks for trigger word identification. Scalability challenges were addressed by Afyouni et al. (2022)’s Deep-Eware, combining incremental NLP pipelines with geospatial processing for real-time event tracking. The life event taxonomy used in Chen et al. (2024)’s study, includes 11 categories
and 43 life events-based on the Holmes-Rahe Stress Inventory (Holmes and Rahe, 1967). Furthermore, a life event classifier based on BERT was trained for causal relationship computation. The current study differs from previous works by formerly defining a 121 life-event paradigm and innovatively investigates life events as effective mental health indicators, with dual paradigms (feature-based vs. embedding fusion) proposed for depression and suicide prediction.

2.2 Prior Life Event Inventories

The foundational Social Readjustment Rating Scale (SRRS) (Holmes and Rahe, 1967) established quantitative life event analysis through 43 stress-inducing events rated by 394 participants based on social adaptation demands. While pioneering stress measurement (Scully et al., 2000), SRRS faced critiques regarding categorical granularity and stressor calibration (Monroe, 1982). Dohrenwend et al. (1978) addressed these limitations through methodological refinements in their Psychiatric Epidemiology Research Interview (PERI) scale, expanding to 102 events with population-specific validation across three dimensions: event selection criteria, rater diversity, and consensus metrics. Contemporary analyses (Haimson et al., 2021) reveal remaining gaps in capturing sociotechnical transitions critical for modern digital behavior studies.

3 PsyEvent Dataset Construction

3.1 Life Events Definition

Selecting the appropriate definition of life events is crucial for effectively conducting mental health analysis from social media data. In light of the aforementioned issues, we aim to select life events that more closely align with the major occurrences in people’s daily lives. Our definition of life events is based on the taxonomy proposed by Haimson et al. (2021), which identifies 121 life events organized into 12 categories. This taxonomy is grounded in a comprehensive survey conducted with a representative sample of 554 participants from the U.S. general population. We adopted most of the life event taxonomy proposed by Haimson et al. (2021), with modifications driven by overlapping categories, annotation clarity, and pilot annotation feedback. These adjustments resulted in a refined taxonomy comprising 12 life event categories and 127 sub-events. The finalized taxonomy reflects daily life events that influence mental health and is detailed in Appendix D, with an example of the “New Birth in Family” category shown in Table 1.

<table>
  <tr>
    <th>Life Event Category</th>
    <th>Life Events</th>
  </tr>
  <tr>
    <td>New Birth in Family</td>
    <td>gain of new family member; gave birth / became a parent; adopted a child; became a grandparent; became a great-grandparent; became an aunt/uncle</td>
  </tr>
</table>

Table 1: Life events in the adjusted "New Birth in Family" category

3.2 Dataset Annotation

We used a subset of the SMHD dataset (Zhang et al., 2022; Cohan et al., 2018), which includes Reddit posts related to mental health, making it suitable for detecting life events. A preliminary screening process was conducted to select relevant posts using sentence-BERT embeddings and cosine similarity. Posts were filtered for length to enhance annotation efficiency. We adopted iterative annotator training methods and a serious quality control protocol to ensure the quality. Annotators used a bespoke website system for annotation (for more detailed descriptions, please refer to the appendix A.3.). The dataset includes both life event and control sentences to improve model training. We asked annotators to identify 127 sub-life events within the posts and, if present, label the sentences with the broader category to which the sub-life event belongs. This method resulted in more specific labels for life events.

During the online annotator training sessions, we openly disclosed and discussed our compensation structure, which exceeded the local minimum wage requirements. We recruited annotators through poster advertisements. All participants were well-educated, including those majoring in psychology. The age distribution was as follows: 75% of annotators were 18-25 years old, 18.75% were 26-30, and 6.25% were 31-40 years old. Regarding educational background, 37.5% held bachelor’s degrees while 62.5% possessed graduate-level degrees or higher.

We also attempted automatic annotation with Large Language Models (LLMs, including GPT-4o and GPT-4) for life events extraction. However, it did not excel at certain event tagging: such context-dependent annotation still seems challenging even with the application of advanced context-learning and CoT techniques. We had three annotators la-
belling 220 data points to create a small test set for evaluating the annotation accuracy of GPT-4o. The inter-annotator agreement was 0.86. The average annotation accuracy of GPT-4o on this small test set was found to be 0.70. Therefore, human annotation is solely adopted to ensure a high-quality dataset.

3.3 Data Statistics
After annotating (for the definition of life events and the annotation process of the dataset, please refer to Appendix A.), our final product, PsyEvent, containing 12 categories of life event labels, is divided into three parts: training/validation/test set. After the aforementioned annotation method, we ultimately obtained 7,965 sentences, which were then allocated to the test set, validation set, and training set in a ratio of 7:1:2. Due to natural distribution, there are certain life events less mentioned in these posts and we tried to exclude vague, ambiguous posts for more precise recognition. The number of positive samples in the dataset is shown in Table 2.

<table>
  <tr>
    <th>Life Event Categories</th>
    <th>Positive Num.</th>
  </tr>
  <tr>
    <td>Health</td>
    <td>909</td>
  </tr>
  <tr>
    <td>Financial</td>
    <td>344</td>
  </tr>
  <tr>
    <td>Relocation</td>
    <td>165</td>
  </tr>
  <tr>
    <td>Legal</td>
    <td>226</td>
  </tr>
  <tr>
    <td>Relationship Changes</td>
    <td>323</td>
  </tr>
  <tr>
    <td>New Birth in Family</td>
    <td>178</td>
  </tr>
  <tr>
    <td>Death</td>
    <td>171</td>
  </tr>
  <tr>
    <td>Career</td>
    <td>377</td>
  </tr>
  <tr>
    <td>Education</td>
    <td>199</td>
  </tr>
  <tr>
    <td>Lifestyle Change</td>
    <td>294</td>
  </tr>
  <tr>
    <td>Identity</td>
    <td>262</td>
  </tr>
  <tr>
    <td>Societal</td>
    <td>136</td>
  </tr>
  <tr>
    <th>Total</th>
    <th>3,584</th>
  </tr>
</table>

Table 2: Number of positive samples across 12 categories

4 Life Events Recognition System
We intruduce the models trained to extract life events from social media and self-status determination, along with their respective results.

