Protective Factor-Aware Dataset

• The Protective Factor-Aware Dataset is a longitudinal collection of Reddit posts annotated with 19 risk factors and 5 protective factors to support suicide risk forecasting.
• Its construction employs temporal sequencing and a sliding-window framework with high annotation reliability (Fleiss’ κ ≥ 0.79) for clinically informed risk prediction.
• By explicitly modeling resilience signals like social support and coping strategies, the dataset enables interpretable, multi-task modeling for improved suicide risk intervention.

Searching arXiv for the cited dataset paper and closely related "protective-factor-aware" datasets for corroboration. A Protective Factor-Aware Dataset is a temporally structured, annotated resource designed for subsequent suicide risk modeling that explicitly represents both risk factors and protective factors alongside suicide risk labels. In the formulation introduced for social-media analysis, the dataset is built from 12 years of Reddit posts from r/SuicideWatch and records, for each post, text content, a 19-dimensional risk-factor binary vector, a 5-dimensional protective-factor binary vector, and a four-level suicide risk label. Its distinguishing feature is that protective factors such as social support and coping strategy are treated as first-class signals rather than omitted context, reflecting the claim that protective factors can mitigate suicide risk by moderating the impact of risk factors (Li et al., 14 Jul 2025).

1. Definition and conceptual scope

Within suicide risk prediction, the term denotes a dataset that jointly encodes adverse correlates of suicidality and resilience-related correlates, rather than modeling risk factors alone. The associated paper introduces a “novel Protective Factor-Aware Dataset” and pairs it with a framework for predicting subsequent suicide risk over time on social media (Li et al., 14 Jul 2025). The dataset is therefore not merely a corpus of crisis-language posts; it is a longitudinal representation of user trajectories in which clinically motivated factor annotations are aligned with temporal risk-state transitions.

The primary motivation is methodological. Prior work on suicide risk detection is described as revealing insights into current suicide risk on social media, while paying little attention to predicting subsequent suicide risk over time. The same work also states that existing approaches ignore protective factors and focus predominantly on risk factors alone (Li et al., 14 Jul 2025). In this context, “protective factor-aware” signifies that the dataset is explicitly constructed to support models whose inputs include both liabilities and resilience components.

A plausible implication is that the dataset occupies an intermediate position between clinical annotation schemes and sequential NLP benchmarks. It preserves fine-grained psychosocial labels, yet organizes them in post-level chronological sequences suitable for temporal prediction.

2. Corpus construction and temporal design

The source domain is Reddit, specifically the subreddit r/SuicideWatch, chosen for its focus on first-person reports of suicidal thoughts and behaviors. The collection period spans June 15, 2010 to September 18, 2022, which is described as approximately 12 years (Li et al., 14 Jul 2025). All public posts were retrieved via the Reddit API, personally identifying metadata were removed, and only English-language posts longer than 20 characters were retained.

User selection imposed a temporal-depth criterion: only users with sustained activity, defined as at least 7 posts within any one-week window, were kept. This yielded a final corpus of 237 users and 2,515 posts, with a mean inter-post interval of approximately 2.54 days (Li et al., 14 Jul 2025). These constraints matter because the target task is subsequent risk prediction rather than single-post classification; without recurrent posting behavior, temporal supervision would be sparse or ill-posed.

The dataset represents each user’s timeline as a chronological post sequence

$P_u = (p_{u,1},p_{u,2},\dots,p_{u,T_u}),$

where user $u\in\{1,\dots,N\}$ and $T_u$ is the total posts for $u$ (Li et al., 14 Jul 2025). For modeling, sliding windows are constructed so that, given history length $l$, each training example is $\bigl(p_{u,t-l+1},\dots,p_{u,t}\bigr)$ with ground-truth $y_{u,t+1}$, and the windows shift by one post, yielding $\sum_u (T_u-l)$ training samples (Li et al., 14 Jul 2025). This formulation makes the dataset intrinsically sequential and distinguishes it from static suicide-risk corpora.

3. Annotation schema: risk levels, risk factors, and protective factors

The annotation scheme comprises three components: suicide risk levels, risk factors, and protective factors. Suicide risk levels are based on C-SSRS and include four categories: Indicator (IN), Ideation (ID), Behavior (BR), and Attempt (AT) (Li et al., 14 Jul 2025). Indicator denotes no explicit suicide reference; Ideation denotes suicidal thoughts with no plan; Behavior denotes an explicit plan or self-harm behavior; Attempt denotes mention of a recent or past suicide attempt.

Risk factors are multi-label annotations spanning 19 categories. These are: mental health illness (MHI), physical health/characteristic (PH), substance use (SU), hopelessness (HL), emotion dysregulation (ED), low self-esteem (LS), poor school performance (PSP), low socio-economic status (LSS), interpersonal violence (IV), prior self-harm/suicidal thought/attempt (PSST), poor social support (PSS), interpersonal difficulty (IDF), dysfunctional family (DF), exposure to others’ suicide (EOS), stressful life event (SLE), traumatic experience (TE), cognitive deficit (CD), suicide means (SM), and sexual orientation related issues (SORI) (Li et al., 14 Jul 2025).

Protective factors are also multi-label, with five categories identified as “key resilience components”: social support (SS), coping strategy (CS), psychological capital (PC), sense of responsibility (SR), and meaning in life (ML) (Li et al., 14 Jul 2025). The schema has no further hierarchical nesting beyond the distinction between risk and protective categories.

The central representational unit for a post $p_{u,t}$ is:

• text content $x_{u,t}$,
• risk factor binary vector $u\in\{1,\dots,N\}$0,
• protective factor binary vector $u\in\{1,\dots,N\}$1,
• suicide risk label $u\in\{1,\dots,N\}$2 (Li et al., 14 Jul 2025).

