A hybrid statistical framework for the early detection of fake news

Abstract

This paper proposes a two-tier hybrid framework for the early detection of fake news in fast-moving, time-dependent online environments. At the first level, lexicon-based sentiment analysis is used to monitor the emotional and sentiment structure of news articles, capturing within-text trajectories and identifying patterns of emotional dissonance that distinguish manually fabricated from genuine reports. At the second level, the maximum entropy bootstrap (MEB) is combined with an ensemble of forecasting models—exponential smoothing (ETS), autoregressive integrated moving average (ARIMA), and extreme learning machines (ELM)—to model keyword-based time-series data from Google Trends and to construct one-sided predictive intervals for public-attention dynamics. Anomalies in keyword attention are then identified via bootstrap-based confidence bounds, without relying on Gaussian error assumptions, and interpreted as potential early-warning signals of misinformation-related shocks. The framework is evaluated on a curated corpus of 25 news articles (20 authentic and five manually fabricated), on synthetic stress tests with engineered attention spikes, and on independently documented misinformation episodes involving keywords such as “Obama birth certificate,” “covid 5g,” and “pizzagate.” Empirical results show that the hybrid system reliably separates fake from genuine texts in the controlled corpus and consistently flags crisis-like surges in keyword attention, providing an interpretable, statistically grounded decision-support tool for early-stage misinformation monitoring that integrates semantic analysis with robust time-series modeling.