Social Burden Overview

• Social burden is the aggregate cost borne by individuals and groups due to institutional, algorithmic, and systemic designs, quantified in units like time, effort, and health.
• It spans multiple disciplines with operational metrics ranging from DALYs in health to cost functions in algorithmic recourse and subjective caregiving indices.
• Research shows that improvements in institutional performance can obscure shifted individual and subgroup burdens, highlighting the need for dynamic, context-specific analysis.

Searching arXiv for the cited papers and closely related work on “social burden.” Social burden denotes the costs borne by individuals, groups, or populations that are not exhausted by institution-centered, firm-centered, or system-centered performance criteria. Across current research, it is operationalized in domain-specific ways: as the expected cost that qualified individuals must incur to be classified positively in strategic classification, as the expected burden on qualified false negatives who must pursue algorithmic recourse, as perceived burdensomeness in suicide-note analysis, as the cognitive and emotional burden of household organization, as caregiver burden, as time-cost in maintaining social ties, as Disability-Adjusted Life Years or age-standardized Years Lived with Disability in population health, as externalized victim costs in data breaches, and as total expected delay induced by risk-averse routing (Milli et al., 2018, Barrainkua et al., 4 Sep 2025, Ghosh et al., 2022, Barigozzi et al., 16 May 2025, Mahpouya et al., 24 Oct 2025, Takano et al., 2016, Hunter et al., 2024, Tao et al., 15 Oct 2025, Alkarmi et al., 22 Mar 2026, Nikolova et al., 2014). These formulations differ in units and mechanisms, but they converge on a common analytical concern: burdens are frequently shifted onto people or subpopulations even when headline metrics indicate institutional success.

1. Formal scope and units of analysis

A recurrent structure in the literature is the passage from an individual-level burden to a group-level or population-level aggregate. In strategic classification, with feature space $X$, labels $Y=\{0,1\}$, classifier $f:X\to\{0,1\}$, and manipulation cost $c(x,x')\ge 0$, the individual burden of a true positive $x$ is

$b_f(x)=\min_{x':\,f(x')=1} c(x,x'),$

and the social burden is

$B(f)=E[b_f(X)\mid Y=1],$

which measures “the expected cost that positive individuals need to incur to be classified positively,” regardless of whether they actually adapt (Milli et al., 2018). In recourse, the corresponding object is defined for qualified individuals who are classified negatively: $$b_{f,g}(x,s)=\delta((x,s),g_f(x))\cdot \mathbf{1}\{y=1 \wedge f(x)=0\}$$, and group burden is

$$\mathcal{B}_{f,g}^s = E[\delta((x,s),g_f(x))\mid S=s,Y=1,f(x)=0]\times (1-TPR_s),$$

equivalently $\mathcal{B}_{f,g}^s=\mu_s\cdot (1-TPR_s)$ (Barrainkua et al., 4 Sep 2025).

Population-health work uses additive morbidity and mortality metrics. In stroke modeling, the per-agent burden is $Y=\{0,1\}$0, where $Y=\{0,1\}$1 is Years of Life Lost for agents who die of stroke and $Y=\{0,1\}$2 is Years Lived with Disability for agents who survive stroke; population DALYs are the sum over stroke cases (Hunter et al., 2024). In macro-mental-health analysis, age-standardized YLD is

$Y=\{0,1\}$3

standardized to the GBD 2021 world standard population (Tao et al., 15 Oct 2025). In mean-risk routing, a social planner measures performance by total expected delay,

$Y=\{0,1\}$4

and defines the Price of Risk Aversion as the worst-case ratio between social cost under risk-averse and risk-neutral Wardrop equilibria (Nikolova et al., 2014).

This range of formalizations suggests that social burden is not a single invariant quantity. Rather, it is a family of aggregation schemes that map localized costs—effort, distress, disability, lost time, financial loss, or expected delay—into group or population welfare objects.

