Introduction

The transmission of emotional states through social groups has long been documented in face-to-face contexts, but the mechanisms underlying affect contagion in online social networks remain poorly understood. Recent advances in network analysis and behavioral methodology now permit precise measurement of emotional responses in controlled digital environments. We extend theories of emotional contagion (Hatfield et al., 1994) to online platforms, examining whether emotional content shared through social media produces comparable affective shifts and behavioral consequences as traditional in-person interactions.

Previous neuroimaging work has identified mirror neuron systems as a substrate for emotional mimicry (Iacoboni et al., 2005), yet behavioral studies of online affect transmission are sparse. We hypothesized that exposure to emotionally valenced content on social platforms would produce systematic shifts in observer mood, measured via self-report and behavioral proxy measures. Moreover, we predicted that the intensity and authenticity of shared emotion would moderate contagion strength.

Method

Participants

We recruited 892 participants across four studies using Amazon Mechanical Turk, with per-study samples ranging from 180 to 250 (M = 223). Participants were English-speaking adults (18–65 years) with an approval rating of ≥95% on the platform. Demographics were representative of online panels (56% female, median age 34, 78% college-educated). Exclusion criteria included previous participation and response time below 2 minutes for the task.

Procedure

Each study followed a between-subjects design exposing participants to social media feed excerpts varying in emotional valence (positive, negative, neutral). Excerpts were authentic Facebook-like content (n = 24 per condition) selected and validated by three raters. Following a 3-minute exposure period, participants completed the Positive and Negative Affect Schedule (PANAS; Watson et al., 1988) and indicated likelihood of sharing the content (1–7 Likert scale). Study 2 (N = 215) incorporated a behavioral measure: participants allocated hypothetical tokens to charitable donations, with allocation patterns serving as a behavioral proxy for positive affect. Study 3 examined temporal decay of contagion effects (assessment at 5, 15, and 30 minutes post-exposure). Study 4 manipulated emotional authenticity by presenting identical content with attributions suggesting genuine vs. curated emotional expression.

Results

Mixed-effects linear regression with participant as random intercept revealed a significant main effect of valence on self-reported positive affect (F(2,856) = 38.7, p < .001). Post hoc contrasts (Bonferroni-corrected) showed that positive content produced higher PA scores (M = 4.2, SD = 1.1) compared to neutral (M = 3.1, SD = 1.0) and negative (M = 2.7, SD = 1.2) conditions. Effect sizes were Cohen's d = 0.93 (positive vs. neutral) and d = 1.12 (positive vs. negative). Negative affect showed the predicted pattern with a medium-to-large effect of valence (F(2,856) = 19.4, p < .001; d = 0.87 for negative vs. neutral). Across studies, sharing intent correlated strongly with measured affect change (r = .61, 95% CI [.55, .67]). In Study 2, participants in the positive condition allocated significantly more tokens to charity (M = 4.1, SD = 2.3) versus neutral (M = 2.8, SD = 2.1), t(208) = 4.52, p < .001, d = 0.64. Study 3 revealed that affective contagion persisted over 30 minutes but showed linear decay. Study 4 demonstrated that content labeled as "authentic emotional expression" produced greater contagion (F(1,214) = 6.18, p = .014) than identical content attributed to curation.

Discussion

This four-study investigation provides converging evidence for emotional contagion in online social networks using contemporary behavioral methodology. The magnitude of observed effects (d = 0.64–1.12) aligns with lab-based contagion studies using face-to-face exposure, suggesting that emotional transmission operates comparably in digital contexts. The divergent strength of positive versus negative contagion replicates evolutionary accounts proposing adaptive sensitivity to positive social information. The behavioral findings in Study 2—reflected in charitable donation patterns—extend beyond affect self-report and suggest meaningful downstream consequences of emotional contagion online.

Limitations include reliance on self-selected MTurk samples and brief exposure windows. Future work should examine individual differences in contagion susceptibility, the role of network homophily, and long-term behavioral consequences of repeated exposure to emotionally valenced content. Theoretical implications suggest that models of affective influence must account for the unique affordances of online platforms, including asynchronous presentation, audience multiplicity, and algorithmic curation.

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