From Fake to Hyperpartisan News Detection Using Domain Adaptation (2023)

Abstract: Unsupervised Domain Adaptation (UDA) is a popular technique that aims to reduce the domain shift between two data distributions. It was successfully applied in computer vision and natural language processing. In the current work, we explore the effects of various unsupervised domain adaptation techniques between two text classification tasks: fake and hyperpartisan news detection. We investigate the knowledge transfer from fake to hyperpartisan news detection without involving target labels during training. Thus, we evaluate UDA, cluster alignment with a teacher, and cross-domain contrastive learning. Extensive experiments show that these techniques improve performance, while including data augmentation further enhances the results. In addition, we combine clustering and topic modeling algorithms with UDA, resulting in improved performances compared to the initial UDA setup.

## introduction fake news detection is a challenging task in which the goal is to detect whether the news content does not disseminate false information which may harm society. recently, this problem has broad attention to the research community, especially with the rising interaction with social media platforms, which have become one of the primary sources of information for many individuals @xcite . detecting fake news is challenging for many of us, since some news can be written very convincingly, thus spreading misleading information without control @xcite . therefore, new datasets (such as buzzfeed-webis fake news (buzzfeed) @xcite and isot @xcite ) and novel detection techniques @xcite have emerged in recent years. especially since the 2016 united states presidential election, a related task, namely hyperpartisan news detection, identifies whether the information spread by the news is in a political extreme @xcite . hyperpartisan articles aim to expose information related to only one perspective, ignoring and, in some cases, even attacking the perspectives from other opposing sides @xcite . the consequences of this type of news range from misinformation in the media to an increase in the number of supporters of extreme ideologies @xcite . some works @xcite linked fake news with hyperpartisan news, since their goal is to spread as much as possible and influence people. this phenomenon is related to clickbait @xcite , as the authors use different techniques to make the content more accessible and viral on the media @xcite . recently, many architectures based on bidirectional encoder representations from transformers (bert) @xcite have been developed and fine-tuned on various natural language processing (nlp) tasks. the current work aims to evaluate unsupervised deep learning techniques on the fake news detection task and adapt them to the hyperpartisan news detection task. specifically, we employ the robustly optimized bert pretraining approach (roberta) @xcite and evaluate it in three domain adaptation scenarios: unsupervised domain adaptation (uda) @xcite , cluster alignment with a teacher (cat) @xcite , and cross-domain contrastive learning (cdcl) @xcite . in addition, we analyze topic modeling and clustering algorithms to generate domain labels and perform uda to learn about topic-aware features which are specific to fake and hyperpartisan news detection. more precisely, we evaluate various clustering algorithms for generating domain labels, namely k-means @xcite , k-medoids @xcite , gaussian mixture @xcite , and @xcite . additionally, we explore four topic modeling algorithms: latent dirichlet allocation (lda) @xcite , non-negative matrix factorization (nmf) @xcite , latent semantic analysis (lsa) @xcite , and probabilistic lsa (plsa) @xcite . therefore, the main contributions of this work are as follows: ## results there were conducted multiple experiments to evaluate the impact of using various fine-tuned models for roberta. we also investigate the effects of fine-tuning the roberta model on the downstream task. then, we analyze the impact of using a data augmentation technique @xcite based on the tf-idf scores. in appendix a.1, we present the results of the gpt-2 data augmentation. finally, we use clustering and topic modeling algorithms to extract clusters and topics from the training set and perform domain adaptation. we present the results in terms of accuracy @xcite and f1-score (f1). ## conclusions in this work, we addressed the problem of transferring knowledge from fake to hyperpartisan news detection. we employed three types of architectures based on unsupervised training. we conducted multiple experiments, showing the effects of the hyperparameters in the given configuration. all employed methods manage to perform some domain adaptation. in particular, we showed that cdcl obtains the best results after applying data augmentation based on tf-idf word replacement. in contrast, cat managed the poorest results. by analyzing the t-sne visualization, this model did not learn a good feature representation, with a minimal domain gap between the source and target datasets. the low accuracy we hypothesize is due to a lack of data from the source domain, as we have seen that data augmentation helped. for future work, we aim to investigate our approaches on other fake news datasets.