Misinformation Detection in the Wild: News Source Classification as a Proxy for Non-article Texts (2022)

Abstract: Creating classifiers of disinformation is time-consuming, expensive, and requires vast effort from experts spanning different fields. Even when these efforts succeed, their roll-out to publicly available applications stagnates. While these models struggle to find their consumer-accessible use, disinformation behavior online evolves at a pressing speed. The hoaxes get shared in various abbreviations on social networks, often in user-restricted areas, making external monitoring and intervention virtually impossible. To re-purpose existing NLP methods for the new paradigm of sharing misinformation, we propose leveraging information about given texts’ originating news sources to proxy the respective text’s trustworthiness. We first present a methodology for determining the sources’ overall credibility. We demonstrate our pipeline construction in a specific language and introduce CNSC: a novel dataset for Czech articles’ news source and source credibility classification. We constitute initial benchmarks on multiple architectures. Lastly, we create in-the-wild wrapper applications of the trained models: a chatbot, a browser extension, and a standalone web application.

## introduction with the never-ending growth of the internet user base and its impact on our day-to-day lives, a significant portion of our work and leisure nowadays happens online. for many internet users, a substantial part of their time online, if not most, takes place on social media platforms @xcite . herein, most are constantly exposed to the information overload phenomenon. this means that the users are met with an unprecedented mass of posts, articles, images, and comments, which makes orienting within this space strenuous. constantly verifying truthfulness of each presented information becomes virtually incompatible with the quick scrolling through timelines of new posts. at the same time, the assessment of online media's trustworthiness is becoming more critical than ever. we could already see disinformation (i.e., deliberately constructed false information with the intention of someone's manipulation) being employed during critical social events, such as but not limited to elections, refugee crises, and controversial trials. as a result, we observed an immense interest in methods that could automatically assess various aspects of credibility online in the literature throughout recent years. these include stance detection, automated fact-checking, or specific disinformation detection (also referred to as fake news detection). tasks analyzing constituent attributes of disinformation, such as hate speech, stereotypization, or logical fallacy detection, have also been studied. research in this field is not only restrained to text analysis: studies of visual disinformation detection, namely deepfake classification, are also often visited. despite being around for years, the rollout of such methods to real-world applications stagnates @xcite . moreover, as disinformation gets scrutinized by extensive public interest and threatened by improved education about the problem, it evolves rapidly, making itself harder to spot and monitor. many hoax campaigns have moved to accessrestricted parts of the internet, such as closed facebook groups, telegram channels, and e-mail, making external monitoring and tailored debunking campaigns virtually impossible. consequently, designing new tasks for machine learning models to combat these phenomena is challenging, as the relevant input and desired predictions become less definite. in the czech republic, for instance, many hoax websites started sharing augmented versions of their articles on these exact channels. we thus set out to investigate whether existing tools and data resources from the domain of natural language processing could be modified into generic tools, which would help assess the trustworthiness of various texts online. we chose the field of czech media space as our proof-of-concept field. the problem framing seemed ambiguous at first -we wanted to analyze whether a given news text observed on social networks, blogs, or general websites seems trustworthy or not. we know that many of the subjected texts will likely be abbreviations of standard news articles, but the model should be resilient to arbitrary texts, too. as we show in the review of related work, we later realized that many existing fake news classifiers' methodologies use only a single news source per class as their training reference. such models are, hence, trained to classify originating news sources. while this may seem like a design flaw or a result of minimizing annotation complexity, we use it to our advantage. by training classifiers of a given article's originating source, we can utilize the trustworthiness associated with that medium's brand as a proxy for the reliability of the analyzed text. this way, eventual users interacting with the models' predictions will be able to use their existing experience and quickly recall their trust for the respective medium. additionally, we hypothesize that when the users are exposed to familiar labeling (in the form of likely originating news sources), getting accustomed to the application's framework and terminology may become more effortless than learning a completely new assessment system. we believe that internet users could benefit from accessing these models' predictions in-the-wild and thus assess their possible wrapper applications. we develop a chatbot, a browser extension, and a standalone web application. to demonstrate the feasibility of this in-the-wild application of disinformation classifiers, we follow the process of developing such a tool from scratch. all of the artifacts produced in our study are open-source so that entities wishing to create similar projects can reproduce our results for their regional context