Faking Fake News for Real Fake News Detection: Propaganda-Loaded Training Data Generation (2023)

Abstract: Despite recent advances in detecting fake news generated by neural models, their results are not readily applicable to effective detection of human-written disinformation. What limits the successful transfer between them is the sizable gap between machine-generated fake news and human-authored ones, including the notable differences in terms of style and underlying intent. With this in mind, we propose a novel framework for generating training examples that are informed by the known styles and strategies of human-authored propaganda. Specifically, we perform self-critical sequence training guided by natural language inference to ensure the validity of the generated articles, while also incorporating propaganda techniques, such as appeal to authority and loaded language. In particular, we create a new training dataset, PropaNews, with 2,256 examples, which we release for future use. Our experimental results show that fake news detectors trained on PropaNews are better at detecting human-written disinformation by 3.62–7.69% F1 score on two public datasets.

## introduction the dissemination of false information online can cause chaos, hatred, and trust issues, and can eventually hinder the development of society as a whole @xcite . in particular, humanwritten disinformation foot_1 is often used to manipulate certain populations and reportedly already had a catastrophic impact on multiple events, such as brexit (bastos and mercea, 2019), the covid-19 pandemic @xcite , and the 2022 russian assault on ukraine. hence, there is an urgent need for a defense mechanism against human-written disinformation. our goal is to enhance disinformation detection by generating training examples that are better informed by the known styles and strategies of human-authored disinformation. we started by collecting human-written articles from untrustworthy sites 4 , and we analyzed around 40 of them that spread false claims. throughout our analysis, we found two characteristics of this human-written disinformation. first, about 33% of the articles used propaganda techniques to convince the audience that the fake information was actually authentic, and these techniques often involve the use of emotion-triggering language or logical fallacies @xcite to increase the impact on the reader. statistics about the propaganda techniques are given in appendix a. ajdabiyah , libya | thu apr 7 , 2011 6:34 pm edt ajdabiyah , libya -lrb-reuters -rrb--rebels fighting to overthrow muammar gaddafi said five of their fighters were killed ... "in rebel-held eastern libya, wounded rebels being brought to a hospital ajdabiyah said their trucks and tanks were hit on thursday by a nato air strike outside brega. nato said it was investigating an attack by its aircraft on a tank column in the area along the mediterranean coast on thursday , saying the situation was " unclear and fluid . " rebels said at least five of their fighters were killed when nato planes mistakenly bombed a rebel tank column near the contested port. "a number of vehicles were hit by a nato strike ", officers from un concluded. the fighting for brega , the only active front , has dragged on for a week ... second, more than 55% of the articles that we analyzed contained inaccurate information mixed with correct information: in fact, all claims, except for one or two, in these disinformation articles were factual, which makes the few false claims in these articles even more believable. prior work has made significant progress in generating fake news using large pre-trained sequenceto-sequence (seq2seq) models @xcite @xcite . however, the articles generated by these approaches contain an overwhelmingly large proportion of false information and do not explicitly use propaganda. to address these issues, here we propose a novel generation method. given an authentic news article, we replace a salient sentence with a plausible but fake piece of information using a seq2seq model. as the generated texts can often be entailed by the original contexts, we incorporate a self-critical sequence training objective @xcite that incorporates a natural language inference (nli) model into the loss function. additionally, we use the nli model to filter out generated sentences that can be inferred from the replaced ones. then, we add propaganda techniques to mimic how humans craft disinformation. in particular, we automate two commonly used propaganda techniques, appeal to authority and loaded language, @xcite to add propaganda into the faked sentences. subsequently, we use the silver-standard training data generated from these two steps to train a detector. an example is shown in comparing our method to state-of-the-art fake news generation approaches, the evaluation results on two human-written fake news datasets show that detectors are substantially better at spotting human-written disinformation when trained on our generated fake news dataset. our ablation studies confirm the effectiveness of incorporating propaganda into the generated articles for producing better training data. our contributions can be summarized as follows: ## training data generation our process of generating