The full dataset is defined as

$u\in\{1,\dots,N\}$3

This formalization is important because it makes protective-factor awareness operational rather than rhetorical: the protective signals are explicit supervised variables, not latent properties to be inferred post hoc.

4. Annotation procedure and reliability

The annotations were produced by three PhD-level annotators with backgrounds in Psychology and Computer Science. They were trained on detailed guidelines with examples for each label, and pilot rounds were conducted on 200 posts with iterative refinement of definitions and boundary cases (Li et al., 14 Jul 2025). In the final round, all 2,515 posts were independently annotated, with disagreements resolved by majority vote and group discussion.

Reported inter-annotator agreement is high for both task components. Fleiss’ $u\in\{1,\dots,N\}$4 is given for suicide risk levels, and Fleiss’ $u\in\{1,\dots,N\}$5 for risk-factor and protective-factor labels (Li et al., 14 Jul 2025). These values indicate that the label space, although clinically and linguistically nuanced, was operationalized with substantial consistency.

For an expert readership, the procedural significance lies in the coexistence of ordinal-like crisis labels and multi-label psychosocial factors within one annotation workflow. This suggests that the dataset was intended not only for end-task classification, but also for interpretable modeling in which factor-level signals can be examined independently of raw text.

5. Statistical profile and imbalance characteristics

The dataset contains $u\in\{1,\dots,N\}$6 users and $u\in\{1,\dots,N\}$7 posts, with an average of 10.6 posts per user and an average inter-post interval of 2.54 days (Li et al., 14 Jul 2025). It includes 19 risk factors and 5 protective factors.

The post-level suicide risk distribution is:

• Indicator (IN): 37.5%
• Ideation (ID): 31.5%
• Behavior (BR): 24.0%
• Attempt (AT): 7.0% (Li et al., 14 Jul 2025)

Attempt labels are explicitly identified as the rarest class, requiring model robustness to skew. No synthetic balancing was applied; instead, downstream models are said to account for imbalance via ordinal loss and uncertainty-weighted multi-task learning (Li et al., 14 Jul 2025). This is a notable design choice because it preserves the empirical frequency structure of crisis discourse rather than regularizing the dataset itself into a balanced benchmark.

The dataset’s imbalance profile is clinically plausible: severe outcomes are rarer than ideation or non-explicit indicators. A plausible implication is that performance on the AT class is likely to be especially sensitive to temporal context and factor interactions, since naive frequency-based learning would underrepresent that state.

6. Data format, ethics, and accessibility

The dataset is released under a Creative Commons CC-BY 4.0 license, and its use requires citation of the work (Li et al., 14 Jul 2025). Ethical handling is emphasized: all posts are de-identified, and no usernames or exact locations are included. The research is described as having been conducted under Institutional Review Board exemption for publicly available social media data (Li et al., 14 Jul 2025).

It is published as two files, JSON and CSV. Each record contains:

• user_id
• timestamp
• post_id
• text
• risk_level
• risk_factors
• protective_factors (Li et al., 14 Jul 2025)

An example JSON record includes a post labeled "BR" with risk factors ["SM","PSST","HL"] and protective factors ["SS"] (Li et al., 14 Jul 2025). This field design preserves enough structure for both sequence modeling and auxiliary factor prediction, while remaining compact enough for standard NLP pipelines.

From a data-engineering perspective, the format supports at least three classes of use. First, one can perform direct supervised learning on post text. Second, one can incorporate the structured factor vectors as auxiliary targets or features. Third, one can reconstruct user timelines and apply sliding-window forecasting as specified in the paper. This suggests that the dataset was designed to support both single-task and multi-task formulations.

7. Modeling role, interpretation, and relation to broader “protective-factor-aware” datasets

The dataset was introduced together with a Dynamic Factors Influence Learning approach that captures the varying impact of risk and protective factors on suicide risk transitions over time (Li et al., 14 Jul 2025). The accompanying paper states that the model significantly outperforms state-of-the-art models and LLMs across three datasets, and that the approach provides interpretable weights that help clinicians better understand suicidal patterns and enable more targeted intervention strategies (Li et al., 14 Jul 2025). In that setting, the dataset functions not merely as labeled evidence but as the substrate for dynamic influence estimation.

The phrase “protective-factor-aware” also appears in other domains, but with different meanings. In manufacturing safety, the SH17 dataset is described as a “Protective-Factor–Aware Resource for Human Safety,” where the relevant factors are body parts and personal protective equipment such as helmets, gloves, and safety vests (Ahmad et al., 2024). In job advertising, FairJob is characterized as protective-factor-aware because it provides a stand-in proxy for a sensitive attribute under strong anonymization, enabling fairness–utility analysis in recommendation systems (Vladimirova et al., 2024). These uses broaden the term beyond mental-health resilience.

Even so, the suicide-risk dataset retains a specific conceptual identity. Here, “protective factor” refers to resilience components such as social support, coping strategy, psychological capital, sense of responsibility, and meaning in life, all annotated directly at the post level (Li et al., 14 Jul 2025). That differs materially from PPE compliance or protected-attribute proxies. A common misconception would be to treat all “protective-factor-aware” datasets as instances of fairness-aware data curation; in this case, the construct is primarily psychosocial and clinically grounded rather than demographic or operational.

A plausible implication is that the term is becoming domain-general while remaining semantically domain-specific. In suicide-risk prediction, it denotes datasets that model resilience alongside vulnerability. In other application areas, it may denote exposure to safety equipment, fairness-relevant attributes, or related moderating variables. The 2025 suicide-risk resource is therefore best understood as a canonical protective factor-aware dataset in the mental-health sense: longitudinal, clinically annotated, and explicitly structured for subsequent risk forecasting (Li et al., 14 Jul 2025).