Classification of Covid-19 misinformation on social media based on neuro-fuzzy and neural network: A systematic review

Neural Computing and Applications, Год журнала: 2022, Номер 35(1), С. 699 - 717

Опубликована: Сен. 20, 2022

Язык: Английский

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Fake News Detection Techniques on Social Media: A Survey

Wireless Communications and Mobile Computing, Год журнала: 2022, Номер 2022, С. 1 - 17

Опубликована: Авг. 22, 2022

Social media platforms like Twitter have become common tools for disseminating and consuming news because of the ease with which users can get access to consume it. This paper focuses on identification false use cutting-edge detection methods in context news, user, social levels. Fake taxonomy was proposed this research. study examines a variety spotting discusses their drawbacks. It also explored how detect recognize such as credibility-based, time-based, context-based, substance itself. Lastly, various datasets used detecting fake an algorithm.

Язык: Английский

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Deep vs. Shallow: A Comparative Study of Machine Learning and Deep Learning Approaches for Fake Health News Detection

IEEE Access, Год журнала: 2023, Номер 11, С. 79330 - 79340

Опубликована: Янв. 1, 2023

The internet explosion and penetration have amplified the Fake news problem that existed even before penetration. This becomes more of a concern, if it is health-related news. To address this issue, research aims to propose Content based (CBM) Feature Based Models (FBM). difference between both models in input provided. CBM only takes content as whereas FBM along with contents also two readability features input. Under each category performance five traditional machine learning techniques: Decision Tree, Random Forest, Support Vector Machine, Adaboost-Decision Tree Adaboost-Random Forest compared hybrid Deep Learning approaches namely CNN-LSTM CNN-BiLSTM. News Healthcare data set comprising 9581 articles utilized for study. As highly imbalanced dataset, Easy Data Augmentation technique used balance dataset. Experimental results demonstrate performed better than Models. Amongst proposed FBM, Hybrid CNN - LSTM model had F1 score 97.09% Score 98.9%. Thus under best performing classification fake

Язык: Английский

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ETMA: Efficient Transformer-Based Multilevel Attention Framework for Multimodal Fake News Detection

IEEE Transactions on Computational Social Systems, Год журнала: 2023, Номер 11(4), С. 5015 - 5027

Опубликована: Март 20, 2023

In this new digital era, social media has created a severe impact on the lives of people. recent times, fake news content become one major challenging problems for society. The dissemination fabricated and false articles includes multimodal data in form text images. previous methods have mainly focused unimodal analysis. Moreover, analysis, researchers fail to keep unique characteristics corresponding each modality. This article aims overcome these limitations by proposing an efficient transformer-based multilevel attention (ETMA) framework detection, which comprises following components: visual attention-based encoder, textual joint learning. Each component utilizes different forms mechanisms uniquely deals with detect fraudulent content. efficacy proposed network is validated conducting several experiments four real-world datasets: Twitter, Jruvika dataset, Pontes Risdal dataset using multiple evaluation metrics. results show that method outperforms baseline all datasets. Furthermore, computation time model also lower than state-of-the-art methods.

Язык: Английский

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A social media analytics platform visualising the spread of COVID-19 in Italy via exploitation of automatically geotagged tweets

Online Social Networks and Media, Год журнала: 2021, Номер 23, С. 100134 - 100134

Опубликована: Апрель 30, 2021

Social media play an important role in the daily life of people around globe and users have emerged as active part news distribution well production. The threatening pandemic COVID-19 has been lead subject online discussions posts, resulting to large amounts related social data, which can be utilised reinforce crisis management several ways. Towards this direction, we propose a novel framework collect, analyse, visualise Twitter tailored specifically monitor virus spread severely affected Italy. We present evaluate deep learning localisation technique that geotags posts based on locations mentioned their text, face detection algorithm estimate number appearing posted images, community approach identify communities users. Moreover, further analysis collected predict reliability detect trending topics events. Finally, demonstrate platform comprises interactive map display filter analysed utilising outcome technique, visual analytics dashboard visualises results topic, community, event methodologies.

Язык: Английский

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