The Role of Social Interactions in Mitigating Psychological Distress During the COVID-19 Pandemic: A Study in Sri Lanka

Anuruddhika Rathnayake; Isuri Mapa; Isuru Thilakasiri; Janaka Ekanayake; Parakrama Ekanayake; Ramila Thowfeek; Roshan Godaliyadda; Tharaka Fonseka; Vijitha Herath

REVIEW 3 major objections 5 minor 3 references

The Role of Social Interactions in Mitigating Psychological Distress During the COVID-19 Pandemic: A Study in Sri Lanka

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Unsupervised clustering of 921 Sri Lankan survey responses separates the population into a 'socially connected' group and a 'socially reserved' group, and the connected group reports lower loneliness, emptiness, and fear of death during…

desk verdict The central contrast is baked into the clustering; a useful descriptive study that does not support its main causal claim. read the letter →

arxiv 2412.01843 v2 pith:4GPGOJCN submitted 2024-11-21 physics.soc-ph

Isuru Thilakasiri , Tharaka Fonseka , Isuri Mapa , Roshan Godaliyadda , Vijitha Herath , Ramila Thowfeek , Anuruddhika Rathnayake , Parakrama Ekanayake

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Janaka Ekanayake

This is my paper · ORCID

classification physics.soc-ph

keywords COVID-19mentalhealthsocialconnectednesssociallyreservedgroupfactoranalysisunsupervisedclusteringspectralk-meansSriLankasurvey

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Using a nationwide face-to-face survey collected during the November–December 2021 lockdown period in Sri Lanka, the paper argues that the psychological damage of the pandemic was mediated by how people managed social ties. From 3,020 households, the authors analyze 921 fully complete responses and, without a prior hypothesis, let factor analysis and unsupervised clustering find the structure. They identify four latent factors and two population subgroups: a 'socially connected' group that maintained or increased interactions with neighbors and wider social circles, and a 'socially reserved' group that preferred minimal contact. The connected group reported markedly lower increases in loneliness, emptiness, and fear of death, while the reserved group leaned on social media, hobbies, and other coping mechanisms. If true, the result means that fostering real interpersonal connection, rather than digital distraction, should be central to pandemic mental-health policy, and that support cannot be targeted by demographics alone.

What carries the argument

The load-bearing machinery is a two-stage unsupervised pipeline applied in a four-dimensional factor space. First, PCA with Varimax rotation and a 0.4 loading threshold reduces the 11 psychological items to four factors with eigenvalues above 1; the rotated component matrix is the central object of the analysis because it determines which items define each factor. Second, spectral clustering with a $\sigma$ sweep over the Gaussian affinity parameter $\sigma$ uses the largest stable eigengap to fix the number of clusters at two, and k-means then assigns each respondent to a cluster. The decisive comparison is the difference in cluster mean answers: large positive differences on 'social connectedness' and 'relationship with neighbors' separate the connected group, while large differences on loneliness, emptiness, fear of death, and social media separate the reserved group.

What would settle it

Rerun the same factor analysis and two-stage clustering on the full 3,020 responses with 'Cannot say' retained as a neutral middle category instead of dropped, and with the sample reweighted to national demographics; the central claim would be refuted if the two-cluster solution no longer separates on the social-connectedness items or if the connected group's lower loneliness, emptiness, and fear-of-death differences disappear.

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Extended reading notes

Core claim

The paper's central claim is that the common denominator of psychological impact during Sri Lanka's COVID-19 lockdowns was social connectedness. On the 11 psychological items that survived cleaning, PCA-based factor analysis with Varimax rotation produced four factors, named Coping Mechanisms, Symptoms, Work-Life Balance, and Peer Interactions/Connections, and spectral clustering followed by k-means split respondents into two groups in this factor space. The 'socially connected' group increased social connectedness and neighbor relations relative to their earlier levels and reported lower loneliness, emptiness, and fear of death; the 'socially reserved' group showed the opposite pattern and relied more on social media, hobbies, and substance use. The authors also report that social media loaded with coping mechanisms rather than with peer interaction, that no demographic variable separated the two groups, and that the overall sample's mean responses resembled the reserved group more than the connected group.

