Abstract

We investigate how mental-health signals are expressed and co-constructed in online discourse during corporate layoffs. Using the lens of stress-appraisal theory, we examine the linguistic expression of stress and coping dynamics in r/Layoffs posts. We find that commenters may mirror or amplify the emotional framing of original posts (OPs). For instance, threat-framed posts evoke threat-oriented responses. We employ logit and probit regression models to predict comment-level characteristics and find that the tone and content of the OPs provide an initial framing signal, but comment-level characteristics are driven primarily by local psychological dynamics. Our mediation analysis suggests that the association between OP-level and comment-level characteristics operates primarily through indirect rather than direct pathways.

1 Introduction

Large-scale corporate layoffs create unprecedented levels of uncertainty, fear, and emotional stress among employees. Millions of people share their stories of job loss and seek advice on social media platforms. Reddit, in particular, has emerged as a major platform for deliberations on layoffs, and r/Layoffs serves as a real-time collective diary of distress, anxiety, and resilience. Recent works on stress detection and mental-health classification [11, 16] have investigated how individuals express and process psychological distress, particularly during job searches [10, 22] or unemployment [6, 7, 14, 20]. However, a crucial omission in the literature is how people react during mass layoffs.

Figure 1: This Reddit post illustrates a harm-loss as a primary appraisal, combined with low control and uncertainty in the secondary appraisal. The dominant coping response is emotion-focused, and mental health signals stress, sadness, and anger.

We operationalize ‘mass layoffs’ through concentrated discussion activity within r/Layoffs during a high-volume posting period, rather than external labor-market benchmarks. We extracted our data from the r/Layoffs subreddit with contextual metadata, including flair categories such as recently laid off, job hunting, unemployment, and previously laid off, as well as hierarchical conversation threads and user interactions. Prior studies have primarily focused on broad affective categories or generic sentiment labels, often overlooking the situational and interactional nature of stress responses during crises. In contrast, drawing insights from stress-appraisal theory [13], we explicitly examine both what people express at the moment of job loss and how these expressions shape subsequent community responses within discussion threads. Using the transactional model of stress, we analyse the interrelationships between primary appraisals, secondary appraisals, and coping strategies as they unfold through post-comment interactions. Specifically, we focus on, RQ1: how post-level appraisal framing relates to comment-level responses, RQ2: how coping strategies propagate in discussion threads, and RQ3: how these relationships vary across psychological dimensions.

Table 1: Sample (and partially Paraphrased for anonymity) Reddit expressions in accordance with Stress Appraisal Theory

Category	Sub-Category	Example Reddit Expression
Primary Appraisal	Threat	I'm terrified there will be another round next week. I can't sleep thinking about it.
	Harm or Loss	Losing my job and healthcare in the same day broke me. I don't know how to recover.
	Challenge	This might be the push I needed to try a new career path.
	Benign	My team wasn't affected, but I hope others are doing okay.
Secondary Appraisal	High (Perceived) Control	I've been through this before. I know the steps I need to take next.
	Low (Perceived) Control	Nothing I do seems to matter. Everything feels out of my hands.
	Uncertainty	I honestly don't know what to do now. Everything feels chaotic.
	Blame Others	The ruling class will grind us into little pieces and use our bones to feed their dogs.
	Blame Self	I wish I took more training classes offered by company to expand my knowledge.
	Resource Adequacy	Luckily I have savings and support from my partner, so I'll be fine.
	Prob.-Solving Confidence	I've already updated my portfolio and lined up interviews.
Coping Styles	Problem-Focused	Made a spreadsheet of 50 companies hiring this month. Time to apply.
	Emotion-Focused	Trying to remind myself this wasn't my fault and things will improve.
	Meaning-Focused	I think the idea of a “safe” job is engrained in people's heads and needs to be removed.
	Avoidance/ Avoidant	I haven't touched my email in days. I just can't deal with it right now.
	Seeking Support	Does anyone have tips on negotiating severance? Really need advice.
Flairs (subset)	Recently Laid Off	Got the email this morning. Still in shock and don't know what's next.
	Job Hunting	Sent out 20 applications today. The market is rough.
	Previously Laid Off	Last year was brutal, but I promise things get better with time.
	Advice	Here are five things I wish I knew before filing unemployment…
	Discussion	How is everyone preparing for possible cuts in 2025?

