A heuristic-driven uncertainty based ensemble framework for fake news detection in tweets and news articles

Das, Sourya Dipta; Basak, Ayan; Dutta, Saikat

doi:10.1016/j.neucom.2021.12.037

Cited by 35 publications

(11 citation statements)

References 17 publications

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“…Similarly, a vast number of users on social media can broadcast fake news based on their opinions. In the case of the tourism field, tweets on social media may propagate fake news based on their imagination without spending at a destination (Das et al 2021). It may lead to the loss of genuine consumers due to fake news on online platforms.…”

Section: Research Designs and General Findingsmentioning

confidence: 99%

A systematic literature review and existing challenges toward fake news detection models

Shah

Ganatra

2022

Soc. Netw. Anal. Min.

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Emerging of social media creates inconsistencies in online news, which causes confusion and uncertainty for consumers while making decisions regarding purchases. On the other hand, in existing studies, there is a lack of empirical and systematic examination observed in terms of inconsistency regarding reviews. The spreading of fake news and disinformation on social media platforms has adverse effects on stability and social harmony. Fake news is often emerging and spreading on social media day by day. It results in influencing or annoying and also misleading nations or societies. Several studies aim to recognize fake news from real news on online social media platforms. Accurate and timely detection of fake news prevents the propagation of fake news. This paper aims to conduct a review on fake news detection models that is contributed by a variety of machine learning and deep learning algorithms. The fundamental and well-performing approaches that existed in the past years are reviewed and categorized and described in different datasets. Further, the dataset utilized, simulation platforms, and recorded performance metrics are evaluated as an extended review model. Finally, the survey expedites the research findings and challenges that could have significant implications for the upcoming researchers and professionals to improve the trust worthiness of automated fake news detection models.

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Section: Research Designs and General Findingsmentioning

confidence: 99%

A systematic literature review and existing challenges toward fake news detection models

Shah

Ganatra

2022

Soc. Netw. Anal. Min.

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“…The study by Benjamin et al was able to obtain a large number of useful statistical features from news texts [13], such as complexity features, psychological features, which are effective in fake news detection. Das et al [14] proposed an integrated framework that uses various attributes in news posts as statistical features, demonstrating the framework's effectiveness in short news detection task to detect fake news. The method proposed by Ma et al [4] is able to learn the continuous representation of microblog posts, which introduces RNN to learn the hidden representation from the textual content of related posts.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Feature Method for Early Fake News Detection via Event Categorizer

Zhao

Deng

Huang

2022

Preprint

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Fake news detection with hybrid features has attracted a great dealof attention due to its great potential in preventing the spread offake information in social media, and it has become an importanttask to be addressed due to many negative effects such as reader misleading, economic recession and political volatility. In this paper, wepropose an event categorizer-based neural network (HECNN) framework for hybrid feature fake news detection, which combines a hybridfeature extractor and an event categorizer to extract transferable features of different events for fake news detection. Moreover, we extracta series of features from text, visual text, visuals, and user profilesfor fake news detection. The experimental results show that the proposed HECNN model improves the accuracy and F1 score comparedto the baseline as well as other advanced deep learning methods.

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“…Existing ensemble fake-news methods [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ] often trained multiple deep or shallow models independently and then combined the outcomes of learners via ensemble mechanisms, such as voting. Thus, these models involve many trainable parameters and a costly training process.…”

Section: Introductionmentioning

confidence: 99%

“…Das et al [ 7 ] used a variety of pretrained network models to extract features from news text content. Each model is followed by an output layer that produces probabilities for real and fake classes.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Fake-News Recognition Using Ensemble of Deep Learners

Obaid

Khotanlou

Mansoorizadeh

et al. 2022

Entropy

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Social networks have drastically changed how people obtain information. News in social networks is accompanied by images and videos and thus receives more attention from readers as opposed to traditional sources. Unfortunately, fake-news publishers often misuse these advantages to spread false information rapidly. Therefore, the early detection of fake news is crucial. The best way to address this issue is to design an automatic detector based on fake-news content. Thus far, many fake-news recognition systems, including both traditional machine learning and deep learning models, have been proposed. Given that manual feature-extraction methods are very time-consuming, deep learning methods are the preferred tools. This study aimed to enhance the performance of existing approaches by utilizing an ensemble of deep learners based on attention mechanisms. To a great extent, the success of an ensemble model depends on the variety of its learners. To this end, we propose a novel loss function that enforces each learner to attend to different parts of news content on the one hand and obtain good classification accuracy on the other hand. Also, the learners are built on a common deep-feature extractor and only differ in their attention modules. As a result, the number of parameters is reduced efficiently and the overfitting problem is addressed. We conducted several experiments on some widely used fake-news detection datasets. The results confirm that the proposed method consistently surpasses the existing peer methods.

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A heuristic-driven uncertainty based ensemble framework for fake news detection in tweets and news articles

Cited by 35 publications

References 17 publications

A systematic literature review and existing challenges toward fake news detection models

A systematic literature review and existing challenges toward fake news detection models

A Hybrid Feature Method for Early Fake News Detection via Event Categorizer

Multimodal Fake-News Recognition Using Ensemble of Deep Learners

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