4.1 Life Events Detection
An individual may experience multiple life events throughout their life, and when posting, the poster may describe several life events simultaneously. As a result, there may be instances where a single post contains multiple life events. Therefore, we treat life event detection as a multi-label binary classification task. We employ a BERT-based encoder (Devlin et al., 2019) to train the life events detection model. Since we incorporate control posts, a data balancing sampler is used to ensure that each training batch samples an equal amount of annotated data and control sentences.

<table>
  <tr>
    <th>Life Event Categories</th>
    <th>AUC (%)</th>
  </tr>
  <tr>
    <td>Health</td>
    <td>92.1</td>
  </tr>
  <tr>
    <td>Financial</td>
    <td>95.7</td>
  </tr>
  <tr>
    <td>Relocation</td>
    <td>97.7</td>
  </tr>
  <tr>
    <td>Legal</td>
    <td>96.1</td>
  </tr>
  <tr>
    <td>Relationship Changes</td>
    <td>95.0</td>
  </tr>
  <tr>
    <td>New Birth in Family</td>
    <td>92.6</td>
  </tr>
  <tr>
    <td>Death</td>
    <td>99.7</td>
  </tr>
  <tr>
    <td>Career</td>
    <td>93.5</td>
  </tr>
  <tr>
    <td>Education</td>
    <td>99.2</td>
  </tr>
  <tr>
    <td>Lifestyle Change</td>
    <td>87.9</td>
  </tr>
  <tr>
    <td>Identity</td>
    <td>95.5</td>
  </tr>
  <tr>
    <td>Societal</td>
    <td>97.4</td>
  </tr>
  <tr>
    <th>Avg.</th>
    <th>95.2</th>
  </tr>
</table>

Table 3: Life event detection results on *PsyEvent*.

Results As illustrated in Table 3, the average classification performance is over 95%, through varies across the 12 categories of life events due to disparities in the inherent difficulty of classifying the events themselves. We can observe that among the 12 life event categories, the "Death" category achieved the best classification performance, while the "Lifestyle Change" category had the poorest classification outcome. During the annotation process, we also noticed that annotators had the most questions and discussions about the "Lifestyle Change" category. It is possible that the varying understandings of this type of life event among annotators led to less successful classification results for this category. For a 12-category multi-class task, extracting life events is inherently a challenging task (Zhang et al., 2022). More detailed information is provided in Appendix B.1.

4.2 Self-Status Determination
In addition to the life event detection model, we developed a life event self-status determination model. When individuals recount life events on social media, they may refer to past experiences rather than current ones, or even events involving others rather than themselves. To address these complexities, we specifically trained a model to distinguish whether the narrated events pertain to the individual’s own experiences or those of others.

Such a self-status determination model is to determine whether the *posters themselves* are currently experiencing the life events detected in sentences. Thus, we define this task as a single-label
binary classification task. We employ the same model used for the life event detection task to accomplish the self-status determination task. Finally, the self-status determination model yields an AUC of 76.8%. This status determination model is later used in combination with the Life Event Detection model to infer life events status in downstream tasks. Detailed information is provided in Appendix B.2.

4.3 Case Study

Figure 2 illustrates the information of a portion of a user’s posts after being processed by the Life Events Recognition model, including the posting time, post ID, and corresponding life event labels. Each user has a complete posting history and life event history. With the two Life Event Classification models, we can capture long-term life events change which is critical to one’s mental health status.

5 Downstream Tasks

In this section, we describe the application of the above model to two downstream tasks: early detection of depression risk and suicide risk prediction. The overall architecture is illustrated in Figure 3.

5.1 Early Risk Detection of Depression (ERDD)

Early risk detection is designed to identify mental disorders at an early stage, thereby achieving the goal at a lower cost (Losada and Crestani, 2016). The early detection task is defined as follows: For a user \( U_i \), we define their posting history as \( [P_{i,1}, P_{i,2}, ..., P_{i,n}] \) (where \( n \) is the total number of posts made by user \( U_i \) and \( P_{i,j} \) is the \( j \)-th post published by \( U_i \)). Traditional depression detection models predict whether a user has depression based on their entire posting history \( [P_{i,1}, P_{i,2}, ..., P_{i,n}] \). However, in early risk detection, posts emerge one after another, so at time \( t \), the model can only see \( t \) posts \( [P_{i,1}, P_{i,2}, ..., P_{i,t}] \). Once the model has sufficient confidence, it can make an early prediction on \( y_i \) at time \( t \)(where \( t \leq n \)), allowing the prediction to strike a good balance between accuracy and earliness.

5.2 Suicide Risk Prediction (SRP)

Suicide is one of the leading causes of death globally (Värnik, 2012). Suicide risk prediction (SRP) using social media posts offers a promising approach to proactively identify at-risk individuals, enabling early intervention and potentially reducing mortality rates. While binary classification (i.e., distinguishing between individuals with and without suicidal intent) is a common approach, it fails to capture the nuances in the severity of suicidal intent. Therefore, this paper adopts a five-class classification framework inspired by Gaur et al. (2019). Three classes are derived from the Columbia Suicide Severity Rating Scale (C-SSRS): Suicidal Ideation (ID), Suicidal Behavior (BR), and Actual Attempt (AT), representing increasing levels of risk. Two additional categories are included for a more comprehensive assessment: Suicide Indicator (IN), for users with at-risk language but no active distress symptoms, and Supportive (SU), for individuals discussing suicide without expressing personal risk. Thus, SRP is framed as a multi-class classification problem, using the user’s social media history to determine their risk category.