On the methodology front, beyond exploratory analyses, we employ logit and probit regression models to predict comment-level appraisals, coping strategies, and mental-health expressions from post-level framing, community context, and local psychological signals. Next, we conduct mediation analyses to compare direct post effects with indirect pathways operating through intermediate appraisals or mental health signals, thereby clarifying whether emotional outcomes arise from direct emotional transmission or socially mediated sensemaking processes [1]. Contrary to prior studies that rely on coarse sentiment or broad mental-health categories, we elucidate how stress and coping are socially co-constructed within domain-specific online communities, extending stress-appraisal theory from individual cognition to collective discourse.

2 Stress Appraisal Theory

Drawing on Stress Appraisal Theory, we examine three intricately interlinked psychological dimensions: (1) primary and secondary appraisals, (2) coping styles, and (3) mental-health indicators.

Primary and Secondary Appraisals: Stress arises not only from the stressor itself but also from an individual's appraisal of the situation [13]. Primary appraisal evaluates a situation as a threat, harm or loss, challenge, or benign, whereas secondary appraisal assesses available resources and perceived control [12, 13, 18]. Accordingly, we identify linguistic expressions, such as danger, opportunity, helplessness, blame, etc., and classify them into primary appraisal labels (threat, harm or loss, challenge, or benign) and secondary appraisal labels (low or high perceived control, problem-solving confidence, uncertainty, blame patterns, and resource adequacy). Together, these appraisals shape psychological reactions and behavioural adaptations following job loss [18]. Notably, job insecurity can generate distress exceeding even the effects of specific childhood traumas [18].

Coping Styles: Coping refers to the cognitive and behavioural strategies used to manage stress, including problem-solving, emotion-focused, avoidance, and seeking support [4, 12, 23]. Based on linguistic cues such as planning and action verbs, emotional expression, withdrawal language, and requests for help or advice, we classify coping as problem-focused, emotion-focused, meaning-focused, avoidant, or support-seeking. We aim to investigate whether appraisal patterns influence coping choices, such as whether high perceived control encourages problem-focused coping.

Mental-Health Dimensions: Drawing insights from digital mental-health research, we identify seven dimensions from semantic and contextual cues: anxiety, depression, stress or burnout, anger, resilience, social support, and emotional numbing or avoidance [3, 9, 15]. These dimensions capture both distress and adaptive functioning in layoff-related discussions.

Figure 2: Heatmap showing the relationship between Reddit post flairs and primary appraisal categories.

Figure 3: Heatmap showing the relationship between Reddit post flairs and secondary appraisal categories.

3 Data

Reddit is a rich source for identifying mental health signals during periods of stress, including job loss and employment uncertainty [2, 11, 17, 24]. Thus, we extract our dataset from the r/Layoffs subreddit between August 2025 and November 2025. We deliberately focused on a short time span during which the layoff crisis was at its peak, avoiding a longer period to prevent data contamination from other stress factors. We do not claim this period represents an objective peak in macroeconomic layoffs, but rather a phase of heightened community activity within the subreddit. After preprocessing, the final corpus consists of 987 Reddit posts and 10,233 corresponding user comments. This subreddit provides a unique setting for studying stress and coping, as users share their personal experiences, emotional reactions, and workplace concerns in real-time, anonymously. Each post contains structured metadata (e.g., flair, score, and timestamp) and a narrative that reflects individual interpretations of job insecurity. Comments add conversational context, often deliberating on coping strategies and available community support. This multi-layered dataset offers rich insights into how people collectively appraise and respond to job loss.

Flairs: Psychological expression on Reddit is shaped by the surrounding social context [8]. On the r/Layoffs subreddit, Reddit flairs allow users to self-categorize their situation (e.g., “recently laid off,” “job hunting,” “previously laid off”). These contextual markers shape the tone, content, and emotional intensity of posts and therefore function as important features in our labeling framework.

LLM-based Annotation: Using transformer-based embeddings and a few-shot–guided LLM classifier, each post and comment receives multi-label assignments capturing primary and secondary stress appraisals, coping strategies, and mental-health signals. To ground the model in the transactional stress–coping framework, we provided a curated set of expert-annotated exemplars illustrating conceptual mappings between linguistic cues and theoretical categories—for example, a challenge-framed post (“Just got laid off, but honestly, this might push me to finally build something of my own”) versus a threat-oriented post (“My severance is unclear, and HR won't respond. This is unbelievably stressful.”).

All instances were classified using the gpt-4o-mini model with deterministic decoding (temperature = 0), ensuring reproducibility and consistent label assignment [19, 21]. To evaluate annotation quality, we conducted a systematic human validation study on a stratified small subset of the corpus. Human annotations were produced using the same labeling guidelines as those embedded in the LLM prompts. Reliability was consistently high across appraisal and coping dimensions, but, as expected, this was not the case between adjacent constructs (e.g., threat vs. harm/loss). Due to space constraints, we do not include detailed prompt templates or reliability analyses. Overall, we find that the LLM classifier achieves expert-level consistency while enabling scalable, theory-driven annotation across thousands of posts and comments.

