EU D isinfo T est: a Benchmark for Evaluating Language Models’ Ability to Detect Disinformation Narratives (2024)

Abstract: As narratives shape public opinion and influence societal actions, distinguishing between truthful and misleading narratives has become a significant challenge. To address this, we introduce the EU DisinfoTest, a novel benchmark designed to evaluate the efficacy of Language Models in identifying disinformation narratives. Developed through a Human-in-the-Loop methodology and grounded in research from EU DisinfoLab, the EU DisinfoTest comprises more than 1,300 narratives. Our benchmark includes persuasive elements under Logos, Pathos, and Ethos rhetorical dimensions. We assessed state-of-the-art LLMs, including the newly released GPT-4o, on their capability to perform zero-shot classification of disinformation narratives versus credible narratives. Our findings reveal that LLMs tend to regard narratives with authoritative appeals as trustworthy, while those with emotional appeals are frequently incorrectly classified as disinformative. These findings highlight the challenges LLMs face in nuanced content interpretation and suggest the need for tailored adjustments in LLM training to better handle diverse narrative structures.

## introduction in the digital age, the internet has revolutionized modern information warfare, creating a global multimedia stage where competing voices battle for attention @xcite . disinformation has become a dominant force within the information disorder construct, influencing the dynamics of the information warfare domain @xcite . political actors, recognizing the power of information, leverage strategic information narratives to articulate their positions on specific issues, aiming to shape perceptions and actions among domestic and international audiences @xcite . however, these narratives can often be deceptive and disinformative, designed to sow division, distrust, and fear. such disinformation narratives find fertile ground in an era where information and disinformation coexist in public discourse @xcite . narratives may be an exceptionally persuasive form of communication, playing a crucial role in shaping human decisions @xcite . given the significant and persuasive role that disinformation narratives play in social and political discourse, the ability to detect and counteract these narratives is of vital importance. the transformer architecture @xcite revolutionized disinformation detection, forming the basis for generative large language models (llms) like gpt-4 and discriminative language models (lms) like bert @xcite . these models offer great promise for automated disinformation detection @xcite . however, a thorough examination of their capabilities and vulnerabilities to persuasive and disinformative narratives has yet to be performed. to address this critical challenge, we present a novel benchmark developed to evaluate lms' ability to detect disinformation narratives, setting a new standard in the fight against disinformation. the creation of the eu disinfotest benchmark incorporates a human-in-the-loop component, including the expertise of fact-checking and debunking experts. the report done by a community of experts from eu disinfolab supported by the friedrich naumann foundation for freedom is the basis of our benchmark @xcite . alongside expert participation, we enriched the development of this benchmark by employing llms and ai tools. disinformation narratives typically do not manifest themselves as straightforward texts, but rather as narratives intertwined with persuasion @xcite . thus, we created a dataset that includes both base narratives and their persuasive forms to assess how different persuasion strategies influence model decisions. to provide a broad perspective on persuasion without delving into overly specific techniques, we chose to focus on the classical rhetorical strategies of logos, ethos, and pathos @xcite . these strategies effectively categorize the most distinct persuasion techniques @xcite @xcite . to this end, the narratives are presented in four versions: base, logos, ethos and pathos. additionally, the dataset contains both disinformation and credible narratives for all four versions, enabling a direct comparison of llm responses to disinformation versus credible narratives and exploring the impact of different persuasion strategies on model effectiveness. our contributions are as follows: ## methodology the methodology section describes our approach to collecting and annotating disinformation narratives in europe. the overview of the process is detailed in figure 1 . the methodology implements a human-in-the-loop approach to data collection by integrating a team of four expert annotators, with advanced ai tools such as gpt-4 and microsoft copilot pro. it is important to point out that two of our experts have experience working for debunking organizations certified by the international fact-checking network (icfn). the primary motivation for using an aioptimized process was to efficiently meet the specific requirements of collecting narratives in four distinct forms: base, logos, ethos, and pathos. manual methods, which are typically less adaptable, would face challenges in efficiently meeting these diverse requirements. in addition, recent studies suggest that llms following instructions, as well as hitl approaches to data collection, perform comparably to humans @xcite . note: although ai tools are utilized in our methodology, all outputs from the ai model are controlled and reviewed by human experts. ## benchmark data statistics. the eu disinfotest dataset, detailed in we have integrated these findings into our prompt template (see figure 2 ), and developed a prompt illustrated in figure 5 . this prompt challenges models to classify narratives as either disinformative or credible, and requires them to articulate their analysis. to enhance reliability, we process each narrative through the model three times, using a majority voting mechanism to determine the final classification. evaluation. the eu disinfotest employs a set of metrics to maintain consistent performance evaluations across narrative types: base, logos, ethos, and pathos. the general formula for the metrics is: agg-@xmath0, specifically the f1-score, tnr (true negative rate), or tpr (true positive rate). the primary metric, agg-f1-score, offers a holistic metric of accuracy. similarly, agg-tnr and agg-tpr serve as auxiliary metrics, providing additional insights into the model's ability to distinguish between credible and disinformation narratives. further details on how the f1-score, tnr, and tpr are calculated are provided in appendix a.1. ## results and discussion baseline. in the appendix a.6, we present the results of human evaluations on base narratives (disinformative and credible), offering insight into how human judgment compares with model assessments. ## conclusions and future work we evaluated various llms in detecting disinformation and credible narratives, using the newly developed eu disinfotest benchamrk. this benchmark, created through a human-in-the-loop approach and informed by eu disinfolab research, includes over 1,300 narratives that incorporate persuasive techniques-logos/ethos/pathos-to evaluate model performance against a base narrative. our findings highlight several key insights into the strengths and weaknesses of these models.