2. Burden in algorithmic decision-making

In strategic classification, individuals best-respond to a deployed rule, and “institutional utility” is therefore the strategic utility

$Y=\{0,1\}$5

where $Y=\{0,1\}$6 (Milli et al., 2018). Under outcome-monotonic cost, the institution’s optimal rules can be taken to be threshold classifiers on $Y=\{0,1\}$7, namely $Y=\{0,1\}$8. The central theorem establishes a monotone trade-off: $Y=\{0,1\}$9 is quasi-concave in $f:X\to\{0,1\}$0, achieving its global maximum at some $f:X\to\{0,1\}$1 with $f:X\to\{0,1\}$2; $f:X\to\{0,1\}$3 is non-decreasing in $f:X\to\{0,1\}$4; and whenever $f:X\to\{0,1\}$5, one also has $f:X\to\{0,1\}$6. A corollary is that any $f:X\to\{0,1\}$7 that strictly improves strategic utility over the non-strategic optimum necessarily raises social burden above $f:X\to\{0,1\}$8 (Milli et al., 2018).

The same framework yields subgroup burden measures. With protected attribute $f:X\to\{0,1\}$9, group burden is $c(x,x')\ge 0$0, and the social gap is $c(x,x')\ge 0$1. If group $c(x,x')\ge 0$2 is disadvantaged in features, meaning $c(x,x')\ge 0$3 for all $c(x,x')\ge 0$4, and the cost function satisfies the stated likelihood-condition, then $c(x,x')\ge 0$5 is positive and non-decreasing in $c(x,x')\ge 0$6. If costs differ multiplicatively as $c(x,x')\ge 0$7 with $c(x,x')\ge 0$8, then $c(x,x')\ge 0$9 and is again non-decreasing in $x$0. Accordingly, movement toward strategy-robustness widens the burden gap between advantaged and disadvantaged groups (Milli et al., 2018).

Algorithmic recourse generalizes this concern from classification boundaries to post-hoc actionability. A key result is that equal-opportunity, i.e. $x$1 parity, does not guarantee equal social burden because groups may still differ in $x$2, the average recourse cost among false negatives, or in the distribution of $x$3 (Barrainkua et al., 4 Sep 2025). This directly challenges the standard equal cost paradigm. The proposed MISOB procedure addresses burden by iterative re-weighting without using sensitive attributes at training time. Starting from a plain classifier, it computes current predicted labels, estimates burden, defines weights

$x$4

and re-trains with weighted loss (Barrainkua et al., 4 Sep 2025). On the Adult dataset with race split and Growing Spheres recourse, the plain classifier plus GS had worst-group burden $x$5, $x$6 burden $x$7, $x$8, and $x$9; post-processing for equal $b_f(x)=\min_{x':\,f(x')=1} c(x,x'),$0 increased worst-group burden to $b_f(x)=\min_{x':\,f(x')=1} c(x,x'),$1 while setting $b_f(x)=\min_{x':\,f(x')=1} c(x,x'),$2 and $b_f(x)=\min_{x':\,f(x')=1} c(x,x'),$3; MISOB reduced worst-group burden to $b_f(x)=\min_{x':\,f(x')=1} c(x,x'),$4, raised $b_f(x)=\min_{x':\,f(x')=1} c(x,x'),$5 to $b_f(x)=\min_{x':\,f(x')=1} c(x,x'),$6, and kept $b_f(x)=\min_{x':\,f(x')=1} c(x,x'),$7 (Barrainkua et al., 4 Sep 2025).

The design implication is not that robustness or fairness should be discarded. Rather, the literature recommends explicitly choosing points on the $b_f(x)=\min_{x':\,f(x')=1} c(x,x'),$8 Pareto-front, considering Nash thresholds in the interval $b_f(x)=\min_{x':\,f(x')=1} c(x,x'),$9, or optimizing $B(f)=E[b_f(X)\mid Y=1],$0 when burden is normatively relevant (Milli et al., 2018).

3. Burdensomeness, mental load, and caregiving

In suicide research, perceived burdensomeness is a highly specific interpersonal-risk construct: in Joiner’s theory it is “the feeling of being a burden on friends, family, and/or society, accompanied by the inference that one’s death is worth more than one’s life.” Ghosh et al. operationalize this with CEASE-v2.0, containing 364 full notes and 4,932 sentences, and CoMCEASE-v2.0, a manually translated Hindi–English code-mixed counterpart (Ghosh et al., 2022). Three expert annotators labeled note-level PB/Not-PB and TB/Not-TB; 55 notes (15.1%) were marked PB, 60 (16.4%) TB, and 17 (4.67%) both, with Fleiss’ $B(f)=E[b_f(X)\mid Y=1],$1 for PB and $B(f)=E[b_f(X)\mid Y=1],$2 for TB. Temporal orientation was annotated as Past, Present, or Future on 4,885 usable sentences, with Fleiss’ $B(f)=E[b_f(X)\mid Y=1],$3; the proportions were 30.9% Past, 41.7% Present, and 27.5% Future. The TEMF architecture combines GloVe and fastText meta-embeddings, BERTBase document encoding, local sentence Transformers, additive attention using temporal and emotion-label embeddings, hierarchical abstraction, and a multitask objective over PB and TB. On CEASE-v2.0, TEMF achieved macro-F1 of 54.02% for PB and 56.64% for TB; on CoMCEASE-v2.0, 51.27% and 55.21%, with ablations showing that removing temporal-orientation inputs reduces F1 by up to 4 points and omitting emotion labels costs up to 8 points (Ghosh et al., 2022).