5.3 Applying Life Events

Life event features are critical in depression detection and suicide risk prediction as they provide contextual information that enhances model interpretability and clinical relevance. By identifying specific life stressors such as job loss or relation-
ship breakdown, these features contextualize the onset of mental health issues and improve prediction accuracy. This contextual understanding is valuable for developing targeted interventions and informing clinical decision-making.

We integrate the life event detection model and the life event self-status determination model into two tasks: early risk detection of depression (ERDD) and suicide risk prediction (SRP). We explore two approaches to leverage life events in these tasks.

Life event as features We apply the life event classification models to the ERDD dataset, generating sequences of life event detection probabilities and self-status determination probabilities for each user’s posts. These probability sequences are used as features alongside depression labels to train a depression detection model.

Concatenating life event features Since depression-related posts inherently contain semantic information related to depression, this semantic information can also be leveraged to train a depression classification model. Taking it a step further, since the life event features and post semantic information can both be used to train a depression classification model, why not use both together? Therefore, we concatenate the life event detection probability sequences, self-status determination sequences, and semantic information from the same posts to train a classifier model, hoping to achieve the best possible depression detection results.

6 Experiments

We present the outcomes of two downstream tasks: (1) integrating life events into early risk detection of depression using the two approaches described above, and (2) utilizing life events in suicide risk prediction.

6.1 Early Risk Detection of Depression (ERDD)

Dataset We utilize the ERD dataset proposed by Chen et al. (2023). They extracted users and posts from a public Reddit corpus. They selected depression users by detecting patterns that consist of two parts: one matching self-reported diagnoses (e.g., "diagnosed with") and the other mapping relevant keywords to depression (e.g., major depressive disorder). Control users (i.e., healthy individuals) were randomly selected from those who have never posted or commented in mental health-related subreddits. The dataset comprises 3,105 depression users and 17,209 control users. In this dataset, the average number of life events mentioned by users is shown in Figure 4. We can observe that patient users experience a higher number of life events, including health, career, education, and relationship changes.

![The average number of life events in patient users (left) and control users (right).](page_820_120_601_377.png)

Figure 4: The average number of life events in patient users (left) and control users (right).

Baseline We employ the model proposed by Zhang et al. (2022) as baseline, which utilizes CNN of various kernel sizes as backbone that has yielded good results on the task, and we use RoBERTa-large (Liu et al., 2019), BERT-base (Devlin et al., 2019) and Qwen2-7B (Yang et al., 2024) as encoders to extract semantic information from depression posts as input. More specifically, for RoBERTa and BERT, we use sentence-BERT (Reimers and Gurevych, 2019) to extract embeddings from these posts, and apply the obtained embeddings to train classification models.

Evaluation Metric We adopt 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 C.

Experiment Results The results for the ERDD task are presented in Table 4. We conduct three experiments for each method using different seed values, and the results shown in the table are the average of the three experiments. It can be observed that using the RoBERTa-large as an encoder yields better results than using BERT-base, and Qwen2-7B achieved the best results. Therefore, we used RoBERTa-large and Qwen2-7B as
encoder in subsequent experiments. Naturally, using life events as features alone did not yield satisfactory results since by its nature is sparse feature. However, it works well when in combination with ordinary embeddings, where the model can efficiently learn the contextual semantics as well as the relations between life events and mental status. The most effective approach in this task was concatenating life event features. This method performed the best on both evaluation metrics using Qwen2-7B and showed a more significant improvement on \( ERDE_5 \) while using RoBERTa-large. For RoBERTa, adding life events brings about 25 relative percentage improvement on \( ERDE_5 \). Since \( ERDE_5 \) is more sensitive to delays, this also indicates that using concatenated life event features can predict depression at an earlier stage. For this method, the improvement using Qwen2-7B is not as significant as that with RoBERTa-large. This may be because the embedding dimension of Qwen2-7B is sufficiently high, inherently containing rich semantic information. For the lower-dimensional embeddings of RoBERTa-large, the role of life events becomes more pronounced.

<table>
  <tr>
    <th>Method</th>
    <th>\( ERDE_{50}(\%) \downarrow \)</th>
    <th>\( ERDE_{50}(\%) \downarrow \)</th>
  </tr>
  <tr>
    <td>BERT-base</td>
    <td>12.73 ± 0.12</td>
    <td>6.09 ± 0.28</td>
  </tr>
  <tr>
    <td>Roberta-large</td>
    <td>12.33 ± 1.28</td>
    <td>5.67 ± 0.59</td>
  </tr>
  <tr>
    <td>Qwen2</td>
    <td>9.51 ± 0.91</td>
    <td>4.67 ± 0.79</td>
  </tr>
  <tr>
    <td>LE as feature</td>
    <td>13.56 ± 0.02</td>
    <td>6.30 ± 0.06</td>
  </tr>
  <tr>
    <td>LE concat(Roberta<sup>1</sup>)</td>
    <td>9.77 ± 1.33</td>
    <td>5.41 ± 0.07</td>
  </tr>
  <tr>
    <td>LE concat(Qwen2-7B)</td>
    <td><b>9.26 ± 0.62</b></td>
    <td><b>4.11 ± 0.18</b></td>
  </tr>
</table>

Table 4: Experiment results of Early Risk Detection of Depression (ERDD). Roberta<sup>1</sup> means Roberta-large.

6.2 Suicide Risk Prediction (SRP)

Dataset We utilize the suicide risk prediction dataset proposed by Gaur et al. (2019), a human-annotated dataset encompassing 500 users categorized based on varying levels of suicide risk. The data comprises of 22% control users, 20% users with some suicidal indication, 34% users with suicidal ideation, 15% users with suicidal behaviors, and 9% users have made an attempt to commit suicide. On average, each user has 31.5 posts, spanning a timeframe from 2005 to 2016. The entire dataset is randomly divided into training, validation, and test sets in a 7:1:2 ratio. Additionally, the sum of life event probabilities in this dataset is shown in Figure 5. Interestingly, severe conditions including Behavior and Attempt users exhibit larger proportion of Health-related life events in the total distribution.

