Discovering differential features: Adversarial learning for information credibility evaluation

Abstract

A series of deep learning approaches extract a large number of credibility features to detect fake news on the Internet. However, these extracted features still suffer from many irrelevant and noisy features that restrict severely the performance of the approaches. In this paper, we propose a novel model based on Adversarial Networks and inspirited by the Shared-Private model (ANSP), which aims at reducing common, irrelevant features from the extracted features for information credibility evaluation. Specifically, ANSP involves two tasks: one is to prevent the binary classification of true and false information for capturing common features relying on adversarial networks guided by reinforcement learning. Another extracts credibility features (henceforth, private features) from multiple types of credibility information and compares with the common features through two strategies, i.e., orthogonality constraints and KL-divergence for making the private features more differential. Experiments first on two six-label LIAR and Weibo datasets demonstrate that ANSP achieves the state-of-theart performance, boosting the accuracy by 2.1%, 3.1%, respectively and then on four-label Twit-ter16 validate the robustness of the model with 1.8% performance improvements.

FakedBits- Detecting Fake Information on Social Platforms using Multi-Modal Features

“…The solution utilizes blockchain to identify the veracity of the fake content. Another novel approach using adversarial learning was employed by Wu et al [31]. Here the standard features of the textual feature set were extracted using orthogonality constraints and KL-divergence.…”

Unified Dual-view Cognitive Model for Interpretable Claim Verification

“…This year is really lies on traditional machine learning and deep learning methods to capture semantics , sentiments (Ajao et al, 2019), writing styles (Przybyla, 2020), and stances (Kumar and Carley, 2019) from claim content, and meta-data features, such as user profiles (Shu et al, 2019;Wu et al, 2020b) for verification. Such approaches could improve verification performance, but they are hard to make reasonable explanations for the verified results, i.e., where false claims go wrong; 2) To tackle this issue, many researchers further focus on interpretable claim verification (the second category) by establishing interactive models between claims and each individual relevant article (or comment) to explore coherent (Ma et al, 2019;Wu et al, 2021), similar (Nie et al, 2019;Wu et al, 2020a), or conflicting (Zhou et al, 2020) semantics as evidence for verifying the false parts of claims.…”

Unified Dual-view Cognitive Model for Interpretable Claim Verification

“…(a) Our model with sentence-level attention (b) Our model without sentence-level attention lies on traditional machine learning and deep learning methods to capture semantics , sentiments (Ajao et al, 2019), writing styles (Przybyla, 2020), and stances (Kumar and Carley, 2019) from claim content, and meta-data features, such as user profiles (Shu et al, 2019;Wu et al, 2020b) for verification. Such approaches could improve verification performance, but they are hard to make reasonable explanations for the verified results, i.e., where false claims go wrong; 2) To tackle this issue, many researchers further focus on interpretable claim verification (the second category) by establishing interactive models between claims and each individual relevant article (or comment) to explore coherent (Ma et al, 2019;Wu et al, 2021), similar (Nie et al, 2019;Wu et al, 2020a), or conflicting (Zhou et al, 2020) semantics as evidence for verifying the false parts of claims.…”