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@article{195729,
        author = {Ms. Adlin Belshiya and Ajitha Kumari N and Mahumood Sameema S and Manju P S},
        title = {FAKE NEWS DETECTION USING MACHINE LEARNING ALGORITHM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {1819-1826},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195729},
        abstract = {The rapid development of digital communication platforms and social media sites has dramatically changed the pattern of information generation, dissemination, and consumption. This, in turn, has resulted in an increase in fake news dissemination, which is a serious threat to society. Fake news is a serious threat to society and affects various aspects of life, including public opinion, political manipulation, dissemination of misinformation during critical times, and social stability. Thus, there is a need to develop efficient and reliable fake news detection systems, and this is an important research area.
In this paper, an efficient fake news detection system is proposed, which is based on Natural Language Processing and Machine Learning algorithms. This proposed system utilizes a Doc2Vec model, which is a distributed representation model of words and phrases, to convert text data into dense vector representations, which capture semantic and contextual relationships between words in a text document. This vector representation of text data is further utilized to classify fake and real news articles using a Support Vector Machine algorithm. 
In addition to classification, this system proposes a cosine similarity-based consistency analysis between the headline and body of the news article. This feature is useful in identifying misleading headlines, which is a key feature of fake news. Moreover, this system incorporates Explainable Artificial Intelligence (XAI) based on LIME (Local Interpretable Model-Agnostic Explanations), which provides insights into the prediction of the model by identifying the most influential words in classification.
The entire system is implemented in Python, and a web application is developed using Flask, a Python framework, to enable user interaction with the system in real time. From the experimental results, it is clear that this proposed system achieves better accuracy, contextual understanding, and interpretability compared to other traditional methods.
The proposed system is reliable and efficient in identifying fake news, and this is attributed to various features incorporated in this system, such as classification, consistency, and interpretability.},
        keywords = {Fake News Detection, Natural Language Processing, Doc2Vec, Support Vector Machine, Cosine Similarity, Explainable AI, LIME, Machine Learning},
        month = {April},
        }