MiDAS: Multi-integrated Domain Adaptive Supervision for Fake News Detection

Abstract

Covid-19 related misinformation and fake news, coined an 'infodemic', has dramatically increased over the past few years. This misinformation exhibits concept drift, where the distribution of fake news changes over time, reducing effectiveness of previously trained models for fake news detection. Given a set of fake news models trained on multiple domains, we propose an adaptive decision module to select the best-fit model for a new sample. We propose MIDAS, a multi-domain adaptative approach for fake news detection that ranks relevancy of existing models to new samples. MIDAS contains 2 components: a doman-invariant encoder, and an adaptive model selector. MIDAS integrates multiple pre-trained and fine-tuned models with their training data to create a domain-invariant representation. Then, MIDAS uses local Lipschitz smoothness of the invariant embedding space to estimate each model's relevance to a new sample. Higher ranked models provide predictions, and lower ranked models abstain. We evaluate MIDAS on generalization to drifted data with 9 fake news datasets, each obtained from different domains and modalities. MIDAS achieves new state-of-the-art performance on multi-domain adaptation for out-of-distribution fake news classification.Preprint. Under review.

Big Data ML-Based Fake News Detection Using Distributed Learning

“…The group developed a basic criterion using just hand-coded features and a Gradient Boosting Classifier, both freely accessible on GitHub. Top systems were UCLMR [43], Talos [44], and the Athene system [23]. The CNNs utilised by Talos [44] were one-dimensional, active at the word level, and trained using Google News topic vec-tors for the article's main body and title.…”

“…This study demonstrated that deep learning methods outperformed more conventional machine learning strategies. Bi-LSTM outperforms the competition in detecting bogus news with an F1 score of 96.In[43] authors introduced the Multi-integrated Domain Adaptive Supervision (MIDAS) system to automatically choose the model that best fits a particular collection of data drawn from random distributions. By using local smoothness as a proxy for accuracy and the relevance of training data, MIDAS can increase generalization accuracy across nine distinct fake news datasets.…”

“…By using local smoothness as a proxy for accuracy and the relevance of training data, MIDAS can increase generalization accuracy across nine distinct fake news datasets. MIDAS has a larger than 10% success rate in recognizing bogus news linked to COVID-19, compared to other labelling methods[43]. The results of the literature review were summarized in Table1.…”