Weak Supervision for Fake News Detection via Reinforcement Learning

Abstract

Today social media has become the primary source for news. Via social media platforms, fake news travel at unprecedented speeds, reach global audiences and put users and communities at great risk. Therefore, it is extremely important to detect fake news as early as possible. Recently, deep learning based approaches have shown improved performance in fake news detection. However, the training of such models requires a large amount of labeled data, but manual annotation is time-consuming and expensive. Moreover, due to the dynamic nature of news, annotated samples may become outdated quickly and cannot represent the news articles on newly emerged events. Therefore, how to obtain fresh and high-quality labeled samples is the major challenge in employing deep learning models for fake news detection. In order to tackle this challenge, we propose a reinforced weakly-supervised fake news detection framework, i.e., WeFEND, which can leverage users' reports as weak supervision to enlarge the amount of training data for fake news detection. The proposed framework consists of three main components: the annotator, the reinforced selector and the fake news detector. The annotator can automatically assign weak labels for unlabeled news based on users' reports. The reinforced selector using reinforcement learning techniques chooses high-quality samples from the weakly labeled data and filters out those low-quality ones that may degrade the detector's prediction performance. The fake news detector aims to identify fake news based on the news content. We tested the proposed framework on a large collection of news articles published via WeChat official accounts and associated user reports. Extensive experiments on this dataset show that the proposed WeFEND model achieves the best performance compared with the state-of-the-art methods.

Deep Learning for Fake News Detection in a Pairwise Textual Input Schema

“…Their model achieved up to 97% accuracy with the LSTM. Wang et al [6] developed the WeFEND framework for automatic annotation of news articles, which used user reports from WeChat as a form of weak supervision for fake news detection. They extracted textual and linguistic features from the data and conducted experiments with reinforcement learning, using the Linguistic Inquiry and Word Count (LIWC) and LSTM, reaching an accuracy value of up to 82%.…”

“…In the past decade, the rapid spread of large volumes of online information among an increasing number of social network users is observed. It is a phenomenon that has often been exploited by malicious users and entities, which forge, distribute, and reproduce fake news and propaganda [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Fake news is intentionally forged information, which is distributed either to deceive and make false information believable, or to make verifiable facts doubtful [2,5,[7][8][9][10][11][12]15,[19][20][21].…”

“…Fake news detection is the ability to define the truthfulness of information by analyzing its contents and related features [7,11]. Due to the unstructured and noisy data, the dynamic nature of news, and the increasing number of users, automated solutions for fake news and deception detection in social networks are required [1,2,6,8,10,12,[14][15][16][17][18][19]21,22]. Consequently, fake news and deception detection on social networks present unique challenges and has become an emerging research field, with future directions in data-oriented, feature-oriented, model-oriented, and applicationoriented issues [1][2][3][4][5]8,11,12,15,16,18,22,24,25].…”

Implementation of the BERT-derived architectures to tackle disinformation challenges

“…There are a number of studies that compare older methods with the current SOTA (state-of-the-art) methods, using the older methods as a reference point. In [31], the authors compared 10 methods for detecting fake news in WeChat dataset. The presented results clearly indicate that the methods based on deep neural networks, such as CNN (convolutional neural network), LSTM (long short-term memory), EANN (event adversarial neural networks), detect fake news better than traditional methods such as LR, SVM or RF.…”

Neural Deepfake Detection with Factual Structure of Text

“…Previous works on deepfake detection of text are dominated by neural document classification models (Bakhtin et al, 2019;Zellers et al, 2019;Wang et al, 2019;Vijayaraghavan et al, 2020). They typically tackle the problem with coarse-grained document-level evidence such as dense vectors learned by neural encoder and traditional features (e.g., TF-IDF, word counts).…”