International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Fake news has emerged as a crucial challenge in the contemporary digital landscape, where misinformation proliferates rapidly across social media and online platforms [1][7][10]. The origins of this issue trace back to observations that false information disseminates more swiftly than factual content, leading to widespread societal disruptions, including distorted public perceptions and decision-making processes [2][9][12]. The core problem addressed in this studies is the inadequacy of conventional fake news detect-able methods, which are often labor-intensive and ill-equipped to manage the voluminous daily influx of online content [3][6][13]. Human-led manual verification proves inefficient for processing millions of articles, exacerbating the scalability issues in real-time environments [4][14]. To mitigate this, the propose-able framework settings for learning algorithms integrated with Natural Language Processing (NLP) techniques for automated classification of news articles as authentic or fabricated [5][8][15][18][22]. By extracting and analysing textual features, linguistic patterns, and stylistic elements—such as TF-IDF-based selections and sentiment analysis—the system enables rapid processing of vast datasets [6][9][10][19][23]. This hybrid approach, incorporating ensemble methods and other learning models like BERT, facilitates efficient detection and enhances accuracy across multilingual and cross-platform contexts [8][11][16][17][18][20][25]. Ultimately, the project endeavours to develop an intelligent system that safeguards users from deceptive content, fosters reliable information ecosystems, and upholds the integrity of news consumption in society [21][24].

Keywords: fake news detection, machine learning, NLP, text classification, supervised learning, feature extraction, social media analysis, information verification, automated detection, news authenticity

Downloads: | DOI: 10.17148/IJARCCE.2025.14839