Semi-supervised Content-based Detection of Misinformation via Tensor Embeddings

TL;DR

This paper introduces a semi-supervised, content-based fake news detection method using tensor embeddings and graph propagation, achieving high accuracy with limited labeled data.

Contribution

It proposes a novel tensor-based embedding approach for fake news detection that requires fewer labels than traditional supervised methods.

Findings

Achieves 75.43% accuracy with 30% labels on a public dataset.

Attains 70.92% accuracy with only 2% labels on a large dataset.

Performs on par or better than fully supervised models.

Abstract

Fake news may be intentionally created to promote economic, political and social interests, and can lead to negative impacts on humans beliefs and decisions. Hence, detection of fake news is an emerging problem that has become extremely prevalent during the last few years. Most existing works on this topic focus on manual feature extraction and supervised classification models leveraging a large number of labeled (fake or real) articles. In contrast, we focus on content-based detection of fake news articles, while assuming that we have a small amount of labels, made available by manual fact-checkers or automated sources. We argue this is a more realistic setting in the presence of massive amounts of content, most of which cannot be easily factchecked. To that end, we represent collections of news articles as multi-dimensional tensors, leverage tensor decomposition to derive concise article embeddings that capture spatial/contextual information about each news article, and use those embeddings to create an article-by-article graph on which we propagate limited labels. Results on three real-world datasets show that our method performs on par or better than existing models that are fully supervised, in that we achieve better detection accuracy using fewer labels. In particular, our proposed method achieves 75.43% of accuracy using only 30% of labels of a public dataset while an SVM-based classifier achieved 67.43%. Furthermore, our method achieves 70.92% of accuracy in a large dataset using only 2% of labels.