Mental-load research shifts the unit of analysis from text to household organization. Barigozzi et al. define mental load as “the combination of thinking work and its emotional weight,” formally

$B(f)=E[b_f(X)\mid Y=1],$4

and use the TIMES Observatory, covering 415 couples in Emilia-Romagna, Italy (Barigozzi et al., 16 May 2025). The main emotional load indicator is

$B(f)=E[b_f(X)\mid Y=1],$5

with each component measured on a 0–100 scale. Time-use gaps are defined as $B(f)=E[b_f(X)\mid Y=1],$6 and $B(f)=E[b_f(X)\mid Y=1],$7. Women self-report as main organizer for household work in 54.7 percent of cases and for childcare in 45.0 percent; employed women report thinking about household organization while at work in 41.10 percent of cases versus 9.58 percent for employed men, and about childcare organization in 47.26 percent versus 13.51 percent (Barigozzi et al., 16 May 2025). Emotional fatigue is also higher for women: $B(f)=E[b_f(X)\mid Y=1],$8 versus $B(f)=E[b_f(X)\mid Y=1],$9, $$b_{f,g}(x,s)=\delta((x,s),g_f(x))\cdot \mathbf{1}\{y=1 \wedge f(x)=0\}$$0, $$b_{f,g}(x,s)=\delta((x,s),g_f(x))\cdot \mathbf{1}\{y=1 \wedge f(x)=0\}$$1, $$b_{f,g}(x,s)=\delta((x,s),g_f(x))\cdot \mathbf{1}\{y=1 \wedge f(x)=0\}$$2. A common misconception is that burden here is fully explained by absolute time spent. The reported linear-probability models show instead that within-couple gaps matter more than women’s own hours: for organizer attribution, the coefficient on $$b_{f,g}(x,s)=\delta((x,s),g_f(x))\cdot \mathbf{1}\{y=1 \wedge f(x)=0\}$$3 is significant while the coefficient on woman’s own household time is not, and the same pattern holds for childcare (Barigozzi et al., 16 May 2025).

Caregiving research introduces a psychometrically explicit burden index for observational data. CareBI is constructed from 18 NSOC items organized into six first-order factors—Overload, Difficulty, Mood, Health, Social Participation, Relationship Quality—which cluster into three second-order domains: Objective, Subjective, and Interpersonal burden (Mahpouya et al., 24 Oct 2025). Exploratory factor analysis used polychoric correlations, minimum residual estimation, and direct oblimin rotation; the six-factor solution had $$b_{f,g}(x,s)=\delta((x,s),g_f(x))\cdot \mathbf{1}\{y=1 \wedge f(x)=0\}$$4, $$b_{f,g}(x,s)=\delta((x,s),g_f(x))\cdot \mathbf{1}\{y=1 \wedge f(x)=0\}$$5, and $$b_{f,g}(x,s)=\delta((x,s),g_f(x))\cdot \mathbf{1}\{y=1 \wedge f(x)=0\}$$6. The final bifactor model estimated with WLSMV had $$b_{f,g}(x,s)=\delta((x,s),g_f(x))\cdot \mathbf{1}\{y=1 \wedge f(x)=0\}$$7, $$b_{f,g}(x,s)=\delta((x,s),g_f(x))\cdot \mathbf{1}\{y=1 \wedge f(x)=0\}$$8, and $$b_{f,g}(x,s)=\delta((x,s),g_f(x))\cdot \mathbf{1}\{y=1 \wedge f(x)=0\}$$9. Individual scores $$\mathcal{B}_{f,g}^s = E[\delta((x,s),g_f(x))\mid S=s,Y=1,f(x)=0]\times (1-TPR_s),$$0 on the general factor are transformed to

