METHOD FOR DETECTING MISINFORMATION IN NEWS ARTICLES

Abstract

ABSTRACT A method (100) for detecting misinformation in news articles. Further, the method comprising collecting a dataset of news articles from multiple online sources. Further, the method (100) comprising the steps of pre-processing the collected articles by removing extraneous characters, converting text to lowercase, and tokenizing the text into meaningful segments. Further, the method (100) comprising the steps of extracting features from the pre-processed text using a term frequency-inverse document frequency (TF-IDF) vectorizer. Further, the method (100) comprising the steps of training a machine learning model using a supervised learning algorithm. Further, the method (100) comprising the steps of evaluating the trained model's performance on a separate testing dataset to classify articles as either genuine or fake news.

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to the field of data processing and analysis, and in particular relates to a method for utilizing word embedding in natural language processing to enhance the extraction of semantic relationships from textual data, specifically in the context of news articles, thereby improving the accuracy and efficiency of information retrieval and analysis.
BACKGROUND
[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] The exponential growth of digital information, particularl , Claims:1. A method (100) for detecting misinformation in news articles, the method comprising the steps of:
collecting a dataset of news articles from multiple online sources;
pre-processing the collected articles by removing extraneous characters, converting text to lowercase, and tokenizing the text into meaningful segments;
extracting features from the pre-processed text using a term frequency-inverse document frequency (TF-IDF) vectorizer;
training a machine learning model using a supervised learning algorithm selected from the group consisting of Naive Bayes, Logistic Regression, K-Nearest Neighbours, Decision Trees, Random Forest, and Support Vector Machines; and
evaluating the trained model's performance on a separate testing dataset to classify articles as either genuine or fake news.

2. The method (100) as claimed in claim 1, wherein the feature extraction further includes utilizing word embedding to capture semantic relationships within the news articles.

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