How Entangled is Factuality and Deception in G erman? (2024)

Abstract: The statement “The earth is flat” is factually inaccurate, but if someone truly believes and argues in its favor, it is not deceptive. Research on deception detection and fact checking often conflates factual accuracy with the truthfulness of statements. This assumption makes it difficult to (a) study subtle distinctions and interactions between the two and (b) gauge their effects on downstream tasks. The belief-based deception framework disentangles these properties by defining texts as deceptive when there is a mismatch between what people say and what they truly believe. In this study, we assess if presumed patterns of deception generalize to German language texts. We test the effectiveness of computational models in detecting deception using an established corpus of belief-based argumentation. Finally, we gauge the impact of deception on the downstream task of fact checking and explore if this property confounds verification models. Surprisingly, our analysis finds no correlation with established cues of deception. Previous work claimed that computational models can outperform humans in deception detection accuracy, however, our experiments show that both traditional and state-of-the-art models struggle with the task, performing no better than random guessing. For fact checking, we find that natural language inference-based verification performs worse on non-factual and deceptive content, while prompting large language models for the same task is less sensitive to these properties.

## introduction in nlp, a vast body of the existing research on deception has focused on detecting the veracity of a statement in a given context or domain @xcite @xcite , often ignoring the difference between factual accuracy and truthfulness, implicitly equating the two. for example, consider the statement "the colosseum in rome is an iconic ancient amphitheater. my family owns a secret underground passage that leads directly to it". the first part of the statement is factually accurate 1 and verifiable, while the second part is likely fabricated, illustrating the importance of disentangling truthfulness from factual accuracy to accurately assess deceptive intent. similarly, in fact verification, mis-and disinformation -differing in terms of the underlying deceptive intent -are typically conflated @xcite . both in deception detection and fact checking, this conventional focus leaves a gap in understanding how deceptive intent operates independently of factual accuracy, which is crucial for developing more sophisticated models that can discern subtle cues of deceptions even when embedded within truthful contexts. in @xcite , we disentangle factuality and deception by introducing a framework in which deception is defined as arguing against one's own beliefs (see figure 1 ). this approach emphasizes the deceptive intent rather than the factual correctness of the statement, thereby providing a better understanding of how deception can occur even when parts of a statement are factually accurate. 1 factual accuracy here refers to the correctness of a statement based on objective evidence that can be verified. it indicates that the statement aligns with the actual state of affairs and can be proven true or false using reliable sources. the belief-based deception corpus (defabel) developed using this framework provides the opportunity to explore the relationship between personal beliefs, factual accuracy, and deceptive intent. the dataset comprises of both deceptive and non-deceptive arguments supporting the same topic statement. moreover, the defabel corpus is the only publicly available dataset on deception in german, facilitating an investigation into the generalizability of linguistic cues across languages. with this paper, we perform the previously lacking experiments to assess the effectiveness of automatic deception models within this corpus. we make the following contributions: (1) we investigate how linguistic cues of deception manifest in german-language texts. surprisingly, we find no statistically significant correlations between deception labels and established cues for deceptive content. (2) we assess the effectiveness of transformerbased and large language models in identifying deceptive arguments. our observations indicate that these models may not be accurately capturing deception cues from language, as their performance is close to random guessing. (3) finally, we explore how evidence-based fact checking is confounded by deception and factuality. our findings suggest that non-factual and deceptive content poses particularly challenging for fact checking models foot_0 . ## how do linguistic cues of deception manifest in german texts? (rq1) this study evaluates the reliability of linguistic cues widely presumed to encode deception, for developing an automated deception detection system in german. as a groundwork to investigate this, we conduct a correlation analysis to understand the relationship between each of the linguistic cues and the deception label. ## how efficient are computational models in detecting deception? (rq2) in section 3, we demonstrate that previously reported deception cues do not significantly correlate with the deception labels in the defabel dataset. however, previous studies have shown that automatic methods have achieved some success in predicting deception. therefore, we evaluate the performance of traditional feature-based methods, finetuned transformer models, and instruction-tuned large language models on the defabel corpus. ## is fact verification confounded by deception and factuality? (rq3) ## conclusion & future work belief-based deception, which disentangles factuality from deceptive intent, presents the possibility to investigate deceptive intent in isolation and gauge its impact on downstream applications, such as automatic fact verification. in our correlation analysis, we found no clear links between linguistic cues of deception and expected patterns in german texts, highlighting the importance of considering cultural and languagespecific differences in deception. future research should investigate whether german texts exhibit unique linguistic patterns in deceptive contexts due to these factors. furthermore, investigating beliefbased data in the english language -currently unavailable to the best of our knowledge -could offer valuable insights into whether our negative findings are influenced by the framework. it is also important to note that the dataset contains labels for instances at the textual level rather than at the sentence level. further investigation is necessary to explore potential variability in the deceptiveness of individual arguments. our automatic deception detection experiments show that computational models are not yet reliable. further studies focusing on explainability using the current dataset could help understand the linguistic patterns the models are relying on for predicting deceptiveness. to enhance the robustness of deception detection tools, future work should prioritize incorporating context, cultural factors, and individual differences. to gauge the impact of deception on fact checking, we explore if this property confounds verification models. a roberta-based model shows lower performance for instances with non-factual claims and deceptive evidence documents, indicating that these instances are more difficult to verify. when using llm prompting, performance is not substantially impacted by these properties. however, non-factual instances are a frequent error class for both models indicating that these properties might be challenging for the models. future work should investigate which additional properties (non-)deceptive evidence or (non-)factual claims may exhibit to understand if the performance may be impacted e.g., by the choice of topic, argument structure or persuasive language.