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

  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},

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.

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,



XLNet: Generalized Autoregressive Pretraining for Language Understanding

XLNet is proposed, a generalized autoregressive pretraining method that enables learning bidirectional contexts by maximizing the expected likelihood over all permutations of the factorization order and overcomes the limitations of BERT thanks to its autore progressive formulation.

A robustly optimized bert pretraining approach, arXiv preprint arXiv:1907.11692

  • 1907

Unsupervised Cross-lingual Representation Learning at Scale

It is shown that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks, and the possibility of multilingual modeling without sacrificing per-language performance is shown for the first time.

SMOTE: Synthetic Minority Over-sampling Technique

A combination of the method of oversampling the minority (abnormal) class and under-sampling the majority class can achieve better classifier performance (in ROC space) and a combination of these methods and the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy is evaluated.

Pre-training text encoders as discriminators rather than generators, arXiv preprint arXiv:2003.10555

  • 2003

An empirical study of the naive Bayes classifier

This work analyzes the impact of the distribution entropy on the classificationerror, showing that low-entropy featuredistributions yield good performance of naive Bayes and demonstrates that naive Baye works well for certain nearlyfunctional featuredependencies.

Trends & Controversies: Support Vector Machines

This issue's collection of essays should help familiarize readers with this interesting new racehorse in the Machine Learning stable, and give a practical guide and a new technique for implementing the algorithm efficiently.

Overview of CONSTRAINT 2021 Shared Tasks: Detecting English COVID-19 Fake News and Hindi Hostile Posts

The findings of the shared tasks conducted at the CONSTRAINT Workshop at AAAI 2021 are presented and the most successful models were BERT or its variations.

Fake News Detection System using XLNet model with Topic Distributions: CONSTRAINT@AAAI2021 Shared Task

The team introduced an approach to combine topical distributions from Latent Dirichlet Allocation (LDA) with contextualized representations from XLNet, and compared the method with existing baselines to show that XLNet \(+\) Topic Distributions outperforms other approaches by attaining an F1-score of 0.967.

TUDublin team at Constraint@AAAI2021 - COVID19 Fake News Detection

The main goal of the work was to create a model that would carry out a binary classification of messages from social media as real or fake news in the context of COVID-19.