ABSTRACT
By the outbreak of Coronavirus disease (COVID-19), started in late 2019, people have been exposed to false information that not only made them confused about the scientific aspects of this virus but also endangered their life. This makes fake news detection a critical issue in social media. In this article, we introduce a convolutional neural network (CNN)-based model for detecting fake news spread in social media. Considering the complexity of the fake news detection task, various features from different aspects of news articles should be captured. To this aim, we propose a multichannel CNN model that uses three distinct embedding channels: (1) contextualized text representation models;(2) static semantic word embeddings;and (3) lexical embeddings, all of which assist the classifier to detect fake news more accurately. Our experimental results on the COVID-19 fake news dataset (Patwa et al. , 2020 , Fighting an infodemic: COVID-19 fake news dataset, arXiv preprint arXiv:2011.03327) shows that our proposed three-channel CNN improved the performance of the single-channel CNN by 0.56 and 1.32% on the validation and test data, respectively. Moreover, we achieved superior performance compared to the state-of-the-art models in the field proposed by Shifath et al. , 2021 , A transformer based approach for fighting COVID-19 fake news, arXiv preprint arXiv:2101.12027 and Wani et al. , 2021 , Evaluating deep learning approaches for COVID-19 fake news detection, arXiv preprint arXiv:2101.04012. [ FROM AUTHOR] Copyright of Digital Scholarship in the Humanities is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
Due to the widespread use of social media, people are exposed to fake news and misinformation. Spreading fake news has adverse effects on both the general public and governments. This issue motivated researchers to utilize advanced natural language processing concepts to detect such misinformation in social media. Despite the recent research studies that only focused on semantic features extracted by deep contextualized text representation models, we aim to show that content-based feature engineering can enhance the semantic models in a complex task like fake news detection. These features can provide valuable information from different aspects of input texts and assist our neural classifier in detecting fake and real news more accurately than using semantic features. To substantiate the effectiveness of feature engineering besides semantic features, we proposed a deep neural architecture in which three parallel convolutional neural network (CNN) layers extract semantic features from contextual representation vectors. Then, semantic and content-based features are fed to a fully connected layer. We evaluated our model on an English dataset about the COVID-19 pandemic and a domain-independent Persian fake news dataset (TAJ). Our experiments on the English COVID-19 dataset show 4.16% and 4.02% improvement in accuracy and f1-score, respectively, compared to the baseline model, which does not benefit from the content-based features. We also achieved 2.01% and 0.69% improvement in accuracy and f1-score, respectively, compared to the state-of-the-art results reported by Shifath et al. (A transformer based approach for fighting covid-19 fake news, arXiv preprint arXiv:2101.12027, 2021). Our model outperformed the baseline on the TAJ dataset by improving accuracy and f1-score metrics by 1.89% and 1.74%, respectively. The model also shows 2.13% and 1.6% improvement in accuracy and f1-score, respectively, compared to the state-of-the-art model proposed by Samadi et al. (ACM Trans Asian Low-Resour Lang Inf Process, https://doi.org/10.1145/3472620, 2021).
ABSTRACT
Due to the widespread use of social media, people are exposed to fake news and misinformation. Spreading fake news has adverse effects on both the general public and governments. This issue motivated researchers to utilize advanced natural language processing concepts to detect such misinformation in social media. Despite the recent research studies that only focused on semantic features extracted by deep contextualized text representation models, we aim to show that content-based feature engineering can enhance the semantic models in a complex task like fake news detection. These features can provide valuable information from different aspects of input texts and assist our neural classifier in detecting fake and real news more accurately than using semantic features. To substantiate the effectiveness of feature engineering besides semantic features, we proposed a deep neural architecture in which three parallel convolutional neural network (CNN) layers extract semantic features from contextual representation vectors. Then, semantic and content-based features are fed to a fully connected layer. We evaluated our model on an English dataset about the COVID-19 pandemic and a domain-independent Persian fake news dataset (TAJ). Our experiments on the English COVID-19 dataset show 4.16% and 4.02% improvement in accuracy and f1-score, respectively, compared to the baseline model, which does not benefit from the content-based features. We also achieved 2.01% and 0.69% improvement in accuracy and f1-score, respectively, compared to the state-of-the-art results reported by Shifath et al. (A transformer based approach for fighting covid-19 fake news, arXiv preprint arXiv:2101.12027, 2021). Our model outperformed the baseline on the TAJ dataset by improving accuracy and f1-score metrics by 1.89% and 1.74%, respectively. The model also shows 2.13% and 1.6% improvement in accuracy and f1-score, respectively, compared to the state-of-the-art model proposed by Samadi et al. (ACM Trans Asian Low-Resour Lang Inf Process, https://doi.org/10.1145/3472620, 2021).
ABSTRACT
In recent years, due to the widespread use of social media and broadcasting agencies around the world, people are extremely exposed to being affected by false information and fake news, all of which have negative impacts on both collective thoughts and governments’ policies. In recent years, the great success of pre-trained models for embedding contextual information from texts motivates researchers to utilize these embeddings in different natural language processing tasks. However, in a complex task like fake news detection, it is not determined which contextualized embedding can assist the classifier with more valuable features. Due to the lack of a comparative study about utilizing different contextualized pre-trained models besides distinct neural classifiers, we aim to dive into a comparative study about using different classifiers and embedding models. In this paper, we propose three classifiers with different pre-trained models for embedding input news articles. We connect Single-Layer Perceptron (SLP), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN) after the embedding layer which consists of novel pre-trained models such as BERT, RoBERTa, GPT2, and Funnel Transformer in order to benefit from deep contextualized representation provided by those models as well as deep neural classifications. We evaluate our proposed models on three well-known fake news datasets: LIAR (Wang, 2017), ISOT (Ahmed et al., 2017), and COVID-19 Patwa et al. (2020). The results on these three datasets show the superiority of our proposed models for fake news detection compared to the state-of-the-art models. The results show 7% and 0.1% improvements in classification accuracy compared to the proposed model by Goldani et al. (2021) on LIAR and ISOT, respectively. We also achieved 1% improvement compared to the proposed model by Shifath et al. (2021) on the COVID-19 dataset.