Evaluating Deep Neural Networks for Automatic Fake News Detection in Political Domain

Fernández-Reyes, Francis C.; Shinde, Suraj

doi:10.1007/978-3-030-03928-8_17

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…Fernandez et al [112] proposed a DNN in the best possible way for fake news detection in the political domain by combining linguistic and metadata features. The authors believed that a multi-class classifier combining RNNs or CNNs for embedding analysis and a fully connected layer for combining metadata features is the best possible technique to achieve higher accuracy.…”

Section: B Unsupervised Learningmentioning

confidence: 99%

A Taxonomy of Fake News Classification Techniques: Survey and Implementation Aspects

et al. 2022

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In the present era, social media platforms such as Facebook, WhatsApp, Twitter, and Telegram are significant sources of information distribution, and people believe it without knowing their origin and genuineness. Social media has fascinated people worldwide in spreading fake news due to its easy availability, cost-effectiveness, and ease of information sharing. Fake news can be generated to mislead the community for personal or commercial gains. It can also be used for other personal benefits such as defaming eminent personalities, amendment of government policies, etc. Thus, to mitigate the awful consequences of fake news, several research types have been conducted for its detection with high accuracy to prevent its fatal outcome. Motivated by the aforementioned concerns, we present a comprehensive survey of the existing fake news identification techniques in this paper. Then, we select Machine Learning (ML) models such as Long-Short Term Memory (LSTM), Passive Aggressive Algorithm, Random Forest (RF), and Naive Bayes (NB) and train them to detect fake news articles on the self-aggregated dataset. Later, we implemented these models by hyper tuning various parameters such as smoothing, drop out factor, and batch size, which has shown promising results in accuracy and other evaluation metrics such as F1-score, recall, precision, and AUC score. The model is trained on 6335 news articles, with LSTM showing the highest accuracy of 92.34% in predicting fake news and NB were showing the highest recall. Based on these results, we propose a hybrid fake news detection technique using NB and LSTM. At last, challenges and open issues along with future research directions are discussed to facilitate the research in this domain further.

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Section: B Unsupervised Learningmentioning

confidence: 99%

A Taxonomy of Fake News Classification Techniques: Survey and Implementation Aspects

et al. 2022

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“…Fernández-Reyes and Shinde [7] have considered the recurrent neural network and convolutional neural network approaches for the detection of fake news. The research experimentation was performed based on the publically available Liar dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

Ant Colony Decision Tree Method to Detect the Suspicious News

Kumari*,

Balkishan

2019

IJRTE

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The ease of availability and low cost of social media platforms has made the human vulnerable to devour the information from social media. Social media serves the unverified information which easily gets disseminated among people through different groups, applications, and other online social platforms. These fake news can lead to any suspicious activities which urges to derive the term suspicious news. The escalation of these suspicious news has open a lot of research ventures to detect and attenuate the impact on human. These suspicious activities have become a nuisance for legitimate users. Despite the presence of existing methods for the suspicious news detection exists, but the continuous growth of such activities is difficult to manage with an individual approach. In this research work, an ensemble approach is considered to detect suspicious news content. Here, Ant Colony Decision Tree method is ensembled for the detection of suspicious news (ACDTDSN). This approach uses the heuristic function and pheromone trail to obtain the optimal solution. The overall functionality of system is based on the content based approach for the detection of suspicious news with steps of dataset consideration, pre-processing, feature selection, and classification. The experimentation is performed using the FakenewNet dataset which consist of BuzzFeed and PolitiFact categories of news content. The results of the proposed ACSTDSN framework are accessed with the performance evaluation measures.

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