[PDF] MR-FNC: A Fake News Classification Model to Mitigate Racism | Semantic Scholar (2025)

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This work proposes a topic-agnostic (TAG) classification strategy that uses linguistic and web-markup features to identify fake news pages and reports experimental results which show that this approach attains high accuracy in the identification of fake news, even as topics evolve over time.

This study proposes implementing a web service that integrates a deep learning model for the classification of fake news in Spanish, and the performance of several neural network architectures, including MLP, CNN, and LSTM was evaluated.

This work uses the Google Neural Machine Translator (NMT) to identify and translate Non-English text to English to make the system language agnostic, and assesses whether translated English data gives accuracy comparable to an untranslated English dataset.

This paper proposes a model for recognizing forged news messages from twitter posts, by figuring out how to anticipate precision appraisals, in view of computerizing forged news identification in Twitter datasets, and shows that SVM and Naïve Bayes classifier outperforms the other algorithms.

A new deep learning method to detect fake news based on a combination of different word embedding techniques and a hybrid Convolutional Neural Network and Bidirectional Long Short-Term Memory (BILSTM) model is presented.

An automated software tool which distinguishes between non-Islamophobic, weak Islamophobic and strong Islamophobic content, which enables future quantitative research into the drivers, spread, prevalence and effects of Islamophobic hate speech on social media.

This work contributes to research efforts to detect and understand hateful content on the Web by undertaking a multimodal analysis of Antisemitism and Islamophobia on 4chan's /pol/ using OpenAI’s CLIP, a large pre-trained model that uses the Contrastive Learning paradigm.

There has not been a rise in Islamophobia, but that terror attacks in the UK and abroad caused spikes in anti-Islam comments on the blogs, and a method for analysing blog comments was demonstrated to identify the trend inIslamophobia in the blog communities of a country.

An interactive web-application prototype has been developed and built in order to detect and classify toxic language such as anti-Islamic online text-contents and shows that the supervised ML approach using word level is the finest approach for both datasets that produce high accuracy.

CoronaBias dataset is presented, revealing the existence of anti-Muslim rhetoric around COVID-19 in the Indian sub-continent and the portrayal of religion as a symbol of patriotism played a crucial role in deciding how the Muslim community was perceived during the pandemic.

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