effortlessly. our contributions can be summarized as follows: ## related work in this section, we review other works which presented datasets for the task of disinformation classification. for a survey of the disinformation classification or automated-fact checking methods as such, we refer the reader to @xcite and @xcite respectively. apart from classification methods that use articles' full text at the input, numerous works have studied utilizing granular manipulative techniques @xcite or associated metadata instead (e.g., authors, hyperlinks) @xcite . most of the disinformation classification datasets in the public domain have emerged after 2017 (d' @xcite . the most prominent and intensively studied ones have become the liar, fever, r/fakeddit, and fakenewsnet datasets. wang (2017) have proposed the liar dataset consisting of shorter excerpts of political speeches and quotes across six trustworthiness classes. the dataset includes over 10, 000 instances in total. similarly, @xcite have introduced the fak-enewsnet, which holds over 20, 000 instances and distinguishes two basal classes (fake or real). these texts are primarily political quotes and speech excerpts, too. next, @xcite have presented the fever dataset, which includes nearly 200, 000 instances of concise texts with respective links to wikipedia. the annotations include whether the statements dispute or not, and thus this dataset has a larger basis in the task of stance detection. lastly, we mention the r/fakeddit dataset by @xcite , which contains reddit posts automatically annotated with a trustworthiness label derived from the overall credibility of the originating subreddit. we also wish to highlight that many recent works focus on languages other than english. resources for disinformation detection have been introduced for arabic @xcite , danish @xcite , french @xcite , and others. for a detailed survey of other datasets with less traction in the literature, we refer the reader to d' @xcite . while the listed datasets are usually referred to as the best training and evaluation resources for disinformation (or fake news) classification, none actually hold news articles' data. in fact, most contain just shorter texts or excerpts. moreover, all of these infer the individual items' class based on the overall source credibility while providing little or no methodology that would support their approach in terms of media sciences. ## cnsc dataset herein we present the czech news source classification dataset (cnsc). in the latter subsections, we review the technical details of the data acquisition, the methodology for news source credibility categorization, and lastly, present statistics of the data. ## baseline experiments in the following section, we present the baseline results for the two newly formulated tasks on the cnsc dataset: in this particular case, the number of classes for nsc corresponds to the number of news sources present in the dataset (9). the number of classes for the sclc task corresponds to the number of credibility labels (3), as outlined in subsection 3.2. ## ethical discussion and limitations in this section, we review the limitations of our solution and discuss the ethical aspects of its use. as already mentioned, one must bear in mind that the overall credibility of a given news source does not deduce all of its articles' trustworthiness or factual correctness. still, different studies @xcite found the source trustworthiness to be an effective indicator of its articles' credibility (especially when other coverage or context are limited). the literature on machine learning identification has mainly built classifiers on this premise. we believe this approach offers a reasonable trade-off between the annotation complexity and overall performance. in our solution, the originating news source serves as a proxy of credibility. while writing in a style of a particular outlet does not, once again, conclusively derive the text's eventual trustworthiness, detecting patterns used in fraudulent and hoax outlets can provide a helpful warning flag for potentially deceptive and harmful texts. any publicly available application of this technology should clearly state this information at the very beginning and provide its users with additional resources about the methodology. moreover, the users should be aware that the analysis is automatic. we include examples of best practices (with short descriptions easily understandable by the general public) in our wrapper applications. the technology could be misused by falsely labeling misinformation as trustworthy and manipulating its users according to the agenda of the service provider. therefore, we believe only trusted, independent institutions (e.g., universityaffiliated centers and non-governmental organizations) should assume the role of operators. we advise prospective providers to disclose the source labeling methodology and the samples used fully. ## conclusion we show that when appropriately adapted and wrapped, the existing methods for disinformation detection can serve as supportive tools for the new form of disinformation contexts online. we present an open-source cnsc dataset with over 22, 000 czech news articles spanning 9 sources across the credibility spectrum, the first of its kind in such a small language. we build on top of a detailed methodology for news trustworthiness assessment in the czech republic and establish 3 credibility classes for the news sources. we train baseline models for the news source and source credibility label classification and achieve f-1 scores of 0.95 and 0.97, respectively. lastly, we introduce three in-the-wild wrapper applications of our models, whose code we are making public. in our future work, we want to conduct focus group studies analyzing the efficacy and user behavior of the intervention tools we introduced. we also intend to propose better metrics and benchmarks for detecting the ever-evolving disinformation.