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A majority-based learning system for detecting misinformation
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PRE
AI
DOI：10.1080/0144929X.2024.2326562.png)
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摘要
En 中文
Combating misinformation is both a multifaceted problem and a pressing societal concern. In response, we propose a user-centric system founded on the majority vote model, offering flexibility and synergy in integrating established machine-learning methods or classifiers such as SVM, MLP, LSTM, RF, and XGB. Computational experiments demonstrate promising results in implementing our proposed system to identify text-based fake news, advertorials, and plagiarised information in social media. The dataset employed in these experiments is primarily sourced from volunteer contributors and fact-checking websites. The result evaluation indicators encompass balanced accuracy and F1 score. Overall, this study introduces a significant and autonomous countermeasure to address misinformation.
Keyword:
Misinformation
fake news
detection
machine learning
majority-based
期刊
B
IF:
3.1
论文数:
605
被引数:
6.7K
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