![Stacked Bar Chart of Life Events by Suicide Risk](page_1012_120_388_246.png)

Figure 5: The sum of life event probabilities mentioned by users in the SRP dataset by suicide risk.

Baseline We utilize the same baseline model as used in ERDD. And we use RoBERTa-large (Liu et al., 2019), BERT-base (Devlin et al., 2019) and Qwen2-7B (Yang et al., 2024) as encoders to extract semantic information from depression posts as input.

Experiment Results The results of the SRP task are demonstrated in Table 5. Notably, the approach of concatenating life event features with semantic features significantly improves SRP performance, demonstrating the value of incorporating life events into predictive models. However, life event features, by their nature, are sparse and thus insufficient on their own to drive effective predictions. In contrast, when life event features are combined with any contextual semantic features, the performance improves by over 2%. This suggests that while life event features alone may not be effective enough, their combination with semantic features derived from advanced embeddings can significantly enhance model performance. This integration not only enhances model accuracy but also maintains interpretability, as life events offer clear, contextual explanations for predictions. For instance, life events can directly link specific stressors to mental health outcomes, making the model’s decisions more transparent and clinically actionable.

However, we observed that the performance gains from integrating life event features with large language model embeddings varied across the two tasks (ERDD and SRP). This indicates that the effectiveness of combining life event features with semantic embeddings may depend on the specific task and model architecture. Therefore, further
exploration is needed to optimize the integration of life event features with large language model embeddings for different predictive tasks.

<table>
  <tr>
    <th>Method</th>
    <th>F1 Score (%) ↑</th>
  </tr>
  <tr>
    <td>BERT-base</td>
    <td>33.50 ± 0.38</td>
  </tr>
  <tr>
    <td>Roberta-large</td>
    <td>34.52 ± 0.75</td>
  </tr>
  <tr>
    <td>Qwen2</td>
    <td>36.44 ± 2.17</td>
  </tr>
  <tr>
    <td>LE as feature</td>
    <td>12.49 ± 0.00</td>
  </tr>
  <tr>
    <td>LE concat(Roberta-large)</td>
    <td>36.00 ± 0.83</td>
  </tr>
  <tr>
    <td>LE concat(Qwen2-7B)</td>
    <td><b>39.13 ± 2.12</b></td>
  </tr>
</table>

Table 5: Experiment results of Suicide Risk Prediction (SRP)

6.3 Analysis

The extraction of life events can also provide explanations for the diagnosis of mental illnesses. In this section, we will specifically demonstrate an example, starting from a post where a user was diagnosed, and looking backward to show the life events experienced by this user before being diagnosed with depression to illustrate the auxiliary role of life event identification in disease diagnosis.

<table>
  <tr>
    <th>User Post No.: 33</th>
    <th>Predicted life events label: Education, Health, Relationship Changes</th>
  </tr>
  <tr>
    <td colspan="2">Post: This is the second semester I’ve blown it ... My parents were going through a rough divorce and my life was a mess ... I was diagnosed with mild depression ... I got into community college and I bombed my first year there ...</td>
  </tr>
</table>

Table 6: This user’s 33rd post was labeled with Education, Health, and Relationship Changes by the aforementioned life event classification model.

Table 6 shows the post published by the user at the time of diagnosis. This post, after being processed by the two life event classification models, was labeled with "Education", "Health" and "Relationship Changes". The poster was diagnosed with depression while also experiencing stress from school and family upheaval. These are significant stressor events contributing to the poster’s depression. For students, exams are a significant source of stress, and the pressure felt by students after entering college is particularly pronounced among first-year students (Robotham, 2008). Moreover, even before the breakdown of a marriage, children whose parents later divorce exhibit higher levels of anxiety/depression and antisocial behavior compared to children whose parents remain married; the divorce itself can further exacerbate the child’s anxiety/depression (Strohschein, 2005).

<table>
  <tr>
    <th>User Post No.: 31</th>
    <th>Predicted life events label: Death</th>
  </tr>
  <tr>
    <td colspan="2">Post: Friend died the other night. Not really too upset over it ... I just feel kinda cold hearted because I’m not crying over it. I’m just bummed ... It’s not that I don’t care, but it’s not something that I’m gonna sit around and cry over.</td>
  </tr>
  <tr>
    <th>User Post No.: 32</th>
    <th>Predicted life events label: Death, Relationship Changes</th>
  </tr>
  <tr>
    <td colspan="2">Post: Cut a friend out of my life. Getting the vibe that she hates me ... I was getting tired of that also .... I was at another friend’s funeral the other day and barely any of them bothered to show up .... they couldn’t even bother themselves to show up at his funeral ... As of right now I don’t really plan on reaching out to her ...</td>
  </tr>
</table>

Table 7: Life events mentioned in the user’s 31st and 32nd posts.

Additionally, before this, the poster had experienced events such as deteriorating relationships with friends and the death of a friend, as shown in Table 7. Peer relational issues may be a common cause of worry symptoms in depression and generalized anxiety disorders, or this risk factor may facilitate the association between these symptoms (Konac et al., 2021). Therefore, life events can provide an explainable pathway for the development of mental disorders, aiding in the diagnosis of these conditions. Moreover, Figure 6 illustrates the principle and function of ERDD. The ERDD model diagnosed the poster with depression at the 27th post, while the individual did not mention their own diagnosis until the 33rd post.

![ERDD aims to predict the onset of depression in posters using a minimal number of posts, thereby detecting the condition at an earlier stage.](page_1012_1342_388_180.png)

Figure 6: ERDD aims to predict the onset of depression in posters using a minimal number of posts, thereby detecting the condition at an earlier stage.