Load-bearing premise

The load-bearing premise is that the 921 cleaned responses, which are 88% male, 92% household heads, and drawn disproportionately from households village officers identified as most severely impacted, still represent Sri Lankan adults closely enough to support population-level conclusions about mental-health response.

Editorial extensions

If this is right

Maintaining or increasing contact with neighbors and wider social circles during lockdowns is associated with lower pandemic-related emotional distress, so interventions should protect real-world social ties rather than only providing information.
Social media use functioned as a coping or distraction behavior rather than a substitute for peer connection, so digital campaigns that simply push online engagement may miss the mechanism that protects mental health.
Because no demographic variable separated the two groups, generic targeting by income, education, ethnicity, age, or gender would not identify who needs connection support during a future crisis.
The socially reserved group is the higher-risk group for loneliness, emptiness, and fear of death, and proactive outreach to people who prefer minimal interaction could reduce that gap.
Organized online or physically distanced socializing events are a concrete policy suggestion the authors draw for future pandemic preparedness.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A replication that imputes the 'Cannot say' responses or reweights the 921 responses to national demographics would test whether the connected/reserved split is a stable population feature or a product of the cleaning steps, since the final sample is 88% male and 92% household heads.
If the factor structure generalizes, the same four factors and two clusters could be sought in other low- and middle-income countries with comparable lockdown surveys; finding the same loadings would strengthen the case that social connectedness is the common denominator rather than a Sri Lankan particularity.
A longitudinal design following the same respondents after restrictions lifted would test an implicit corollary: that the connected group's psychological advantage persisted, or that the reserved group's reliance on coping mechanisms faded once normal interaction resumed.
The claim that social media is not perceived as peer interaction could be tested directly by asking respondents why they used each platform, distinguishing active one-to-one contact from passive browsing.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Read this one if you want a clean example of a circular clustering claim. The paper uses a decent Sri Lankan survey (n=921 after cleaning), runs PCA-based factor analysis, finds four factors, then k-means on those factors splits people into 'socially connected' and 'socially reserved.' The connected group shows lower loneliness, emptiness, and fear of death, and the authors conclude that maintaining social connections mitigated distress. The problem is that the symptom items are in the factor space that k-means partitioned. Factor 2 is literally Loneliness, Emptiness, and Fear of Death. So the clusters are optimized to differ on those variables. The contrast in Figures 9 and 10 is partly a property of the clustering objective, not an independent empirical discovery. The stress-test note is right, and it is load-bearing: it does not just weaken the causal reading, it undermines the internal validity of the core contrast.

What is genuinely useful: the dataset is public, the factor analysis is standard and reported in detail, and the observation that social media loads with coping mechanisms rather than with peer interactions is interesting and not circular. The authors are transparent about the filtering (Table 1 shows the attrition and the demographic skew), and they correctly note that no demographic variable distinguished the clusters. The math is standard and the citation pattern is adequate, with the relevant COVID social-connection literature cited.

The other weaknesses are the expected ones: the sample is 88% male and 92% household heads, deliberately oversampled in 'most severely impacted' households, so the population-level conclusions do not follow. And the title's 'mitigating' implies causality from a cross-sectional design. These are serious, but the circularity is the showstopper. It could be fixed: cluster on the social-interaction items alone and test the symptom factor as an out-of-sample outcome, or at least present the clusters as descriptive profiles and drop the causal language.

Who is this for? People working on pandemic mental health in South Asia, and anyone teaching a methods class on why you cannot characterize clusters using the same variables that defined them. It deserves a serious referee—the data and the descriptive parts are worth engaging with—but as a scientific claim about social connectedness mitigating distress, it does not hold. I would reject it after review, not desk-reject it, because the analysis is redoable with a small change.

