Optimized Fake News Detection in Social Networks Using Boosting Algorithms andMachine Learning Classifiers
- Title
- Optimized Fake News Detection in Social Networks Using Boosting Algorithms andMachine Learning Classifiers
- Creator
- Gupta, Udit; Raj, Swati; Pernabas, Julian Benadit
- Description
- Rising incidence of fake news on social media has turned verifying information into an imperative issue; hence, fact-checking information is becoming an important task. The traditional machine learning-based models like Logistic Regression, Nae Bayes, Support Vector Machines, and Random Forest suffer from the high-dimensional textual data, and the model may not yield optimal results in fake news detection classification. This paper suggests a better detection framework incorporating Gradient Boosting, CatBoost, and AdaBoost, along with Multinomial Nae Bayes for comparative study. This research uses TF-IDF vectorization and advanced text preprocessing, such as stopword removal, tokenization, and feature engineering,are done for better classification accuracy. The research was carried out on public dataset, including the Fake Job Posting dataset of Kaggle, to ensure model flexibility. The findings show remarkable performance enhancement with CatBoost posting the best accuracy of 98.23% and an ROC-AUC score of 0.9739, surpassing traditional models. A statistical significance test (t-test) validates the improvements as significant. Results have shown that ensemble-based approaches perform well in handling imbalanced and high-dimensional text data, and they should be generalizable to real-world fake news detection tasks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
- Source
- Lecture Notes in Networks and Systems;Volume;1612 LNNS;pp.275-287
- Date
- 01-01-2026
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Fake news detection, Boosting algorithms, Machine learning, TF-IDF, Text preprocessing.
- Coverage
- Gupta U., Department of Computer Science and Engineering, Christ University, Bangalore, India; Raj S., Christ University, Bangalore, India; Pernabas J.B., Christ University, Bangalore, India
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISSN: 23673370; ISBN: 978-981952871-4;
- Format
- online
- Language
- English
- Type
- Conference paper
Collection
Citation
Gupta, Udit; Raj, Swati; Pernabas, Julian Benadit, “Optimized Fake News Detection in Social Networks Using Boosting Algorithms andMachine Learning Classifiers,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25429.