7 Conclusion

In this study, we demonstrated the potential of leveraging social media data to explore mental health through the lens of major life events. Recognizing that life events are often scattered throughout social media posts, we adopted a sentence-level annotation approach. To ensure high-quality data, we established a meticulous annotation guideline and incorporated rigorous testing and quality control processes throughout the annotation workflow. Using this carefully curated dataset, PsyEvent, we intro-
duced a structured methodology for identifying life events from social media posts. Our experiments revealed that integrating life event information into downstream tasks, such as Early Risk Detection of Depression (ERDD) and Suicide Risk Prediction (SRP), largely improves task performance. These findings underscore the value of life event analysis as a powerful tool in mental health research. Future studies can build upon our dataset and methodologies to further enhance the applications of social media analysis in mental health interventions.

Ethical Statement

Dataset  To protect personal privacy during data collection, we replaced usernames with random identifiers to prevent user identification without external information. All datasets used in this study are publicly available or used under appropriate licenses. We comply with data use agreements to prevent privacy violations and misuse.

Annotation  We ensure that annotators are compensated with a fair wage above the minimum requirement. Any questions or concerns they have will be addressed promptly. Given that the content involves sensitive topics such as stressful life events, we acknowledge the potential emotional impact on annotators. Therefore, they are free to take breaks or discontinue the task at any time. We also interviewed some annotators about their feeling after annotation. They only reported slight discomfort at the time of reading sad or frightening posts due to empathy, and they found no long-term negative effects on them.

Application  This study 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 supportive tool by experienced psychiatrists in the future.

Limitations

Although in this work we demonstrated the potential of life events for mental health research, we did not explore the pathways through which life events exert their influence, nor the differences in the impact of different life event categories. Such research would be of greater assistance to mental health studies. Moreover, other mental health research tasks could be explored to investigate the differences in the role of life events across various tasks.

Further research is needed to identify better ways to apply life event features, as different tasks may require different approaches to using life events to achieve optimal results. Additionally, the integration of large language models with life events also needs further exploration to optimize the combination of large language model embeddings and life events for different prediction tasks.

Acknowledgement

This work has been supported by the China NSFC project (No. U23B2018) and Guangxi major science and technology project (No. AA23062062).

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A PsyEvent Dataset Construction

A.1 Life Events Definition

Selecting the appropriate definition of life events is crucial for effectively conducting mental health analysis from social media data. In light of the aforementioned issues, we aim to select life events that more closely align with the major occurrences in people’s daily lives. Our definition of life events is based on the taxonomy proposed by Haimson et al. (2021), which identifies 121 life events organized into 12 categories. This taxonomy is grounded in a comprehensive survey conducted with a representative sample of 554 participants from the U.S. general population.

In the survey, participants were asked to describe, in open-ended paragraphs, the major life events they had experienced. The researchers analyzed these responses using qualitative open coding (Strauss and Corbin, 1998), a method for identifying patterns and themes in textual data. Through this process, they determined which life events to include in the taxonomy and how to categorize them. To refine the taxonomy further, the researchers engaged in in-depth discussions to address any surprising or ambiguous data points. They prioritized events that participants explicitly identified as having a significant impact on their lives. Additionally, the researchers incorporated insights from the life transitions literature, ensuring that less common but widely recognized major transitions were also included. To validate and enhance the practical application of the taxonomy, they conducted experiments, confirming its relevance and reliability for analyzing life events.

We adopted most of the life event taxonomy proposed by Haimson et al. (2021), with modifications driven by overlapping categories, annotation clarity, and pilot annotation feedback.

To enhance annotation consistency, we merged similar categories, such as "Family Relationships" and "Relationships", and renamed the former as "New Birth in Family", focusing on family member changes. Events related to interpersonal relationship shifts were grouped under the renamed "Relationship Changes" category. Stressful life events, namely "exams" and "substance abuse and addiction" were also added to account for their potential impact on mental health.

These adjustments resulted in a refined taxonomy comprising 12 life event categories and 127 sub-events. The finalized taxonomy reflects daily life events that influence mental health and is detailed in Appendix D.

A.2 Annotation Data Selection

Raw Dataset The raw dataset we started from is a subset of SMHD (Zhang et al., 2022; Cohan et al., 2018), whose data originated from subreddits related to mental illnesses on Reddit. Consequently, many users in the dataset had been diagnosed with one or more mental disorders and had experienced various stressful life events, making it a highly suitable initial dataset for detecting life events.

Data Preliminary Screening To enhance annotation efficiency and ensure that the data to be annotated contains as many life events as possible, rather than irrelevant information, we conducted a preliminary screening of the data that needed to be annotated. We initially utilized sentence-BERT (Reimers and Gurevych, 2019) to encode both posts and life events into embeddings, calculating the cosine similarity between them. If the similarity exceeded our predefined threshold, we selected that post as a candidate for annotation. Subsequently, to facilitate annotation, we filtered out posts that were either too short or excessively long.

A.3 Iterative Annotator Training

We invited well-educated volunteers to conduct the annotation, including students majoring in psychology. Our entire annotation process is as follows:
Training Session: We provided detailed training documents to instruct participants on the annotation methods. We personally explained the training documents through video conferences and demonstrated some annotation examples.

Annotator Practice: We collected test questions from samples where the authors had reached a consensus in the preliminary annotations, created a question bank, and prepared it for practice. We extracted a certain number of practice questions from the pre-established question bank to create tests. After each test, annotators were able to see where their decisions differed from ours. We provided explanations for each question to help them better understand our annotation requirements. Annotators are only allowed to participate in formal annotation after achieving a sufficient score in their practice sessions.

Formal Annotation Annotators who have passed the practice session can proceed to formal annotation. Additionally, annotators will continuously discuss any issues encountered during the annotation process with us. Annotators conducted their practice and annotation on the system that we had established. The introduction to the annotation system is provided in Appendix E.

A.4 Quality Control

In addition to rigorous training for annotators, we have also implemented other methods to ensure the quality of the annotation. We conducted checks on the annotators’ annotated content. We regularly selected 10% of the completed annotations for review and scored them. At the same time, we promptly corrected any annotations we deemed inappropriate. If the weighted score of the reviewed content was below the threshold we had set, all annotations in that batch we checked were invalidated and redistributed to the annotators for re-annotation.

