Leveraging BERT, DistilBERT, and TinyBERT for Rumor Detection

IF 3.4 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Access Pub Date : 2025-04-22 DOI:10.1109/ACCESS.2025.3563301

Aijazahamed Qazi;R. H. Goudar;Rudragoud Patil;Geetabai S. Hukkeri;Dhanashree Kulkarni

引用次数: 0

Abstract

The rapid spread of false information on social media has become a major challenge in today’s digital world. This has created a need for an effective rumor detection system that can identify and control the spread of false information in real-time. The proposed work introduces a rumor detection system by integrating transformer-based models such as BERT, DistilBERT, and TinyBERT with traditional Machine Learning (ML) techniques. The classifiers include Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF) and Naïve Bayes (NB) help in categorizing content as either rumor or non-rumor based on the patterns. The proposed work evaluated BERT, DistilBERT, TinyBERT combined with ML models (SVM, DT, RF, NB) across PHEME dataset using 70:30, 60:40, and 80:20 splits. Overall, BERT + DT and TinyBERT + SVM provided significant results, with BERT + RF and DistilBERT + NB demonstrating better classification capabilities across various events and split ratios on the dataset.

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利用BERT， DistilBERT和TinyBERT进行谣言检测

虚假信息在社交媒体上的迅速传播已成为当今数字世界的一大挑战。这就产生了对有效的谣言检测系统的需求，该系统可以实时识别和控制虚假信息的传播。提出的工作通过将基于变压器的模型（如BERT、DistilBERT和TinyBERT）与传统的机器学习（ML）技术集成在一起，引入了一个谣言检测系统。分类器包括决策树（DT）、支持向量机（SVM）、随机森林（RF）和Naïve贝叶斯（NB），帮助根据模式将内容分类为谣言或非谣言。本文采用70:30、60:40和80:20的分割方法对PHEME数据集上的BERT、DistilBERT、TinyBERT和ML模型（SVM、DT、RF、NB）进行了评估。总体而言，BERT + DT和TinyBERT + SVM提供了显著的结果，BERT + RF和蒸馏BERT + NB在数据集上的各种事件和分割比率上表现出更好的分类能力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC

CiteScore

9.80

自引率

7.70%

发文量

6673

审稿时长

6 weeks

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