METHOD OF ADAPTIVE DETECTING FAKE NEWS BASED ON A GENERALIZED VECTOR OF TEXTUAL FEATURES

Abstract

The rapid spread of "fake news" via social media and online platforms poses a significant threat to informed public discourse and trust in information. While existing detection methods analyze content (text, images) or social context (source, sharer sentiment), they often struggle to adapt to the evolving, sophisticated tactics of misinformation campaigns, losing efficacy as new deceptive forms emerge. This paper presents an innovative, adaptive Natural Language Processing framework designed to tackle this dynamic challenge. Our core strategy involves a feature vector built from generalized textual characteristics, capturing enduring linguistic patterns and structural irregularities indicative of fabricated content, rather than superficial, easily outdated markers. A key aspect is the system’s designed evolvability: it supports continuous expansion of this feature vector and retraining of the classifier with new datasets. This ensures sustained responsiveness and effectiveness against novel fake news iterations in a constantly changing information landscape. The system’s efficacy is validated through a dual evaluation: qualitative visual analytics offer insights into its decision-making, while quantitative statistical metrics (precision, recall, F1-score) confirm its robustness. Experimental results demonstrate a commendable detection accuracy of approximately 90%, underscoring the power of the generalized features and adaptive learning. Ultimately, this research contributes to the critical development of a more reliable, accurate, and dynamically responsive system for identifying and mitigating the spread of fake news. The development of such sophisticated tools holds profound implications for safeguarding the integrity of information, fostering media literacy, and addressing one of the most pressing informational challenges in contemporary society.