• Krittika P. Goswami
• Dr. J. W. Bakal

The digital age has seen a surge in fake news, necessitating the development of robust detection and recommendation systems. This paper presents a comprehensive methodology for fake news detection and the recommendation of truthful news using semantic analysis. The system starts with user input, such as a news headline or article snippet, and uses techniques like tokenization, stop word removal, stemming, and lemmatization to normalize the text. Semantic analysis is then conducted using Natural Language Processing (NLP) techniques, sentiment analysis, topic modeling, and semantic similarity algorithms to understand the text's meaning and context. Machine learning models or rule-based algorithms are then used to detect characteristics indicative of fake news, such as sensationalism, misleading information, inconsistencies with reputable sources, and propaganda-like language. The detection process classifies the input text as potentially fake or genuine, and recommends truthful news sources based on textual similarity, source credibility, and historical accuracy. The system also incorporates a feedback loop to continually improve performance. This methodology integrates advanced techniques from natural language processing, machine learning, and information retrieval, providing valuable insights to users and mitigating misinformation spread.

"Detection and Classification of fake news on Social Media Application", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 12, page no.d693-d698, December-2024, Available :http://www.jetir.org/papers/JETIR2412384.pdf

"Detection and Classification of fake news on Social Media Application", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 12, page no. ppd693-d698, December-2024, Available at : http://www.jetir.org/papers/JETIR2412384.pdf