From sharing propensity to diffusion control: temporal catch-up costs of delayed misinformation correction
4 affiliations
- 1School of Mental Health, Wenzhou Medical University, Wenzhou, China
- 2School of Media and Communication, Shanghai Jiao Tong University, Shanghai, China
- 3School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
- 4School of Journalism and New Media, Xi’an Jiaotong University, Xi’an, China
Abstract
IntroductionWhy do corrections underperform misinformation at the network level? We distinguish a correction-sharing disadvantage (lower willingness to transmit corrections) from a correction-timing disadvantage (corrections enter after misinformation gains a first-mover advantage).MethodsStudy 1 compared self-reported forwarding likelihood across misinformation, matched detailed corrections, and corrections presented after instructions to assume prior misinformation forwarding among 266 Chinese participants using three health-message pairs. Study 2 used the ratings as a relative propensity profile, not calibrated platform probabilities, in a competitive agent-based model across Barabási–Albert, Erdős–Rényi, and Watts–Strogatz networks. We varied transmission intensity, onset delay, seed supply, and placement. Temporal catch-up cost (TCC) was the smallest tested correction-to-misinformation seed ratio restoring the immediate equal-seeding cumulative-coverage reduction, minus 1.ResultsUnder the present asymmetrically labeled conditions, direct corrections elicited higher stated forwarding likelihood than misinformation (mean difference = 1.24, 95% CI [0.52, 1.96], Hedges’ g = 0.63), whereas assumed prior forwarding did not significantly reduce correction forwarding (mean difference = 0.35, 95% CI [−0.37, 1.07]). Across three regimes, all 18 equal-seeding delay penalties were positive and increased with delay and transmission intensity. After one generation, all nine regime-topology combinations recovered the benchmark within the grid (TCC = 0.80–7.60); after two generations, outcomes ranged from finite recovery to failure within the grid and, for BA under the reference regime, model-implied unattainability. Reference-regime degree targeting especially reduced BA catch-up requirements.DiscussionThese hypothetical intentions are susceptible to social desirability and the intention–behavior gap; because labeling differed across conditions, they do not isolate correction content from disclosed veracity or corrective framing. Correction difficulty can therefore emerge despite higher stated correction-forwarding likelihood under disclosed labels; the thresholds are conditional model comparisons, not platform-specific resource or clock-time estimates.
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BibTeX
@article{ding2026from,
title = {{From sharing propensity to diffusion control: temporal catch-up costs of delayed misinformation correction}},
author = {Wuyao Ding and Wuyao Ding and Zhangyu Cheng and Yan Ge and Yan Ge},
journal = {Frontiers in Physics},
year = {2026},
month = oct,
doi = {10.3389/fphy.2026.1953841},
url = {https://doi.org/10.3389/fphy.2026.1953841},
note = {CC BY},
}
RIS
TY - JOUR
TI - From sharing propensity to diffusion control: temporal catch-up costs of delayed misinformation correction
AU - Wuyao Ding
AU - Wuyao Ding
AU - Zhangyu Cheng
AU - Yan Ge
AU - Yan Ge
PY - 2026
DA - 2026/10/26
JO - Frontiers in Physics
DO - 10.3389/fphy.2026.1953841
UR - https://doi.org/10.3389/fphy.2026.1953841
AB - IntroductionWhy do corrections underperform misinformation at the network level? We distinguish a correction-sharing disadvantage (lower willingness to transmit corrections) from a correction-timing disadvantage (corrections enter after misinformation gains a first-mover advantage).MethodsStudy 1 compared self-reported forwarding likelihood across misinformation, matched detailed corrections, and corrections presented after instructions to assume prior misinformation forwarding among 266 Chinese participants using three health-message pairs. Study 2 used the ratings as a relative propensity profile, not calibrated platform probabilities, in a competitive agent-based model across Barabási–Albert, Erdős–Rényi, and Watts–Strogatz networks. We varied transmission intensity, onset delay, seed supply, and placement. Temporal catch-up cost (TCC) was the smallest tested correction-to-misinformation seed ratio restoring the immediate equal-seeding cumulative-coverage reduction, minus 1.ResultsUnder the present asymmetrically labeled conditions, direct corrections elicited higher stated forwarding likelihood than misinformation (mean difference = 1.24, 95% CI [0.52, 1.96], Hedges’ g = 0.63), whereas assumed prior forwarding did not significantly reduce correction forwarding (mean difference = 0.35, 95% CI [−0.37, 1.07]). Across three regimes, all 18 equal-seeding delay penalties were positive and increased with delay and transmission intensity. After one generation, all nine regime-topology combinations recovered the benchmark within the grid (TCC = 0.80–7.60); after two generations, outcomes ranged from finite recovery to failure within the grid and, for BA under the reference regime, model-implied unattainability. Reference-regime degree targeting especially reduced BA catch-up requirements.DiscussionThese hypothetical intentions are susceptible to social desirability and the intention–behavior gap; because labeling differed across conditions, they do not isolate correction content from disclosed veracity or corrective framing. Correction difficulty can therefore emerge despite higher stated correction-forwarding likelihood under disclosed labels; the thresholds are conditional model comparisons, not platform-specific resource or clock-time estimates.
SN - 2296-424X
ER -