Factcheck: Real Time Detection of Misinformation

DOI

10.2991/978-94-6463-858-5_280How to use a DOI?

Keywords

Machine Learning; Deep Learning; Natural Language Processing; Passive Aggressive Classifier; CatBoost; Random Forest; XGBoost; TextCNN; Misinformation; News; Prediction; Tf-idf Vectorization

Abstract

One of the biggest problems today is how fast misinformation spreads, affecting people, groups, and entire societies. With so many of all of the people relying on social media such as WhatsApp, Facebook, and blogs, it’s more important now than ever it was to check for whether the information that we see is actually true. Misinformation can generate meaningful confusion as well as genuine harm, notably in countries like India, where getting reliable news stays important. That’s precisely where this specific study comes directly in; the cool thing concerning our method is that it duly uses Machine Learning, Deep Learning, along with Natural Language Processing to quickly spot false information. This very system isn’t just actually for certain researchers, it’s specifically designed in order to help such media outlets, many policymakers, and even everyday people fight back against all of the fake news. And the key thing is that the model does quite well, showing an awesome 93.1% accuracy. Therefore, it becomes a great tool for the finding of misinformation.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - M. Hema Sree
AU  - A. P. Sunija
PY  - 2025
DA  - 2025/11/04
TI  - Factcheck: Real Time Detection of Misinformation
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 3351
EP  - 3363
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_280
DO  - 10.2991/978-94-6463-858-5_280
ID  - Sree2025
ER  -