Figure 4: Relationship between post-level and comment-level primary appraisals.

4 Exploratory Analysis

This section describes the patterns of association among appraisals, coping styles, and mental-health signals in layoff-related discourse. Unless otherwise stated, reported values reflect proportions and conditional distributions rather than correlation coefficients.

Primary and Secondary Appraisals Across Flairs: Figure 2 elucidates that posts depicting direct layoff experiences, especially recently laid off, show concentrated patterns of high threat and harm/loss, indicating strong emotional involvement. Conversely, flairs related to meme or resources contain minimal emotional aspects, unlike experiential flairs. Therefore, the social context signalled by flairs may shape both the emotional and cognitive framing of the discourse. Similarly, Figure 3 indicates that Recently-laid-off posts exhibit higher levels of low_control and blame_other, indicating unpredictability and external causation. In contrast, question posts are associated with seeking_information. These patterns are consistent with Stress Appraisal Theory, i.e., situational framing (via flair) shapes how users evaluate their coping resources and, subsequently, their coping behaviours.

Figure 5: Relationship between post-level primary appraisals and comment-level secondary appraisals.

Primary Appraisal Propagation from Post to Comment: Figure 4 reports the relationship between primary appraisals expressed in OPs and the appraisal patterns of subsequent comments. OPs classified as threat or harm/loss generally evoke similar threat-oriented responses, indicating that commenters often echo the poster's perception of situational danger. Interestingly, a significant portion of the replies is threat-oriented, indicating that community discourse often accentuates risk, regardless of the initial framing.

Primary Appraisal in Posts to Secondary Appraisal in Comments: Interestingly, Figure 5 reveals that challenge-framed posts mostly generate high-control responses from commenters, with 40% of replies reflecting confidence or a perceived ability to act. In contrast, harm/loss- and threat-framed posts exhibit more distributed secondary appraisal patterns. Uncertainty-framed posts also attract relatively high proportions of uncertainty in replies (27%). Overall, this indicates that commenters adjust their appraisal style in ways that both reflect and subtly reshape the emotional framing of the OP.

Figure 6: Bivariate heatmaps depicting (A) the conditional distributions of comment-level coping classifications as a function of post-level coping and (B) the conditional distributions of comment-level mental-health signals as a function of post-level coping.

Coping Strategies and Mental Health: Consistent with the Transactional Model of Stress and Coping, in Figure 6, we observe a clear pattern of coping convergence and emotional diffusion. For instance, emotion-focused posts generate predominantly emotion-focused comments, with strong mental-health signals of anger, anxiety, resilience, and sadness, indicating that these comments prioritize emotional responses over instrumental advice. Similarly, in accordance with adaptive coping matching, problem-focused posts attract problem-focused responses and also display resilience and anxiety in comments. Interestingly, irrespective of the coping strategies in the OP, most comments are overwhelmingly emotion-focused and display resilience, indicating the community's role in reinforcing shared sense-making and providing reassurance.

5 Predictive Analysis

Figure 7: Logit and probit regression coefficients for significant predictors of primary appraisals of layoff discourse, i.e., threat. Bars represent estimated coefficients from logit (purple) and probit (pink) models. Stars denote statistical significance for logit models ( p < 0.10,  p < 0.05,  p < 0.01).

Figure 8: Logit and probit regression coefficients for significant predictors of secondary appraisals of layoff discourse, i.e., uncertainty. Bars represent estimated coefficients from logit (purple) and probit (pink) models. Stars denote statistical significance for logit models ( p < 0.10,  p < 0.05,  p < 0.01).

Figure 9: Logit and probit regression coefficients for significant predictors of mental health of layoff discourse, i.e., sadness. Bars represent estimated coefficients from logit (purple) and probit (pink) models. Stars denote statistical significance for logit models ( p < 0.10,  p < 0.05,  p < 0.01).

We employ logit and probit regression models, standard tools for discrete choice and limited-dependent-variable analysis in the social sciences [5]. Estimating both specifications enables us to assess the robustness of coefficient signs. We find results are consistent across models, and this consistency across models reflects robustness in directional associations.

Primary Appraisal Dynamics of Layoff Discourse: To examine the mechanisms underlying appraisal formation in comments, we estimated a set of binary response models predicting whether a comment expresses a harm/loss, threat, challenge, or benign primary appraisal. For each outcome, we fit both logistic and probit regression models and considered the same set of explanatory variables, i.e., post-level appraisals, coping styles, mental-health indicators, subreddit flairs, and standardized post scores, as well as comment-level secondary appraisals, coping strategies, and mental-health signals.

