Misinformation Detection and Analysis of Social Media Platforms Using Machine Learning and Deep Learning

Year : 2026 | Volume : 04 | Issue : 02 | Page : 22 34
By

Arun Chorge,

Prajwal Dahane,

Sonu Gupta,

  1. Research Scholar, Department of MCA, Thakur Institute of Management Studies, Career Development & Research (TIMSCDR), Maharashtra, India
  2. Research Scholar, Department of MCA, Thakur Institute of Management Studies, Career Development & Research (TIMSCDR), Maharashtra, India
  3. Assistant Professor & Deputy Director, Department of MCA, Thakur Institute of Management Studies, Career Development & Research (TIMSCDR), Maharashtra, India

Abstract

Social media platforms have become deeply embedded in everyday life, reshaping how people produce, distribute, and engage with information. Although these platforms facilitate quick communication, they have also developed into important avenues for the spread of false information and fake news. The increasing volume of online content makes manual verification impractical, creating a need for reliable automated detection systems. The effectiveness of deep learning and machine learning models in spotting fraudulent content in online news articles is examined in this study. It compares the performance of a conventional logistic regression model with that of a combined convolutional neural network–long short-term memory (CNN–LSTM) deep learning framework. The models are trained and tested using the Information Security and Object Technology (ISOT) Fake News Dataset for experimentation. Before model development, the textual data is carefully preprocessed through several steps, including cleaning unnecessary content, breaking text into tokens, removing common stop words, and applying lemmatization to normalize word forms. For the baseline model, TF–IDF vectorization is used to represent textual features, while the proposed deep learning model learns contextual and sequential patterns directly from text sequences. Experimental results indicate that both approaches achieve strong classification performance; however, the CNN–LSTM model consistently produces superior results across all evaluation metrics. The suggested model outperforms the logistic regression baseline with an accuracy of 99.30% and an F1-score of 99.29%. The findings demonstrate that combining local feature extraction with long-range contextual learning significantly improves misinformation detection. The proposed approach offers an effective and scalable solution for identifying deceptive content and can support efforts to reduce the spread of misinformation across social media and online news platforms.

Keywords: Misinformation detection, fake news detection, machine learning, deep learning, CNN–LSTM, natural language processing, social media analytics

[This article belongs to International Journal of Algorithms Design and Analysis Review ]

How to cite this article: Arun Chorge, Prajwal Dahane, Sonu Gupta. Misinformation Detection and Analysis of Social Media Platforms Using Machine Learning and Deep Learning. International Journal of Algorithms Design and Analysis Review. 2026; 04(02):22-34.
How to cite this URL: Arun Chorge, Prajwal Dahane, Sonu Gupta. Misinformation Detection and Analysis of Social Media Platforms Using Machine Learning and Deep Learning. International Journal of Algorithms Design and Analysis Review. 2026; 04(02):22-34. Available from: https://journals.stmjournals.com/ijadar/article=2026/view=260473

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Regular Issue Subscription Original Research
Volume 04
Issue 02
Received 10/03/2026
Accepted 13/07/2026
Published 15/08/2026
Publication Time 158 Days

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