MATHEMATICAL MODELLING OF INFORMATION DIFFUSION IN DIGITAL JOURNALISM ECOSYSTEMS: INTERPRETIVE ANALYSIS OF REACH, VELOCITY, AND NETWORK DYNAMICS
Keywords:
Algorithmic Amplification, Digital Journalism, Diffusion Velocity, Mathematical Modelling, Network Centrality, Predictive Modelling, Structural InequalityAbstract
This study developed and applied quantitative diffusion models to systematically examine how journalistic content spreads across contemporary digital platforms, with specific emphasis on reach, diffusion velocity, and structural inequality. Anchored on diffusion of innovations theory and network theory, the study employed API-based scraping to collect shares, impressions, and engagement metrics over 48 hours, applied SIR compartmental models to estimate infection rates (β: Twitter ≈0.42; Facebook ≈0.28; Aggregators ≈0.15) and time-to-peak (Twitter 12–18 hrs; Facebook ~21 hrs; Aggregators ~30 hrs), used Lorenz curves and Gini coefficients (mainstream Gini=0.62; independent Gini=0.41) to quantify concentration, and implemented regression-based predictive modelling across 100 articles with reproducible Python and R workflows (R²≈0.78 overall; SIR fit R²≈0.71) and residual diagnostics to assess heteroscedasticity and MAE stratified by outlet type. Ethical anonymization and platform policy compliance were observed. Findings revealed marked platform-specific diffusion velocities, significant concentration of visibility in mainstream outlets (Gini ≈ 0.62 versus 0.41 for independent outlets), and high predictive accuracy of diffusion models (R² up to 0.78), particularly within algorithmically amplified environments. The study concluded that digital journalism diffusion is governed by structured, non-random dynamics shaped by platform architecture, network topology, and algorithmic mediation, and recommended platform-specific editorial timing strategies, visibility-balancing interventions for independents, and adoption of hybrid SIR/Bass/network models integrating content covariates, clustering coefficients, bot-activity controls, and external forcing for robust forecasting. Cross-validation, bootstrapping, significance testing (p<0.05), effect-size estimates, confidence intervals, and sensitivity analyses validated model generalizability across contexts and informed policy-relevant intervention design and scalability.References
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