training data for propaganda-loaded disinformation consists of two main steps: disinformation generation ( §2.1) and propaganda generation ( §2.2). below, we describe each of these steps in detail. ## disinformation detection the disinformation detection task challenges detectors to determine whether a given input article contains inaccurate information or not. we experiment on four detectors, including hdsf @xcite , grover @xcite , bert @xcite , and roberta @xcite . hdsf leverages the hierarchical structures of discourse-level features, such as dependency trees, to predict the veracity of a news article. grover is an unidirectional seq2seq model pre-trained on news documents. we use the discriminative version for detection which is adapted from its generative version by feeding the [cls] token representations to a multi-layer perceptron. similarly, bert and roberta take in the entire article as input and feed the representations of the first token to a classification head to determine the veracity of each article. in addition, all models are optimized using cross entropy. for fair comparison, we set the maximum sequence length to 512 and we use the large variants for all models. more details can be found in appendix j. ## experiments in our experiments, we aim (1) to analyze the performance of different models on our new propanews dataset, (2) to examine the effect of various training data sets, and (3) to investigate how much silver-standard data is equivalent to goldstandard data. ## related work fake news generation and detection there has been a focus in prior research on using neural networks to automatically generate fake news as a means of defending against the proliferation of machine-generated fake news. zellers et al. (2019) pre-trained a generator with the gpt-2 architecture @xcite on a large-scale news corpus and demonstrated that it was effective in detecting neural fake news. more recently, @xcite improved the controllability of the generated fake news by conditioning the generator on knowledge elements, such as entities, relations and events, extracted from the original news article. @xcite enhanced the factuality of the generated article by introducing a fact retriever that fetches relevant information from external corpora. @xcite used adversarial reinforcement learning to generate topic-preserving articles. these studies developed methods for generating fake news that is hard to distinguish from real news to humans. nevertheless, due to the overwhelming amount of inaccurate information introduced and the lack of propaganda techniques in the generated texts, these approaches are suboptimal for detecting human-written fake news, as shown in §5.2. in contrast, we generate fake news by incorporating propaganda techniques and preserving the majority of the correct information. hence, our approach is more suitable for studying defense against human-written fake news. also, since our dataset is annotated with the exact offset of the disinformative passages, it enables research on interpretable detection of fake news. ## conclusions and future work we have proposed a novel method for generating disinformation that is closer to human-written fake news. evaluation on two human-written fake news datasets, politifact and snopes, demonstrated the effectiveness of our generated data propanews in enabling better detection performance on human-written fake news. we hope that the dataset presented in this work, propanews, can serve as an enabling resource for detecting human-written fake news and encouraging future research in this direction. in future work, we plan to extend our approach to other languages and to cover more propaganda techniques. we are also interested in studying other aspects of fake news generation, such as novelty and elaboration, as well as engaging linguistic style. to understand the gap between our automatic data generation method and fake news written by humans, we expanded pn-silver to different sizes and compared the performance of roberta-large when trained on these generated datasets and the human-written fake news dataset, snopes. note that since the timeline17 dataset only contains around 4k samples, we additionally crawled new york times news articles as an input to our generator for the "5 times" to "10 times" experiments. the results are shown in figure 3 . although the detector performance at first improves as we add more silver training data, it reaches a plateau after the size is increased five-fold. this illustrates that while our approach is more effective compared to baseline generation methods, there is still a clear gap between our generated articles and human-crafted fake news, likely in aspects such as style (as discussed in §5.2), intent (i.e., limited modeling of propaganda techniques), and falsehood (i.e., the generated content is 100% false). despite the advantages of our generation approach, as compared to previous methods, it is uncapable of generating other propaganda techniques covered in @xcite , such as straw man. thus, our method is not generic enough to handle all types of propaganda techniques within a unified framework. moreover, our approach is limited to generating english-only news articles, and cannot be applied to other languages.