Referee Report

3 major / 5 minor

Summary. The paper analyzes a nationwide face-to-face survey of 3,020 Sri Lankan households, retains 921 complete responses after cleaning, and applies PCA-based factor analysis to identify four factors (Coping Mechanisms, Symptoms, Work-Life Balance, Peer Interactions/Connections). Using spectral clustering to select K=2 and k-means to partition the four-dimensional factor space, the authors label the resulting groups as 'socially connected' and 'socially reserved' and claim that the connected group experienced less loneliness, emptiness, and fear of death, concluding that maintaining social connections mitigated psychological distress during the pandemic.

Significance. If the central contrast were valid, this study would be a valuable addition to the sparse literature on the psychological impact of COVID-19 in low-income countries, and it is commendable that the dataset and methodology are described in detail and the data are publicly available. The confirmatory factor analysis with several fit indices and the transparency about the data-cleaning pipeline are strengths. However, the central claim rests on a circular analysis: the clusters are defined in a space that includes the very symptom variables used to demonstrate their differences, so the paper does not provide independent evidence that social connectedness mitigates distress. The substantial sample attrition and the deliberate oversampling of severely impacted households further limit generalization.

major comments (3)

[2.4, 3 (Table 3, Table 4, Figures 9-10)] The clustering input and the outcome variables are the same. Section 2.4 states that k-means was applied to the four-dimensional reduced space after PCA with Varimax rotation; Table 3 and Table 4 show that Factor 2, 'Symptoms', is composed of the Loneliness, Emptiness, and Fear of Death items. Figures 9 and 10 then compare the two clusters on these exact item means. Because k-means optimizes within-cluster distances in this four-dimensional space, it is expected to separate clusters along all input dimensions, including the symptom factor. Consequently, the finding that the 'socially connected' cluster has lower loneliness, emptiness, and fear of death is partly an artifact of the clustering objective rather than an independent result. To support the mitigation claim, the authors should either cluster on the social-connection items only (e.g., Factor 4 and possibly Factor 3) and then test the symptom factor as a held-out variable, or apply a statistical adjustment that acknowledges the cluster labels are functions of the symptom variables.
[2.1, Table 1, Figure 2] The processed sample is not representative of the Sri Lankan population, so the population-level claims are unsupported. Section 2.1 reports that the 921 responses retained represent 30.5% of the original 3,020, and Table 1 shows the processed dataset is 88.4% male versus 70.7% in the full survey, 92.3% household heads versus 80.1%, and 0% unemployed versus 20.96% in the full survey. Additionally, Figure 2 states that village officers assisted in selecting households 'most severely impacted' by the pandemic, so the sample is deliberately enriched for high distress. The paper should not describe these respondents as representative of the population (e.g., Section 2.1 claims 'the selected households were representative of the population’s diversity'); the conclusions should be restricted to the processed sample or the analyses should incorporate survey weights.
[Title, Section 6 Conclusion] The causal phrasing in the title and conclusion goes beyond what a cross-sectional design can support. The paper's title says 'The influence of social interactions in mitigating psychological distress' and the Conclusion states that the connected group had lower symptoms because they maintained social connections. Because the data are cross-sectional, the association between cluster membership and symptom scores could reflect reverse causality (distressed individuals withdraw socially) or shared causes such as personality or social preference. Please revise the language to describe associations rather than causal effects.

minor comments (5)

[2.3, Figure 3] The choice of the number of clusters (K=2) relies on a free parameter sigma, but no sensitivity analysis is reported; please show that the two-cluster solution is robust across a range of sigma values.
[2.2] The phrase 'complementary factor analysis (CFA)' should be 'confirmatory factor analysis' to avoid confusion with the earlier exploratory analysis.
[Discussion] The sentence 'This observation of observation of social media acing as a mere distraction' contains a duplicated phrase and a typo ('acing' should be 'acting').
[3, Figures 9 and 10] It would be helpful to report the cluster sizes and standardized effect sizes for the contrasts in Figures 9 and 10, not just mean differences, to allow readers to judge the practical importance of the differences.
[References] The reference format for 'COVID-19 Cases (2024)' and 'COVID-19 Deaths (2024)' in the Introduction is inconsistent with the rest of the reference list; please format them as standard entries.