$$\mathcal{B}_{f,g}^s = E[\delta((x,s),g_f(x))\mid S=s,Y=1,f(x)=0]\times (1-TPR_s),$$1

then rounded to obtain the final CareBI score (Mahpouya et al., 24 Oct 2025). Reliability is summarized by $$\mathcal{B}_{f,g}^s = E[\delta((x,s),g_f(x))\mid S=s,Y=1,f(x)=0]\times (1-TPR_s),$$2. Higher CareBI predicts sleep interruption ($$\mathcal{B}_{f,g}^s = E[\delta((x,s),g_f(x))\mid S=s,Y=1,f(x)=0]\times (1-TPR_s),$$3 per SD), feeling “down, depressed, or hopeless” ($$\mathcal{B}_{f,g}^s = E[\delta((x,s),g_f(x))\mid S=s,Y=1,f(x)=0]\times (1-TPR_s),$$4), and interference with paid work ($$\mathcal{B}_{f,g}^s = E[\delta((x,s),g_f(x))\mid S=s,Y=1,f(x)=0]\times (1-TPR_s),$$5); k-means clustering yields empirically derived cut-points of 0–30 for low burden, 31–50 for moderate burden, and 51–100 for high burden (Mahpouya et al., 24 Oct 2025).

4. Social ties, bridging, and routine news exposure

Takano and Fukuda model the burden of maintaining social relationships as a time-cost of social grooming that rises with tie strength. If $$\mathcal{B}_{f,g}^s = E[\delta((x,s),g_f(x))\mid S=s,Y=1,f(x)=0]\times (1-TPR_s),$$6 is the number of days on which grooming has occurred and $$\mathcal{B}_{f,g}^s = E[\delta((x,s),g_f(x))\mid S=s,Y=1,f(x)=0]\times (1-TPR_s),$$7 is the observation period, the marginal cost of one additional grooming act is

$$\mathcal{B}_{f,g}^s = E[\delta((x,s),g_f(x))\mid S=s,Y=1,f(x)=0]\times (1-TPR_s),$$8

For an ego with $$\mathcal{B}_{f,g}^s = E[\delta((x,s),g_f(x))\mid S=s,Y=1,f(x)=0]\times (1-TPR_s),$$9 ties of strengths $\mathcal{B}_{f,g}^s=\mu_s\cdot (1-TPR_s)$0, the total grooming cost per day is

$\mathcal{B}_{f,g}^s=\mu_s\cdot (1-TPR_s)$1

where $\mathcal{B}_{f,g}^s=\mu_s\cdot (1-TPR_s)$2 is mean tie strength (Takano et al., 2016). Empirically, they find a scaling law $\mathcal{B}_{f,g}^s=\mu_s\cdot (1-TPR_s)$3 with $\mathcal{B}_{f,g}^s=\mu_s\cdot (1-TPR_s)$4, implying that deep ties become disproportionately more costly. In the associated Yule–Simon reinforcement model, the tie-strength distribution follows $\mathcal{B}_{f,g}^s=\mu_s\cdot (1-TPR_s)$5, and $\mathcal{B}_{f,g}^s=\mu_s\cdot (1-TPR_s)$6 increases with $\mathcal{B}_{f,g}^s=\mu_s\cdot (1-TPR_s)$7; higher $\mathcal{B}_{f,g}^s=\mu_s\cdot (1-TPR_s)$8 therefore yields wider and shallower ego-networks. Across six communication systems, fitted $\mathcal{B}_{f,g}^s=\mu_s\cdot (1-TPR_s)$9 include Twitter $Y=\{0,1\}$00, Phone $Y=\{0,1\}$01, and SMS $Y=\{0,1\}$02 (Takano et al., 2016).

A distinct form of burden emerges in information diffusion. Chen et al. define a user’s cascade bridging value in cascade $Y=\{0,1\}$03 as

$Y=\{0,1\}$04

and overall User Bridging Magnitude as $Y=\{0,1\}$05, aggregating $Y=\{0,1\}$06 across cascades and normalizing by the maximum number of cascades any user participates in (Chen et al., 2021). Subjective well-being is inferred from original tweets by tri-polarity sentiment classification and summarized as

$Y=\{0,1\}$07

In hierarchical regression, $Y=\{0,1\}$08 is the strongest negative predictor of SWB, with $Y=\{0,1\}$09, $Y=\{0,1\}$10, $Y=\{0,1\}$11, and model $Y=\{0,1\}$12; the top 20% of users by bridging magnitude experienced an average SWB drop of 0.33 after the onset of COVID-19, twice the decline of the bottom 20% (Chen et al., 2021).

