Fake News Spreaders Detection: Sometimes Attention Is Not All You Need
نویسندگان
چکیده
Guided by a corpus linguistics approach, in this article we present comparative evaluation of State-of-the-Art (SotA) models, with special focus on Transformers, to address the task Fake News Spreaders (i.e., users that share News) detection. First, explore reference multilingual dataset for considered task, exploiting techniques, such as chi-square test, keywords and Word Sketch. Second, perform experiments several models Natural Language Processing. Third, using most recent Transformer-based (RoBERTa, DistilBERT, BERT, XLNet, ELECTRA, Longformer) other deep non-deep SotA (CNN, MultiCNN, Bayes, SVM). The CNN tested outperforms all and, best our knowledge, any existing approach same dataset. Fourth, better understand result, conduct post-hoc analysis an attempt investigate behaviour presented performing black-box model. This study highlights importance choosing suitable classifier given specific task. To make educated decision, propose use techniques. Our results suggest large pre-trained like Transformers are not necessarily first choice when addressing text classification one article. All code developed run tests is publicly available GitHub.
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ژورنال
عنوان ژورنال: Information
سال: 2022
ISSN: ['2078-2489']
DOI: https://doi.org/10.3390/info13090426