Circularity Check

1 steps flagged · score 6.0 of 10

Main contrast is partly circular: k-means clusters are built on the same symptom factor later used to show that the 'socially connected' group has lower distress.

fitted input called prediction [Section 2.4 (K-means clustering) and Section 3 (Results, Tables 3-4, Figures 9-10)]
"In this study, K-means clustering was applied to the reduced space of the dataset with to four dimensions after PCA with Varimax rotation. ... The questions regarding 'Loneliness, Emptiness and Fear of death' were categorized under the factor appropriately named 'Symptoms.' ... From the results shown in Figures 9, 10, it was concluded that the clustering has been done depending on the 'socially connected or reserved' nature of the respondent."

The k-means input is the four-dimensional PCA factor space, which includes the 'Symptoms' factor composed of exactly the Loneliness, Emptiness, and Fear of death items. K-means partitions this space by minimizing within-cluster distances, so the two clusters are constructed to be separated jointly on all input dimensions, including the symptom factor. The paper's later finding that the 'socially reserved' group has higher loneliness, emptiness, and fear of death is therefore a re-description of the variables used to create the clusters, not an independent consequence of social connectedness.

full rationale

The paper's central inference reduces partially by construction: k-means is applied to the factor space that includes the 'Symptoms' factor, and the subsequent comparison of the two clusters on the Loneliness, Emptiness, and Fear of death items is effectively a restatement of the clustering objective. This is not an out-of-clustering validation, so the claim that social interactions mitigated psychological distress is not independently tested by this contrast. However, the circularity is only partial: the algorithm does not literally force the specific direction or magnitude of the symptom differences, and the factor analysis itself is a legitimate descriptive step. I found no load-bearing self-citation circularity; citations to Ilangarathna et al. and Senarath et al. are data and method references, not imported uniqueness claims or ansatz justifications. The score of 6 reflects that the central cluster contrast is partly tautological, while the paper retains some independent content in its descriptive factor structure and demographic analysis.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The central claim rests on assumptions about data quality, missingness, and cluster validity that are not independently verified. In particular, the processed sample is highly skewed and the cluster solution is not validated, so the population-level conclusion is fragile.

free parameters (2)

sigma (spectral clustering bandwidth) = not reported
Tuneable parameter in the affinity matrix controlling the zooming effect; the selected sigma value for the final clustering is not stated, so the K=2 decision is not fully reproducible.
number of clusters K = 2
Chosen from the spectral eigengap plot, a subjective heuristic with no sensitivity analysis for other K values.

assumptions (3)

domain assumption Self-reported Likert responses for the 12 psychological impact questions are valid measures of the respondents' psychological state.
The survey relies on retrospective self-reports of changes in loneliness, emptiness, relationships, and social media use, with no clinical assessment or validation against other instruments (Section 2.1).
domain assumption Dropping responses with missing values or 'Cannot say' answers does not materially bias the results.
The paper discards 2,099 of 3,020 responses to reach n=921 without demonstrating that missingness is random or unrelated to distress; the remaining sample is heavily skewed toward male household heads (Section 2.1).
ad hoc to paper The two-cluster solution from spectral clustering and k-means reflects real subpopulations rather than artifacts of the algorithm.
The choice of K=2 is justified only by a visual eigengap plot, and no cluster stability analysis, silhouette scores, or bootstrap validation is provided (Section 2.3-2.4).

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Cite this review

Pith. "Pith review of The Role of Social Interactions in Mitigating Psychological Distress During the COVID-19 Pandemic: A Study in Sri Lanka." pith.science (2026). https://pith.science/paper/4GPGOJCN