Additionally, each post underwent two rounds of annotation, meaning that the same post was annotated by two different individuals to ensure the data quality. We calculated the Fleiss’ kappa (Fleiss et al., 1981) between the two rounds of annotation to measure the inter-annotator agreement. Finally, the average inter-annotator agreement for the two rounds of annotation was 0.67 falling within substantial agreement.

A.5 Annotation Platform Development

Due to the extensive context required to identify life events, we designed a specific annotation method. We built a website to serve as an annotation system; a screenshot of the website can be found in Appendix E. The system presents each annotator with a post and highlights one sentence within that post. Annotators can select any sentence from the post and label it with a life event tag. Additionally, posts that fail the checks are redistributed to annotators by the system.

A.6 Labels and Data Splits

The vast majority of social media content is unrelated to life events. To better enable the model to recognize life events in posts, we have incorporated a subset of annotated sentences that do not contain life events as control sentences into the dataset as well.

The data underwent two rounds of annotation, resulting in multiple annotations for each post, and we accept any single annotation as valid. To consolidate the multiple annotations for each sentence into a single golden label, we adopt the following approach (for further explanation of the annotation labels, please refer to Appendix E): we consider a sentence to contain a life event if either of the two annotators identifies it as such, and we determine that the poster is experiencing a life event only if both annotators agree that the poster is going through a life event.

B Life Events Recognition System

B.1 Life Events Detection

An individual may experience multiple life events throughout their life, and when posting, the poster may describe several life events simultaneously. As a result, there may be instances where a single post contains multiple life events. Therefore, we treat life event detection as a multi-label binary classification task. When extracting life events from posts, we need the model to determine whether each life event is associated with each sentence in the post. We employ a BERT-based encoder (Devlin et al., 2019) and apply a linear layer with a sigmoid activation function to the hidden state of the [CLS] token to predict the probabilities of all life events. Additionally, we use binary cross-entropy as the loss function to train the model. Because we incorporate control posts, we use a data balancing sampler to ensure that each training batch samples
an equal amount of annotated data and control sentences.

Results  We trained the life event detection model on *PsyEvent* using the BERT–Large (Devlin et al., 2019) model (340M parameters). We follow a previous symptom extraction study (Zhang et al., 2022), where BERT–Large model excels at various symptom recognition, and the results are shown in Table 3.

B.2 Self-Status Determination

In addition to the life event detection model, we have also designed a life event self-status determination model. When people narrate life events on social media, they might be referring to experiences from the past rather than current experiences or even events that happened to others around them rather than themselves. Therefore, we have developed a life event self-status determination model to account for these possibilities.

The purpose of the self-status determination model is to determine whether the *posters themselves* are currently experiencing the life events detected in sentences. Thus, we define this task as a single-label binary classification task. During annotation, we ask annotators to label whether the life events in the posts are those that the poster is personally and currently experiencing. We used the labels obtained from the processing method described in Section A.6 to train the self-status determination model. Additionally, to enable the model to better learn relevant textual features, we treat samples with life events that are not currently experienced by the poster as negative samples and those with life events that are currently experienced by the poster as positive samples, using this to train the self-status determination model (for detailed annotation labels, see Appendix E).

We employ the same model used for the life event detection task to accomplish the self-status determination task. We utilize a BERT-based model with a linear layer on top of the [CLS] representation, employing a sigmoid activation function to predict the probability of the life event status, and finally, we use binary cross-entropy as the loss function. Finally, we train the self-status determination model, which yields an AUC of 76.8. This status determination model is later used in combination with the Life Event Detection model to infer life events status in downstream tasks.

C Evaluation Metrics

![A plot showing the cost factor lc_o(k) for ERDE_5 and ERDE_50.](page_728_370_484_312.png)

Figure 7: the cost factor \( lc_o(k) \) for \( ERDE_5 \) and \( ERDE_{50} \).

The following section introduces the evaluation metrics for two downstream tasks:

\[
Precision = \frac{TP}{TP + FP}, \quad Recall = \frac{TP}{TP + FN}
\]

where \( TP \) represents true positives (samples correctly classified as positive), \( TN \) represents true negatives (samples correctly classified as negative), \( FP \) represents false positives (samples incorrectly classified as positive), and \( FN \) represents false negatives (samples incorrectly classified as negative).

For suicide risk prediction, the F1 score is used as the evaluation metric, providing a harmonic mean of precision and recall:

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

For the task of early risk detection of depression, the **Early Risk Detection Error (ERDE)** is defined as:

\[
ERDE_o(d, k) = \begin{cases}
c_{fp}, & \text{for } FP \\
c_{fn}, & \text{for } FN \\
lc_o(k) \cdot c_{tp}, & \text{for } TP \\
0, & \text{for } TN
\end{cases}
\]

Here, \( c_{fp} \) and \( c_{fn} \) adjust the penalties for false positives (\( FP \)) and false negatives (\( FN \)), respectively. \( c_{fn} \) is set to 1, while \( c_{fp} \) is calculated as the ratio of positive cases in the dataset to the total number of users. The term \( lc_o(k) \in [0, 1] \) represents the cost of delayed detection for true positives (\( TP \)), and \( c_{tp} \) quantifies the penalty for delays in
detecting \( TP \). Setting \( c_{tp} \) to 1 implies that a delayed detection is treated as equivalent to a missed detection.

The function \( lc_o(k) \) governs the cost increase as the number of posts \( k \) grows and is defined as:

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

The parameter \( o \) controls the inflection point along the x-axis where the cost escalates sharply. We use \( ERDE_5 \) and \( ERDE_{50} \) as evaluation metrics for early depression detection, as shown in Figure 7.

D Detailed Life Events Taxonomy

Table 8 and 9 and 10 display the detailed life events and the 12 major categories.

