Attention-Based Deep Learning Models for Detection of Fake News in Social Networks

Ramya, S. P.; Eswari, R.

doi:10.4018/ijcini.295809

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…Actual accuracy and performance measures are missing. [13] The author in this paper used four deep Neural Networks namely CNN, LSTM, BiLSTM and CLSTM. This attention based Deep Neural Network performed a binary classi cation of fake news.…”

Section: Literature Reviewmentioning

confidence: 99%

Uncovering the Truth: A Deep Learning Ensemble Model for Identifying Fake News

Namatullah,

Khurshid,

Jaleel

et al. 2024

Preprint

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The rapid outspread of misinformation and its continuous spreading on digital platforms have raised a serious concern due to its ability to create harmful effects. Over the past ten years, fake news has become increasingly popular in Pakistan. Now it’s a challenging task to identify or differentiate among fake news and real news. Several researchers have made tremendous advancements to detect misleading information in previous years, but due to the nature of the problem, there are still several unresolved problems. The main goal of this research is to create the detection dataset for Pakistani news by semantically extracting news data from various sources and through social media platforms. We have categorized the textual properties of news article. To evaluate our proposed dataset, we used various learning algorithms namely Naive Bayes, Support Vector Machine (SVM), Random Forrest, Logistic Regression, Recurrent neural network (RNN), Long short-term memory (LSTM) and Bidirectional Long short-term memory (Bi-LSTM). By merging them, we built an Ensemble Learning classifier (Meta Model) to achieve higher accuracy. Our findings proved that our proposed Deep Ensemble Learning model outperformed others with an accuracy of 89 percent. The outcomes also demonstrated that an ensemble model outperformed the individual Base model. The findings suggested that an ensemble model can maximize not only accuracy but it can also be used on small datasets. The metrics like accuracy, Precision, F1-score and recall are used to measure an effectiveness of each applied model.

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Section: Literature Reviewmentioning

confidence: 99%

Uncovering the Truth: A Deep Learning Ensemble Model for Identifying Fake News

Namatullah,

Khurshid,

Jaleel

et al. 2024

Preprint

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“…They evaluated five different deep learning models, namely, CNN, LSTM, BiLSTM, CNN + LSTM, and CNN + BiLSTM. Ramya [13] proposed a deep-learning-based fake news detection system using CNN, LSTM, and BiLSTM. They evaluated the models' performance based on accuracy, precision, recall, and F1 score using a complex problem dataset called LIAR, and performance analysis was performed on each of the LIAR dataset's training, validation, and testing sets.…”

Section: Related Workmentioning

confidence: 99%

Automated Detection of Persuasive Content in Electronic News

Darnoto,

Siahaan,

Purwitasari

2023

Informatics

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Persuasive content in online news contains elements that aim to persuade its readers and may not necessarily include factual information. Since a news article only has some sentences that indicate persuasiveness, it would be quite challenging to differentiate news with or without the persuasive content. Recognizing persuasive sentences with a text summarization and classification approach is important to understand persuasive messages effectively. Text summarization identifies arguments and key points, while classification separates persuasive sentences based on the linguistic and semantic features used. Our proposed architecture includes text summarization approaches to shorten sentences without persuasive content and then using classifiers model to detect those with persuasive indication. In this paper, we compare the performance of latent semantic analysis (LSA) and TextRank in text summarization methods, the latter of which has outperformed in all trials, and also two classifiers of convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM). We have prepared a dataset (±1700 data and manually persuasiveness-labeled) consisting of news articles written in the Indonesian language collected from a nationwide electronic news portal. Comparative studies in our experimental results show that the TextRank–BERT–BiLSTM model achieved the highest accuracy of 95% in detecting persuasive news. The text summarization methods were able to generate detailed and precise summaries of the news articles and the deep learning models were able to effectively differentiate between persuasive news and real news.

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A regularization based simple shallow perceptron network for detection of fake news in social networks

Ramya,

Eswari

2024

Multimed Tools Appl

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Attention-Based Deep Learning Models for Detection of Fake News in Social Networks

Cited by 4 publications

References 2 publications

Uncovering the Truth: A Deep Learning Ensemble Model for Identifying Fake News

Uncovering the Truth: A Deep Learning Ensemble Model for Identifying Fake News

Automated Detection of Persuasive Content in Electronic News

A regularization based simple shallow perceptron network for detection of fake news in social networks

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