Figure 7 reports coefficients for statistically significant predictors of threat. Both models exhibit mostly consistent coefficient signs and magnitudes, indicating the robustness of our findings. We find that threat appraisals are primarily associated with comment-level anger, sadness (negatively), and anxiety. Responses are also associated with emotion- and problem-focused responses and high uncertainty. For brevity, we have not reported the findings for other primary appraisals, but we do find variations across them. For instance, high control is positively associated with challenge posts but negatively associated with harm/loss posts. Overall, we find that while OP-level appraisals provide an initial framing signal, the linguistic expression in comments is driven primarily by local psychological dynamics, consistent with transactional stress theory.

Secondary Appraisal Dynamics of Layoff Discourse: Similar to our previous analysis, we also tried to predict secondary appraisals in comments, i.e., high control, low control, uncertainty, and blaming others, from post-level context and comment-level psychological signals. Figure 8 reports logit–probit regression coefficients for uncertainty. We find that uncertainty posts are positively associated with comment-level anxiety but negatively associated with comment-level anger, resilience, and sadness. We also find a negative relationship with comment-level support-seeking or problem-focused coping styles. Similarly, we note that high perceived control is strongly associated with comment-level resilience and negatively associated with anxiety, sadness, and support-seeking or emotion-focused coping, indicating an efficacy-oriented response pattern. Conversely, low perceived control is positively associated with emotion-focused and support-seeking coping strategies, reflecting helplessness-oriented stress processing. We have not reported the probit/logit findings for all secondary appraisals, but for all of them, comment-level aspects dominate prediction, while post-level signals play a weaker anchoring role.

Coping Dynamics of Layoff Discourse: We find that across all four coping outcomes, comment-level emotional and cognitive states are the primary determinants, while post-level variables exert weaker but interpretable effects. For instance, problem-focused coping is positively associated with comment-level resilience and high perceived control and negatively associated with anger, sadness, anxiety, and uncertainty, indicating an action-oriented response pattern under conditions of perceived efficacy. Conversely, emotion-focused coping shows strong positive associations with anger, sadness, anxiety, and uncertainty and a pronounced negative association with high perceived control.

Similarly, seeking social support is strongly predicted by comment-level distress signals, particularly anger, anxiety, uncertainty, and low control. This suggests that support-seeking emerges as a socially mediated response to heightened stress. Collectively, these results reinforce a transactional view of coping in which responses are shaped primarily by local psychological dynamics within comments, while post-level framing serves as a secondary contextual cue.

Mental-Health Dynamics of Layoff Discourse: Finally, we consider mental-health dynamics. As an illustrative example, Figure 9 reports the logit and probit estimates for Sadness. We find a positive association between sadness in the OP and comment-level characteristics such as uncertainty, low control, and support-seeking. Conversely, we find a negative association between sadness and problem-focused coping dynamics. Similarly, perceived uncertainty is a positive predictor of anxiety and sadness, while high control exhibits a positive relationship with resilience. These patterns are intuitive, but once again, we note that comment-level aspects, rather than the characteristics of the OP, dominate the prediction of mental-health dynamics.

Table 2: Mediation analysis results for the top 10 pathways (from original post → comment), ranked by absolute indirect effect. Prefixes p_ and c_ denote post-level and comment-level psychological dimensions, respectively. Therefore, each row reports a mediation chain from post-level psychological framing (X) to a downstream comment outcome (Y) through an intermediate comment state (M). ACME denotes the Average Causal Mediation Effect (indirect effect) with 95% confidence intervals. ADE denotes the Average Direct Effect of X on Y not operating through M. Total is the sum of indirect and direct effects. % Med. indicates the proportion of the total effect explained by mediation. Significance levels are indicated as follows: *** p < 0.001.

