Emotion-guided Cross-domain Fake News Detection using Adversarial Domain Adaptation (2022)

Abstract: Recent works on fake news detection have shown the efficacy of using emotions as a feature or emotions-based features for improved performance. However, the impact of these emotion-guided features for fake news detection in cross-domain settings, where we face the problem of domain shift, is still largely unexplored. In this work, we evaluate the impact of emotion-guided features for cross-domain fake news detection, and further propose an emotion-guided, domain-adaptive approach using adversarial learning. We prove the efficacy of emotion-guided models in cross-domain settings for various combinations of source and target datasets from FakeNewsAMT, Celeb, Politifact and Gossipcop datasets.

## introduction in recent years, our reliance on social media as a source of information has increased multi-fold, bringing along exponential increase in the spread of fake news. to counter this, researchers have proposed various approaches for fake news detection @xcite . however, models trained on one domain are often brittle and vulnerable to incorrect predictions for the samples of another domain @xcite . this is primarily due to the shift between the two domains, as depicted in figure 1 (1). to handle this, some domain-adaptive frameworks @xcite @xcite have been proposed which help align the source and target domains in the feature space to ameliorate domain shift across different problems. these frameworks guide the feature extractors to extract domain-invariant features by aligning the source and target domains in the feature space, thus generalizing well across domains. however, due to the absence of labels in the target-domain data, the adaptation is often prone to negative transfer, * equal contribution which can disturb the class-wise distribution and affect the discriminability of the final model, as shown in figure 1 (2). some recent studies have observed a correlation between the veracity of a text and its emotions. there exists a prominent affiliation for certain emotions with fake news, and for other emotions with real news @xcite , as illustrated in figure 1 (3). further, some works have successfully utilized emotions as features, or emotion-guided features to aid in fake news detection @xcite @xcite . however, we observe that these works only consider the in-domain setting for evaluation, without analyzing the robustness of these frameworks to domain shift in cross-domain settings. this is another important direction that needs to be explored. domain a domain b fake real emotion 1 emotion 2 1 2 3 emotion 3 emotion 4 4 figure 1: (1) cross-domain texts not aligned. (2) domain adaptation leads to some alignment. (3) emotionguided classification in one domain. (4) emotion-guided domain adaptation leads to improved alignment of the two domains. in this paper, we study the efficacy of emotionaided models in capturing better generalizable features for cross-domain fake news detection. our contributions can be summarized as follows: ## related works several studies over the last few years have explored the correlation of fake news detection with emotions. k et al. ( 2020 ) emotionized text representations using explicit emotion intensity lexicons. @xcite utilized the discrepancies between publisher's emotion and the thread's comments' emotions to detect fake news. however, most of these methods relied upon some additional inputs during evaluation. @xcite proposed an emotion-aided multi-task learning approach, where emotion classification was the auxiliary task implicitly aligning fake news features according to emotion labels. inspired by @xcite , zhang et al. (2020) proposed the first fake news detection work to tackle domain shifts between different datasets. they proposed a multi-modal framework with a gradient reversal layer (grl) to learn domaininvariant features across different domains and used a joint fake news detector on the extracted features. @xcite proposed a robust and generalized fake news detection framework adaptable to a new target domain using adversarial training to make the model robust to outliers and maximum mean difference (mmd)-based loss to align the features of source and target. @xcite extended the problem by treating it as a multi-source domain adaptation task, using the labeled samples from multiple source domains to improve the performance on unlabeled target domains. they also utilized weak labels for weak supervision on target samples to improve performance. however, no previous work has aligned features between different domains using emotion-guided features and domain adaptation using adversarial training. we show that applying both of these approaches leads to improved performance due to better alignment of inter-domain features. ## experimental analysis & results we evaluated our proposed approach on various combinations of source and target datasets. each model was optimized on an in-domain validation set from the source set. mtl(e) and mtl(p) models outperform their stl counterparts in cross-domain settings, as seen in da stl models generally outperform stl models in cross-domain settings across multiple combinations of datasets. however, we see the stl model outperformed the da stl model for celeb dataset as the source dataset and fakenewsamt dataset as target, confirming that unguided adaptation can sometimes lead to negative alignment, reducing the performance of the model. da mtl(e) and da mtl(p) models improve performance in cross-domain settings. ## conclusion in this work, we showed the efficacy of emotionguided models for improved cross-domain fake news detection, and presented an emotion-guided domain-adaptive fake news detection approach, evaluating it against baseline stl, emotion-guided mtl, da stl and emotion-guided da mtl models for various source and target combinations from four datasets. our proposed approach led to improved cross-domain fake news detection accuracy, indicating that emotions are generalizable across domains and aid in better alignment of different domains during domain adaptation.