Computational Models of Argument Structure and Argument Quality for Understanding Misinformation

Abstract

With the continuing spread of misinformation and disinformation online, it is of increasing importance to develop combating mechanisms at scale in the form of automated systems that can find checkworthy information, detect fallacious argumentation of online content, retrieve relevant evidence from authoritative sources and analyze the veracity of claims given the retrieved evidence. The robustness and applicability of these systems depend on the availability of annotated resources to train machine learning models in a supervised fashion, as well as machine learning models that capture patterns beyond domain-specific lexical clues or genre-specific stylistic insights. In this thesis, we investigate the role of models for argument structure and argument quality in improving tasks relevant to fact-checking and furthering our understanding of misinformation and disinformation. We contribute to argumentation mining, misinformation detection, and fact-checking by releasing multiple annotated datasets, developing unified models across datasets and task formulations, and analyzing the vulnerabilities of such models in adversarial settings. We start by studying the argument structure's role in two downstream tasks related to fact-checking. As it is essential to differentiate factual knowledge from opinionated text, we develop a model for detecting the type of news articles (factual or opinionated) using highly transferable argumentation-based features. We also show the potential of argumentation features to predict the checkworthiness of information in news articles and provide the first multi-layer annotated corpus for argumentation and fact-checking. We then study qualitative aspects of arguments through models for fallacy recognition. To understand the reasoning behind checkworthiness and the relation of argumentative fallacies to fake content, we develop an annotation scheme of fallacies in fact-checked content and investigate avenues for automating the detection of such fallacies considering single- and multi-dataset training. Using instruction-based prompting, we introduce a unified model for recognizing twenty-eight fallacies across five fallacy datasets. We also use this model to explain the checkworthiness of statements in two domains. Next, we show our models for end-to-end fact-checking of statements that include finding the relevant evidence document and sentence from a collection of documents and then predicting the veracity of the given statements using the retrieved evidence. We also analyze the robustness of end-to-end fact extraction and verification by generating adversarial statements and addressing areas for improvements for models under adversarial attacks. Finally, we show that evidence-based verification is essential for fine-grained claim verification by modeling the human-provided justifications with the gold veracity labels