#	X (Post)	M (Mediator)	Y (Outcome)	ACME [95% CI]	ADE	Total	% Med.
1	p_primary:challenge	c_mental_health:anger	c_secondary:blame_other	− 0.152 [ − 0.202, − 0.107]***	− 0.041	− 0.192	78.9
2	p_primary:challenge	c_secondary:blame_other	c_mental_health:anger	− 0.149 [ − 0.185, − 0.104]***	− 0.025	− 0.174	85.5
3	p_coping:problem_focused	c_mental_health:anger	c_secondary:blame_other	− 0.148 [ − 0.177, − 0.116]***	− 0.025	− 0.172	85.7
4	p_secondary:blame_self	c_mental_health:resilience	c_secondary:high_control	0.138 [0.106, 0.176]***	− 0.009	0.129	–
5	p_primary:challenge	c_secondary:blame_other	c_coping:emotion_focused	− 0.133 [ − 0.171, − 0.090]***	− 0.035	− 0.168	79.1
6	p_coping:problem_focused	c_secondary:blame_other	c_mental_health:anger	− 0.133 [ − 0.161, − 0.103]***	− 0.037	− 0.170	78.4
7	p_primary:challenge	c_secondary:high_control	c_primary:threat	− 0.130 [ − 0.171, − 0.092]***	− 0.012	− 0.142	91.4
8	p_primary:challenge	c_secondary:blame_other	c_primary:threat	− 0.129 [ − 0.164, − 0.089]***	− 0.013	− 0.142	90.5
9	p_primary:challenge	c_mental_health:anger	c_coping:emotion_focused	− 0.128 [ − 0.169, − 0.083]***	− 0.040	− 0.168	76.1
10	p_mental_health:resilience	c_mental_health:anger	c_secondary:blame_other	− 0.128 [ − 0.181, − 0.074]***	− 0.030	− 0.158	81.1

Mediation Analysis of Psychological Pathways: Table 2 presents the ten pathways with the largest indirect effects. Challenge-framed and problem-focused posts are often associated with lower anger, external blame, threat, and emotion-focused coping in comments, primarily through intermediate comment-level appraisals and mental-health signals. In these pathways, the indirect effect (ACME) is substantially larger than the direct effect (ADE), accounting for approximately 76–91% of the total association. This suggests that post-level framing is related to downstream responses mainly through comment-level sensemaking rather than through direct emotional transmission [1]. Row 4 exhibits inconsistent mediation, as the positive indirect association through resilience is partially offset by a small negative direct association with high control. Therefore, the proportion mediated cannot be substantively interpreted for this pathway. Given the cross-sectional design and lack of temporal ordering, these findings represent statistical decompositions of structured associations between OPs and comments rather than causal relationships between them.

Limitations: This study has several limitations that open up avenues for future research. First, the cross-sectional and observational design prevents us from making causal inferences, particularly in mediation-style analyses without temporal ordering. Second, comments are nested within posts and users, and independence assumptions may be compromised. Third, annotation primarily relies on LLM-assisted labeling with partial human validation, which may introduce construct ambiguity. Finally, the focus on a single subreddit and the short time window limit generalizability beyond this context. Future research could extend this approach to cross-platform settings and longitudinal user trajectories.

6 Conclusion

Our study advances computational social science research by providing a theory-grounded, large-scale analysis of intricate psychological dynamics in layoff-related discourse on Reddit. A core contribution of our study is the empirical evidence suggesting that comment-level psychological states, rather than post-level framing, are the strongest predictors of downstream appraisal outcomes. The strong agreement between the logit and probit models ensures the robustness of our findings. Notably, mediation analyses suggest that the associations between OP-level appraisals and downstream comment-level appraisals or coping styles are largely indirect.

Theoretically, these findings enrich transactional stress theory in the context of online platforms by showing that appraisal signals embedded in OPs can potentially function as collective cognitive cues that shape interpretation, attribution, and emotional regulation at scale. However, linguistic expression in comments is primarily a function of local psychological dynamics. Our results suggest that communities such as r/Layoffs operate as ecosystems of social coping, where deliberative sensemaking and secondary appraisal play crucial roles in overcoming collective distress during periods of economic disruption. Overall, this study contributes to the growing literature on AI for social good by demonstrating how computational models can elucidate latent psychosocial mechanisms in large-scale social media data, and in turn inform systems that foster resilience, empathy, and constructive discourse in vulnerable communities.

Ethical Considerations: Reddit posts and comments were collected through its public API and analysed anonymously. We report only aggregate patterns and paraphrased posts to protect user privacy.

GenAI Usage Disclosure: LLMs were used in the research process to improve code for executing experiments, clarify language, correct grammatical errors, and generate figure layouts. The models used include ChatGPT and Claude Code, with different versions applied at different stages. LLMs were also used for the classification task itself, as described in Section 3.

Acknowledgments

This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation.

Source

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ACM Digital Library

Imported from ACM’s structured HTML source. ACM Reference Format: Aparup Khatua. 2026. How Communities Process Layoffs: Appraisal, Coping, and Emotional Mediation on Reddit. In 37th ACM Conference on Hypertext (HT '26), September 14--18, 2026, London, United Kingdom. ACM, New York, NY, USA 7 Pages. https://doi.org/10.1145/3800935.3830872

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