Fake Information Detection Using Deep Learning Methods: A Survey

Dhiman, Pummy; Kaur, Amandeep; Bonkra, Anupam

doi:10.1109/aisc56616.2023.10085519

Cited by 7 publications

(1 citation statement)

References 36 publications

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“…The social media or microblogging platforms play a crucial role in the propagation of information irrespective of its accuracy and verification [78].Detection at an early stage plays crucial role in managing the situation effectively [79]. LSTM networks are a kind of RNN that include long short-term memory cells, allowing the RNN to recall the previous output for a longer time period [80] [81].A drawback of the current research is that the model has only been tested and confirmed using data from only Twitter.…”

Section: Limitations and Future Researchmentioning

confidence: 99%

Emotion Infused Rumour Detection Model using LSTM

Sharma,

Sethi,

Ahuja

2024

IJCDS

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Twitter now 'X', is a highly favored platform for sharing brief messages, known as tweets, read and shared among users at a rapid pace. Hence, the dissemination of information occurs quickly within the community of users in network. Twitter's unregulated environment provides a suitable platform for individuals to share and circulate unverified information; this propagation of rumours can greatly affect society. The detection of rumour accurately on Twitter from tweets is a crucial task. In this study, we suggested an Emotion Infused Rumour Detection model based on an LSTM model that employs tweet text and twenty-one distinct linguistic, user, post, and network features to classify between rumour and non-rumour tweets. comparison of the proposed model i.e. Emotion Infused Detection model using LSTM was done with two different deep learning models to check the achieved outcomes. The findings of the evaluations exhibit the supremacy of the deep learning-based model for identifying rumours. The suggested Emotion Infused Rumour Detection model, which uses an LSTM model, earned an F1-score of 0.91 in identifying rumour and non-rumour tweets, outperforming the state-of-the-art findings. The suggested approach can lessen the influence of rumours on society, prevent loss of life and money, and increase users' confidence in social media platforms. The model proposed has the potential to promptly and accurately recognize tweets containing rumours, aiding in the prevention of the spread of misinformation.

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Section: Limitations and Future Researchmentioning

confidence: 99%

Emotion Infused Rumour Detection Model using LSTM

Sharma,

Sethi,

Ahuja

2024

IJCDS

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Exploring the trend of recognizing apple leaf disease detection through machine learning: a comprehensive analysis using bibliometric techniques

Bonkra,

Pathak,

Kaur

et al. 2024

Artif Intell Rev

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This study’s foremost objectives were to scrutinize how unexpected weather affects agricultural output and to assess how well AI-based machine learning and deep leaning algorithms work for spotting apple leaf diseases. The researchers carried out a bibliometric study to obtain understanding of the current research trends, citation patterns, ownership and partnership arrangements, publishing patterns, and other parameters related to early identification of apple illnesses. Comprehensive interdisciplinary scientific maps are limited because syndrome recognition is not restricted to any solitary arena of research, despite the fact that there have been many studies on the identification of apple diseases. By employing a scientometric technique and 109 publications from the Scopus database published between 2011 and 2022, this study attempted to assess the condition of the research area and combine knowledge frameworks. To find important journals, authors, nations, articles, and topics, the study used the automated processes of VOSviewer and Biblioshiny software. Patterns and trends were discovered using citation counts, social network analysis, and citation and co-citation studies.

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Emerging artificial intelligence applications: metaverse, IoT, cybersecurity, healthcare - an overview

Sharma,

Jindal

2023

Multimed Tools Appl

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Fake Information Detection Using Deep Learning Methods: A Survey

Cited by 7 publications

References 36 publications

Emotion Infused Rumour Detection Model using LSTM

Emotion Infused Rumour Detection Model using LSTM

Exploring the trend of recognizing apple leaf disease detection through machine learning: a comprehensive analysis using bibliometric techniques

Emerging artificial intelligence applications: metaverse, IoT, cybersecurity, healthcare - an overview

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