Fake news detection on Twitter

Sharma, Srishti; Saraswat, Mala; Dubey, Anil Kumar

doi:10.1108/ijwis-02-2022-0044

Cited by 8 publications

(2 citation statements)

References 58 publications

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“…Lee et al [27] selected 21 features from the data stream and converted each feature in the network traffic data to 0 or 1 using a bitmap rule, dividing them into normal traffic or attack according to the range of feature values, thereby reducing the size and dimension of the data. Feature representation methods based on deep learning not only develop rapidly in the field of network attack detection, but also become the main method of feature representation in other research fields [28,29].…”

Section: Feature Extraction and Representation On Trafficmentioning

confidence: 99%

NFHP-RN: A Method of Few-Shot Network Attack Detection Based on the Network Flow Holographic Picture-ResNet

Yi,

Chen,

Yang

et al. 2024

CMES

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Due to the rapid evolution of Advanced Persistent Threats (APTs) attacks, the emergence of new and rare attack samples, and even those never seen before, make it challenging for traditional rule-based detection methods to extract universal rules for effective detection. With the progress in techniques such as transfer learning and meta-learning, few-shot network attack detection has progressed. However, challenges in few-shot network attack detection arise from the inability of time sequence flow features to adapt to the fixed length input requirement of deep learning, difficulties in capturing rich information from original flow in the case of insufficient samples, and the challenge of high-level abstract representation. To address these challenges, a few-shot network attack detection based on NFHP (Network Flow Holographic Picture)-RN (ResNet) is proposed. Specifically, leveraging inherent properties of images such as translation invariance, rotation invariance, scale invariance, and illumination invariance, network attack traffic features and contextual relationships are intuitively represented in NFHP. In addition, an improved RN network model is employed for high-level abstract feature extraction, ensuring that the extracted high-level abstract features maintain the detailed characteristics of the original traffic behavior, regardless of changes in background traffic. Finally, a meta-learning model based on the self-attention mechanism is constructed, achieving the detection of novel APT few-shot network attacks through the empirical generalization of high-level abstract feature representations of known-class network attack behaviors. Experimental results demonstrate that the proposed method can learn high-level abstract features of network attacks across different traffic detail granularities. Compared with state-of-the-art methods, it achieves favorable accuracy, precision, recall, and F1 scores for the identification of unknown-class network attacks through cross-validation on multiple datasets.

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Section: Feature Extraction and Representation On Trafficmentioning

confidence: 99%

NFHP-RN: A Method of Few-Shot Network Attack Detection Based on the Network Flow Holographic Picture-ResNet

Yi,

Chen,

Yang

et al. 2024

CMES

View full text Add to dashboard Cite

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“…Results of their study showed that their ensemble model achieved an accuracy of 77.30%. For their parts, Sharma et al [29] opted for the same grouping method to classify fake tweets using a combination of ML with XGBoost classifier. This method obtained an accuracy of 81% on FakeNewsNet dataset that contains tweets with user characteristics.…”

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confidence: 99%

A novel ensemble model for detecting fake news

Bensouda,

El Fkihi,

Faizi

2024

IJ-AI

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<div>Due the growing proliferation of fake news over the past couple of years, our</div><div>objective in this paper is to propose an ensemble model for the automatic classification of article news as being either real or fake. For this purpose, we opt</div><div>for a blending technique that combines three models, namely bidirectional long</div><div>short-term memory (Bi-LSTM), stochastic gradient descent classifier and ridge</div><div>classifier. The implementation of the proposed model (i.e. BI-LSR) on real</div><div>world datasets, has shown outstanding results. In fact, it achieved an accuracy</div><div>score of 99.16%. Accordingly, this ensemble learning has proven to do perform</div><div>better than individual conventional machine learning and deep learning models</div><div>as well as many ensemble learning approaches cited in the literature.</div>

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Transformer-based models for combating rumours on microblogging platforms: a review

Anggrainingsih,

Hassan,

Datta

2024

Artif Intell Rev

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The remarkable success of Transformer-based embeddings in natural language tasks has sparked interest among researchers in applying them to classify rumours on social media, particularly microblogging platforms. Unlike traditional word embedding methods, Transformers excel at capturing a word’s contextual meaning by considering words from both the left and right of a word, resulting in superior text representations ideal for tasks like rumour detection on microblogging platforms. This survey aims to provide a thorough and well-organized overview and analysis of existing research on implementing Transformer-based models for rumour detection on microblogging platforms. The scope of this study is to offer a comprehensive understanding of this topic by systematically examining and organizing the existing literature. We start by discussing the fundamental reasons and significance of automating rumour detection on microblogging platforms. Emphasizing the critical role of text embedding in converting textual data into numerical representations, we review current approaches to implement Transformer models for rumour detection on microblogging platforms. Furthermore, we present a novel taxonomy that covers a wide array of techniques and approaches employed in the deployment of Transformer-based models for identifying misinformation on microblogging platforms. Additionally, we highlight the challenges associated with this field and propose potential avenues for future research. Drawing insights from the surveyed articles, we anticipate that promising results will continue to emerge as the challenges outlined in this study are addressed. We hope that our efforts will stimulate further interest in harnessing the capabilities of Transformer models to combat the spread of rumours on microblogging platforms.

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Fake news detection on Twitter

Cited by 8 publications

References 58 publications

NFHP-RN: A Method of Few-Shot Network Attack Detection Based on the Network Flow Holographic Picture-ResNet

NFHP-RN: A Method of Few-Shot Network Attack Detection Based on the Network Flow Holographic Picture-ResNet

A novel ensemble model for detecting fake news

Transformer-based models for combating rumours on microblogging platforms: a review

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