A Heuristic-driven Uncertainty based Ensemble Framework for Fake News Detection in Tweets and News Articles

@article{Das2021AHU,
  title={A Heuristic-driven Uncertainty based Ensemble Framework for Fake News Detection in Tweets and News Articles},
  author={Sourya Dipta Das and Ayan Basak and Saikat Dutta},
  journal={Neurocomputing},
  year={2021},
  volume={491},
  pages={607-620}
}
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15 Citations
Background Citations
1
Methods Citations
3

15 Citations

EFND: A Semantic, Visual, and Socially Augmented Deep Framework for Extreme Fake News Detection

The proposed multi-modal EFND integrates contextual, social context, and visual data from news articles and social media to build a multimodal feature vector with a high level of information density.

Meta-heuristic Searched-Ensemble Learning for fake news detection with optimal weighted feature selection approach

Exploring Fake News Detection with Heterogeneous Social Media Context Graphs

This work proposes to construct heterogeneous social context graphs around news articles and reformulate the problem as a graph classification task, indicating that this approach is highly effective with robust results on a common benchmark dataset.

A Novel Technique to Detect the Fake News by Using the Machine Learning Approaches

A fake news detection system that uses machine learning to address the proliferation of fake news, which can only be spotted after figuring out the meaning and most recent information related to it is proposed.

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

A review on fake news detection models that is contributed by a variety of machine learning and deep learning algorithms and the fundamental and well-performing approaches that existed in the past years are reviewed and categorized and described in different datasets.

A Systematic Literature Review and Meta-Analysis of Studies on Online Fake News Detection

The ubiquitous access and exponential growth of information available on social media networks have facilitated the spread of fake news, complicating the task of distinguishing between this and real…

Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique

The study demonstrates that traditional features extracted from news content with proper model design outperform the existing models that were constructed based on text embedding techniques.

Multimodal Fake-News Recognition Using Ensemble of Deep Learners

This study proposes 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 and confirms that the proposed method consistently surpasses the existing peer methods.

A Comparative Study on COVID-19 Fake News Detection Using Different Transformer Based Models

The rapid advancement of social networks and the convenience of internet availability have accelerated the rampant spread of false news and rumors on social media sites. Amid the COVID-19 epidemic,…

Detecting fake news on Chinese social media based on hybrid feature fusion method

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