<table>
  <tr>
    <th>Life Event Categories</th>
    <th>Life Events</th>
  </tr>
  <tr>
    <td rowspan="3">Health</td>
    <td>personal injury . accident or illness; got violently attacked (including sexual assault); became disabled; mental illness; recovery from mental health struggles; major surgery; hospitalization; pregnancy; pregnancy loss & abortion; menopause; abuse (including sexual abuse); began to self-harm; suicide attempt; substance abuse and addiction; recovery from addiction; oss of healthcare; physical fitness milestone; sex difficulties; change in health of family member; taking medicine</td>
  </tr>
  <tr>
    <td>change in financial state; loan; home purchase; car purchase; other major purchase; home improvement; paid off debt; major financial difficulty; major financial gain; claimed bankruptcy; foreclosure; Mortgage; personal property damaged or stolen</td>
  </tr>
  <tr>
    <td>move within same town/city; move to a different town/city; move to a different state; move to a different country; move to a different country as a refugee; became a permanent resident or citizen of a new country; move out of parent’s home; move in with family; family member moved into household; family member moved out of household; moved into assisted living; lost home / became homeless; major travel</td>
  </tr>
</table>

Table 8: Life events category 1-3, with its respective sub-events.

<table>
  <tr>
    <th>Life Event Categories</th>
    <th>Life Events</th>
  </tr>
  <tr>
    <td>Legal</td>
    <td>got arrested; lawsuit or legal action; turned over power of attorney; loss of driver’s license / DUI; went to jail or prison; released from jail or prison; minor violations of the law</td>
  </tr>
  <tr>
    <td rowspan="4">Relationship Changes</td>
    <td>began serious romantic relationship; ended serious romantic relationship; Engagement; ended engagement; marriage; divorce; marital separation; marital reconciliation; relationship became abusive; serious argument with neighbor or friend; change in number of arguments; serious argument with family member or relative; trouble with in-laws; family betrayal; parenting difficulties</td>
  </tr>
  <tr>
    <td>gain of new family member; gave birth / became a parent; adopted a child; became a grandparent; became a great-grandparent; became an aunt/uncle</td>
  </tr>
  <tr>
    <td>death of spouse; death of child; death of parent; death of pet; death of a friend; death of a loved one; death of extended family member</td>
  </tr>
</table>

Table 9: Life events category 4-7, with its respective sub-events.

E Annotation System

The context required to determine life events in social media posts is relatively long. Hence, we have designed a set of annotation methods tailored for life events. We need to provide annotators with a complete post and enable them to flexibly select any sentence within the post for annotation. The example of the annotation website is shown in Figure 8. The annotation interface displays the post that the website user is currently annotating on the left side, with the sentence currently being annotated highlighted. Users can click buttons to select the previous or next sentence. On the right side of the annotation interface are the life event annotation buttons, which users can click to tag sentences with one of the 12 life event categories. We provide three annotation options of life events for users: the sentence does not contain the life event, the sentence contains the life event but the poster is not currently experiencing it, and the sentence contains the life event that the poster is currently experiencing.

F Model and Computation Resource

The models used in our work include: bert-base (110M parameters), bert-large (340M parameters)
Post:
(TOMP): Can we do erotic novels? Medieval falconer seduces a woman from the 20th century. The plot sounds really silly when you sum it up, but it does goe. A young woman from modern times is brought to a fantasy world in medieval Ireland. She falls in love with a knight in Ireland!?! There she finds a castle/keep lorded over by a man who becomes very infatuated with her. There’s a very intense scene where he corners her and accuses her as a hawke and that he is going to “hurt” her. She is the blacksmith’s daughter, a girl who is strong and steamy. She ends up pregnant. I dunno if this will help, but in the end she writes this absurdly long run-on sentence demanding the fairies take her back to her celtic lover/husband.

<table>
  <tr>
    <th>1</th>
    <th>0</th>
    <th>-1</th>
    <th>Life Events</th>
    <th>Definition</th>
  </tr>
  <tr>
    <td>1</td>
    <td>0</td>
    <td>-1</td>
    <td>Career</td>
    <td></td>
  </tr>
  <tr>
    <td>1</td>
    <td>0</td>
    <td>-1</td>
    <td>Death</td>
    <td></td>
  </tr>
  <tr>
    <td>1</td>
    <td>0</td>
    <td>-1</td>
    <td>Eduction</td>
    <td></td>
  </tr>
  <tr>
    <td>1</td>
    <td>0</td>
    <td>-1</td>
    <td>Financial</td>
    <td></td>
  </tr>
  <tr>
    <td>1</td>
    <td>0</td>
    <td>-1</td>
    <td>Health</td>
    <td></td>
  </tr>
  <tr>
    <td>1</td>
    <td>0</td>
    <td>-1</td>
    <td>Identity</td>
    <td></td>
  </tr>
  <tr>
    <td>1</td>
    <td>0</td>
    <td>-1</td>
    <td>Legal</td>
    <td></td>
  </tr>
  <tr>
    <td>1</td>
    <td>0</td>
    <td>-1</td>
    <td>Lifestyle Change</td>
    <td></td>
  </tr>
  <tr>
    <td>1</td>
    <td>0</td>
    <td>-1</td>
    <td>New Birth in Family</td>
    <td></td>
  </tr>
  <tr>
    <td>1</td>
    <td>0</td>
    <td>-1</td>
    <td>Relationship Changes</td>
    <td></td>
  </tr>
  <tr>
    <td>1</td>
    <td>0</td>
    <td>-1</td>
    <td>Relocation</td>
    <td></td>
  </tr>
  <tr>
    <td>1</td>
    <td>0</td>
    <td>-1</td>
    <td>Societal</td>
    <td></td>
  </tr>
</table>

Figure 8: Website Annotation Interface Example.

(Devlin et al., 2019), roberta-large (355M parameters) (Liu et al., 2019), and qwen2-7B (Yang et al., 2024). The GPU resources we used are two NVIDIA RTX 4090 cards.