@misc{pith2026241201843,
  author       = {Pith},
  title        = {Pith review of: The Role of Social Interactions in Mitigating Psychological Distress During the COVID-19 Pandemic: A Study in Sri Lanka},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4GPGOJCN}},
  note         = {Machine review of arXiv:2412.01843}
}

read the original abstract

Massive changes in many aspects related to social groups of different socioeconomic backgrounds were caused by the COVID-19 pandemic and as a result, the overall state of mental health was severely affected globally. This study examined how the pandemic affected Sri Lankan citizens representing a range of socioeconomic backgrounds in terms of their mental health. The data used in this research was gathered from 3,020 households using a nationwide face-to-face survey, from which a processed dataset of 921 responses was considered for the final analysis. Four distinct factors were identified by factor analysis (FA) that was conducted and subsequently, the population was clustered using unsupervised clustering to determine which population subgroups were affected similarly. Two such subgroups were identified where the respective relationships to the retrieved principal factors and their demographics were thoroughly examined and interpreted. This resulted in the identification of contrasting perspectives between the two groups toward the maintenance and the state of social relationships during the pandemic, which revealed that one group was more 'socially connected' in nature resulting in their mental state being comparatively better in coping with the pandemic. The other group was seen to be more 'socially reserved' showing an opposite reaction toward social connections while their mental well-being declined showing symptoms such as loneliness, and emptiness in response to the pandemic. The study examined the role of social media, and it was observed that social media was perceived as a substitute for the lack of social connections or primarily used as a coping mechanism in response to the challenges of the pandemic

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

3 extracted references · 3 canonical work pages

[1]

Abbas, J., Wang, D., Su, Z., and Ziapour, A. (2021). The role of social media in the advent of covid-19 pandemic: crisis management, mental health challenges and implications. Risk Manag. Healthc. Policy 14, 1917–1932. doi: 10.2147/RMHP .S284313 Acar Güvendir, M., and Özer Özkan, Y . (2022). Item removal strategies conducted in exploratory factor analysis...

work page doi:10.2147/rmhp 2021
[3]

D., and Omar, B

doi: 10.3389/ fpsyg.2020.562213 Apuke, O. D., and Omar, B. (2021). Fake news and COVID-19: modelling the predictors of fake news sharing among social media users. Telematics Inform. 56:101475. doi: 10.1016/j.tele.2020.101475 Athapathu, A., Navaratnam, D., Doluweera, M., and Liyanage, G. (2022). Child emotional and behavioral difficulties and parent stress...

work page arXiv 2021
[11]

K., Chopra, M., and Kumar, S

doi: 10.3389/ fpsyg.2020.559819 Aggarwal, K., Singh, S. K., Chopra, M., and Kumar, S. (2022). Role of social media in the COVID-19 pandemic, 91–115. doi: 10.4018/978-1-7998-8413-2.ch004 Akuratiya, A. D., and Akuratiya, D. A. (2021). The effect of social media on spreading fear and panic during COVID-19 pandemic in Sri Lanka . Available at: www. rsisintern...

work page arXiv 2022

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [1]

Abbas, J., Wang, D., Su, Z., and Ziapour, A. (2021). The role of social media in the advent of covid-19 pandemic: crisis management, mental health challenges and implications. Risk Manag. Healthc. Policy 14, 1917–1932. doi: 10.2147/RMHP .S284313 Acar Güvendir, M., and Özer Özkan, Y . (2022). Item removal strategies conducted in exploratory factor analysis...

work page doi:10.2147/rmhp 2021

[2] [3]

D., and Omar, B

doi: 10.3389/ fpsyg.2020.562213 Apuke, O. D., and Omar, B. (2021). Fake news and COVID-19: modelling the predictors of fake news sharing among social media users. Telematics Inform. 56:101475. doi: 10.1016/j.tele.2020.101475 Athapathu, A., Navaratnam, D., Doluweera, M., and Liyanage, G. (2022). Child emotional and behavioral difficulties and parent stress...

work page arXiv 2021

[3] [11]

K., Chopra, M., and Kumar, S

doi: 10.3389/ fpsyg.2020.559819 Aggarwal, K., Singh, S. K., Chopra, M., and Kumar, S. (2022). Role of social media in the COVID-19 pandemic, 91–115. doi: 10.4018/978-1-7998-8413-2.ch004 Akuratiya, A. D., and Akuratiya, D. A. (2021). The effect of social media on spreading fear and panic during COVID-19 pandemic in Sri Lanka . Available at: www. rsisintern...

work page arXiv 2022