Routine news engagement on social media has been characterized as a latent psychosocial cost borne through everyday exposure. A quasi-experimental study on Bluesky used the entire platform history—approximately 2.37 million users, 26 million posts, and 45 million comments—to compare Treated users who engaged the algorithmic News feed with matched Control users who never did (Pal et al., 20 Jan 2026). Propensity estimation employed 520 covariates and AdaBoost (SAMME) with 200 decision-tree stumps; after stratification into propensity-score bins, the matched sample contained 83,711 Treated and 81,345 Control users, and the maximum Standardized Mean Difference fell from 1.34 to 0.21. Outcomes span affective, behavioral, and cognitive measures, including SVM-inferred depression, anxiety, stress, loneliness, LIWC-2015 categories, posting frequency, interactivity ratio, Coleman–Liau Index, verbosity, repeatability, complexity, and LIWC cognitive processes (Pal et al., 20 Jan 2026). The reported individual treatment effects show strong engagement heterogeneity: for depression, bookmarking has $Y=\{0,1\}$13, commenting $Y=\{0,1\}$14, quoting $Y=\{0,1\}$15, and liking $Y=\{0,1\}$16; for anxiety, bookmarking has $Y=\{0,1\}$17, quoting $Y=\{0,1\}$18, while commenting is negligible; for readability, quoting improves CLI with $Y=\{0,1\}$19, whereas bookmarking reduces CLI with $Y=\{0,1\}$20 (Pal et al., 20 Jan 2026). A common misunderstanding is that all engagement is uniform in effect. The evidence instead indicates systematic trade-offs: increased depression, stress, and anxiety, yet decreased loneliness and increased social interaction, with passive bookmarking carrying substantially larger psychosocial cost than commenting or quoting (Pal et al., 20 Jan 2026).

5. Population health, epidemic policy, and macro-social burden

Population-health models often treat burden as a welfare aggregate over morbidity and mortality trajectories. In an agent-based model of stroke, agents aged at least 35 are initialized with demographic and baseline risk-factor distributions drawn from Irish and Framingham data, then updated daily through age progression, risk-awareness intervention, stroke occurrence, acute outcome, and DALY computation (Hunter et al., 2024). Five-year stroke risk is modeled using age-specific logistic regressions from Hunter et al. and Herrgårdh et al.; daily risk is $Y=\{0,1\}$21, assuming uniform risk over five years. The intervention acts at birthdays ages 50, 60, 70, 80, and 90, and if $Y=\{0,1\}$22, it induces smoking cessation, BMI reduction by $Y=\{0,1\}$23, and SBP/DBP reduction by $Y=\{0,1\}$24, with the same reductions applied to linked family members in one scenario (Hunter et al., 2024). Over 10 years and 1,000 stochastic runs, baseline mean strokes are 551 and mean DALYs 17,344.6; Scenario 1 reduces strokes to 543 and DALYs to 17,142.8; Scenario 2 reduces strokes to 539 and DALYs to 17,107.0, all with $Y=\{0,1\}$25 by Student’s $Y=\{0,1\}$26-test (Hunter et al., 2024).

Pandemic-containment economics formalizes social burden as the joint loss of lives and output. Noguchi writes social loss as $Y=\{0,1\}$27, economic loss as $Y=\{0,1\}$28 with $Y=\{0,1\}$29, and combined pandemic loss as

$Y=\{0,1\}$30

where $Y=\{0,1\}$31 converts social loss into the same units as output (Noguchi, 2020). The first-order condition is

$Y=\{0,1\}$32

or, in the multiphase setting, $Y=\{0,1\}$33. Distributional burden enters through sector-specific output cuts $Y=\{0,1\}$34, since $Y=\{0,1\}$35 and $Y=\{0,1\}$36 with $Y=\{0,1\}$37 (Noguchi, 2020). The paper therefore treats compensation $Y=\{0,1\}$38 as a device for burden-sharing across groups whose exposure to output loss differs sharply. It also argues, following Lerner and Samuelson, that domestically held debt used to finance compensation does not necessarily impose a future-generation burden if it does not crowd out capital formation (Noguchi, 2020).