G Experiment Setting

When training life event classification models, we set the learning rate to 2e-3, max length to 1024, and batch size to 64. To prevent overfitting, the early stopping patience was configured at 4 epochs. In the ERDD and SRP experiments, we set the batch size to 64, the learning rate to 5e-3, the max length to 1024, and the early stopping patience to 4.
<table>
  <tr>
    <th>Life Event Categories</th>
    <th>Life Events</th>
  </tr>
  <tr>
    <td rowspan="2">Career</td>
    <td>started a new job; change in responsibilities at work; change to a different line of work; change in work hours or conditions; business readjustment; promotion; demotion; significant success at work; troubles at work; workplace discrimination or harassment; voluntary job loss (e.g., quit); involuntary job loss (e.g., fired); became a business owner / entrepreneur; retirement; unable to find work; spouse begins or stops work</td>
  </tr>
  <tr>
    <td>begin or end school/college; change in school/college; left school (without graduating); denied entry into school; obtained a certification; examination</td>
  </tr>
  <tr>
    <td rowspan="2">Lifestyle Change</td>
    <td>change in physical habits; change in responsibilities in personal life; new pet; joined the military; returned to civilian life after military; change in living conditions; revision of personal habits; change in recreation; change in social activities; vacation</td>
  </tr>
  <tr>
    <td>identified sexual preference; identified gender; came out as LGBTQ+; gender transition; change in political beliefs; change in religious/spiritual beliefs or practices; coming of age ceremony; new sexual experience; another major identity shift</td>
  </tr>
  <tr>
    <td>Societal</td>
    <td>natural disaster; war; major political event that had personal impact; met a celebrity</td>
  </tr>
</table>

Table 10: Life events category 8-12, with its respective sub-events.

Reviewer paper summaries: The paper introduces PsyEvent, a carefully-annotated social media event dataset, covering 12 major life event categories for a total of almost 8000 annotated sentences. The authors then show its usefulness by using life-events as features in a series of downstream tasks as early risk detection of depression and suicide risk prediction.

The paper proposes to extract and classify life events from social media posts and use them as features in two downstream tasks, i.e. early risk detection of depression and suicide risk classification. The authors recruit volunteer students majoring in psychology to annotate sentences of reddit posts from the SMHD dataset (Cohan et al. 2018) with life events from a modified version of a predefined taxonomy (Haimson et al. 2021). They use this data to train two models: a model to label a given post sentence with (multiple) life event categories, and another model to label whether the life events were experienced by the user or by someone else. They use these models to extract life event categories of reddit posts and evaluate the effectiveness of the life event categories as additional features in two downstream tasks. One task consists in detecting/diagnosing depression given the first k posts of a user. The other task consists in classifying the risk of suicide according to 5 classes from Gaur et al. (2019).
Summary of strengths: The construction of the dataset is well described and the decision taken are well justified
The downstream experiments are well explained and show the usefulness of the resource

1. The authors investigate the interesting question of whether life events can be useful for detecting depression and for predicting suicide risk from social media posts.

2. Good annotation process: 
2.1. annotators were trained and allowed to participate only when obtaining a certain performance;
2.2. quality control: authors annotated 10% of every batch and compared them with the annotation. If the score was below a threshold the batch was re-annotated;
3. Good ablation experiments: for the downstream tasks, the authors evaluate the effect of using different models (i.e. BERT, Roberta, Qwen) to encode the posts, the effect of only providing the model with the life event categories of the posts, and the effect of the combination of the post encodings and their life event categories.
Summary of weaknesses: Overall I found the paper very interesting and a useful contribution to the community. It was not clear to me how the two approaches: life events as features and concatenating life event features are actually implemented. I would suggest the authors to either add a schema clarifying the difference or extending Figure 3 with an overview of these two different strategies

Additionally, maybe a discussion on some of the type of errors the system make and the potential (negative) consequences for downstream task would be useful too. Are there things that Qwen would have classified correctly but the wrong life event made the model doing a wrong prediction?

[major]
1.  the authors automatically annotate life events as "experienced by the user who wrote the post" if both annotators labeled the corresponding sentence with a life event (unclear if the same life event or not). However, annotating who experienced the life event cannot be inferred without understanding the meaning of the sentence and the semantics of the events. therefore, the proposed strategy does not take meaning into account and is inaccurate.
2. lines 136-137; lines 413-415; Results were considered “significant” but no test was performed
3. some lines in the paper are not motivated and supported by evidence: Introduction, lines 93-96 (and similarly in Conclusions, lines 590-594): “We recruit annotators and provide them with rigorous training. The annotation approach we adopt is complex and incorporates numerous quality assurance measures." the authors should include more details to support this claim

[minor]
1. When using the ERDE score for evaluation, certain parameters (i.e. the penalty parameters for errors) necessary for the computation of the score were not reported. Hyperparameters for training models were not reported.
2. when using ERDE there was no justification for the choices of the “o” parameter (used to decide around which timestamp “o”, to start increasing the penalty for correct but late predictions).
3. Some important details are missing: compensation for participants, thresholds used for admittance of annotators and quality control, guidelines (only the annotation interface was shown).
Comments, suggestions and typos: The paper is well written and very clear

- Some of the data was mentioned in the setup of an experiment before its dataset was presented: ERD dataset, line 389

- “automated mental state inference” does not seem to be a used term, and was not explained

-In Figure 2 there is extra text overlapped with “Post Id: 10”

- Why is there the title of the first referenced paper on the first line of the introduction?:  “No health without mental health.”, line 24, title of Prince et al. 2007

- Some references are to arxiv preprints, but peer-reviewed versions of the papers exists (e.g. Cohan et al. 2018): they should be replaced with the published version.

- Many terms are used with different names: e.g. detection vs prediction, ERDD vs ERD of Depression

-In Figure 5 the y axis is described as “probabilities”, but they seem either counts or percentages

-suggestion: "An Unsupervised Approach to Extract Life-Events from Personal Narratives in the Mental Health Domain" Mousavi et al. CLiC-it 2021.
Overall assessment scores: 4

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