Macro-level mental-health work defines burden via age-standardized YLD and then links it to development indicators across economic, educational, social, and technology domains (Tao et al., 15 Oct 2025). The social indicators are life expectancy at birth, unemployment, and prevalence of undernourishment. For young adults aged 20–39, unemployment is the strongest social-risk driver: for bipolar disorder $Y=\{0,1\}$39, anxiety $Y=\{0,1\}$40, schizophrenia $Y=\{0,1\}$41, and depression $Y=\{0,1\}$42; regionally, the unemployment–YLD association reaches approximately $Y=\{0,1\}$43 in South Asia and $Y=\{0,1\}$44 in Sub-Saharan Africa (Tao et al., 15 Oct 2025). Life expectancy is protective, with negative correlations such as $Y=\{0,1\}$45 for depression ages 20–39 and $Y=\{0,1\}$46 for depression ages 40+ (Tao et al., 15 Oct 2025). Lag analysis estimates average lags of 1.3 years for economic indicators, 0.9 years for technology indicators, 4.2 years for educational indicators, approximately 1.5 years for unemployment, and approximately 2.8 years for life expectancy. This supports a temporally differentiated policy view in which unemployment reduction acts on shorter horizons while educational investments operate over longer ones (Tao et al., 15 Oct 2025).

6. Externalized costs, network inefficiency, and comparative themes

In data-breach economics, social burden is explicitly contrasted with corporate cost. Corporate cost includes crisis management, legal fees, regulatory fines, forensic investigations, stock-price drops, and notification costs; social cost is the externalized burden on individuals whose records were exposed, comprising unrecoverable out-of-pocket losses, opportunity cost of time spent resolving fraud, and monetized healthcare expenditures for distress (Alkarmi et al., 22 Mar 2026). The per-victim social cost is

$Y=\{0,1\}$47

where $Y=\{0,1\}$48 and $Y=\{0,1\}$49 with $Y=\{0,1\}$50, and $Y=\{0,1\}$51 million and upper bound $Y=\{0,1\}$52 million settlement; Target 2013 has $Y=\{0,1\}$53 billion against $Y=\{0,1\}$54 million and $Y=\{0,1\}$55 million (Alkarmi et al., 22 Mar 2026). The paper also reports a market saturation effect: as the discounted supply of exposed records rises, the conversion rate from compromised records to identity-theft victims declines over time (Alkarmi et al., 22 Mar 2026).

A more abstract but related systems view appears in mean-risk selfish routing. Here users optimize mean delay plus a variability penalty, with per-edge cost

$Y=\{0,1\}$56

under the coefficient-of-variation bound $Y=\{0,1\}$57 (Nikolova et al., 2014). Social cost, however, remains the total expected delay $Y=\{0,1\}$58. The resulting Price of Risk Aversion,

$Y=\{0,1\}$59

is bounded by $Y=\{0,1\}$60 in general networks with $Y=\{0,1\}$61, and by $Y=\{0,1\}$62 in series-parallel networks, independent of topology size (Nikolova et al., 2014). This result isolates a burden created not by malicious behavior but by decentralized responses to uncertainty.

Taken together, these literatures suggest several recurrent themes. First, institutional or system-level metrics often obscure burdens shifted onto individuals: strategic utility can rise while $Y=\{0,1\}$63 rises, equal-opportunity can hold while $Y=\{0,1\}$64 remains unequal, and firm settlements can understate consumer losses by large factors (Milli et al., 2018, Barrainkua et al., 4 Sep 2025, Alkarmi et al., 22 Mar 2026). Second, subgroup analysis is usually indispensable, because burden gaps emerge from feature distributions, cost functions, labor imbalances, caregiving context, or social-development heterogeneity (Milli et al., 2018, Barigozzi et al., 16 May 2025, Mahpouya et al., 24 Oct 2025, Tao et al., 15 Oct 2025). Third, burden is frequently dynamic rather than static: it accumulates through repeated news exposure, evolves over epidemic phases, appears after breach-discovery lags, and changes with the structure of ties or the topology of routes (Pal et al., 20 Jan 2026, Noguchi, 2020, Alkarmi et al., 22 Mar 2026, Takano et al., 2016, Nikolova et al., 2014). This suggests that social burden is best understood not as an ancillary concern but as a central object of measurement whenever optimization, policy, or platform design can externalize costs onto people.