Can deception be detected solely from written text? Cues of deceptive communication are inherently subtle, and even more so in text-only communication. Yet, prior studies have reported considerable success in automatic deception detection. In this study, we hypothesize that such findings are largely driven by artifacts introduced during data collection and do not generalize beyond specific datasets. We revisit this foundational assumption by introducing a belief-based deception framework, which defines deception as a misalignment between an author’s claims and their true beliefs, irrespective of factual accuracy, allowing deception cues to be studied in isolation. Based on this framework, we construct three corpora—collectively referred to as DeFaBel—including a German-language corpus of deceptive and non-deceptive arguments and a multilingual version in German and English, each collected under different conditions to account for potential belief change and enable cross-linguistic analysis. Using these corpora, we evaluate commonly reported linguistic cues of deception. Across all three DeFaBel variants, we find that these cues exhibit negligible and statistically insignificant correlations with deception labels, contrary to prior work that treats such cues as reliable indicators. We further benchmark against other English deception datasets that follow similar data collection protocols. While some of these show statistically significant correlations, the effect sizes remain low and, critically, the set of predictive cues is inconsistent across datasets. We also evaluate deception detection using feature-based models, pre-trained language models, and instruction-tuned large language models. While some of these models perform well on established deception datasets, they consistently perform near chance on DeFaBel. Our findings challenge the core assumption that deception can be reliably inferred from linguistic cues and call for a rethinking how deception is studied and modeled in Natural Language Processing.

Communicating complex scientific findings to non-experts remains a major challenge in fields like psychology, where research is often presented in highly technical language. One effective way to improve accessibility, for non-experts, is through plain language summaries, which summarize key insights into simple and understandable terms. However, the limited number of institutions that produce lay summaries typically relies on psychology experts to create them manually – an approach that ensures high quality but requires significant expertise, time, and effort. In this paper, we introduce the KLARpsy App, a system designed to support psychology experts in creating plain language summaries of psychological meta-analyses using Large Language Models (LLM). Our system generates initial draft summaries based on a 37-criterion guideline developed to ensure clarity for non-experts. All summaries produced through the system are manually validated and edited by KLARpsy authors to ensure factual correctness and readability. We demonstrate how the system integrates LLM-generated content into an expert-in-the-loop workflow. The automatic evaluation showed a mean semantic-similarity score of 0.73 against expert-written summaries, and human evaluation on a 5-point Likert scale averaged above 3 (higher is better), indicate that the generated drafts are of high quality. The application and code are open source.

People differ fundamentally in what motivates them to pursue a goal and how they approach it. For instance, some people seek growth and show eagerness, whereas others prefer security and are vigilant. The concept of regulatory focus is employed in psychology, to explain and predict this goal-directed behavior of humans underpinned by two unique motivational systems – the promotion and the prevention system. Traditionally, text analysis methods using closed-vocabularies are employed to assess the distinctive linguistic patterns associated with the two systems. From an NLP perspective, automatically detecting the regulatory focus of individuals from text provides valuable insights into the behavioral inclinations of the author, finding its applications in areas like marketing or health communication. However, the concept never made an impactful debut in computational linguistics research. To bridge this gap we introduce the novel task of regulatory focus classification from text and present two complementary German datasets – (1) experimentally generated event descriptions and (2) manually annotated short social media texts used for evaluating the generalizability of models on real-world data. First, we conduct a correlation analysis to verify if the linguistic footprints of regulatory focus reported in psychology studies are observable and to what extent in our datasets. For automatic classification, we compare closed-vocabulary-based analyses with a state-of-the-art BERT-based text classification model and observe that the latter outperforms lexicon-based approaches on experimental data and is notably better on out-of-domain Twitter data.

Machine-learned models for author profiling in social media often rely on data acquired via self-reporting-based psychometric tests (questionnaires) filled out by social media users. This is an expensive but accurate data collection strategy. Another, less costly alternative, which leads to potentially more noisy and biased data, is to rely on labels inferred from publicly available information in the profiles of the users, for instance self-reported diagnoses or test results. In this paper, we explore a third strategy, namely to directly use a corpus of items from validated psychometric tests as training data. Items from psychometric tests often consist of sentences from an I-perspective (e.g., ‘I make friends easily.’). Such corpora of test items constitute ‘small data’, but their availability for many concepts is a rich resource. We investigate this approach for personality profiling, and evaluate BERT classifiers fine-tuned on such psychometric test items for the big five personality traits (openness, conscientiousness, extraversion, agreeableness, neuroticism) and analyze various augmentation strategies regarding their potential to address the challenges coming with such a small corpus. Our evaluation on a publicly available Twitter corpus shows a comparable performance to in-domain training for 4/5 personality traits with T5-based data augmentation.

Automatic emotion categorization has been predominantly formulated as text classification in which textual units are assigned to an emotion from a predefined inventory, for instance following the fundamental emotion classes proposed by Paul Ekman (fear, joy, anger, disgust, sadness, surprise) or Robert Plutchik (adding trust, anticipation). This approach ignores existing psychological theories to some degree, which provide explanations regarding the perception of events. For instance, the description that somebody discovers a snake is associated with fear, based on the appraisal as being an unpleasant and non-controllable situation. This emotion reconstruction is even possible without having access to explicit reports of a subjective feeling (for instance expressing this with the words “I am afraid.”). Automatic classification approaches therefore need to learn properties of events as latent variables (for instance that the uncertainty and the mental or physical effort associated with the encounter of a snake leads to fear). With this paper, we propose to make such interpretations of events explicit, following theories of cognitive appraisal of events, and show their potential for emotion classification when being encoded in classification models. Our results show that high quality appraisal dimension assignments in event descriptions lead to an improvement in the classification of discrete emotion categories. We make our corpus of appraisal-annotated emotion-associated event descriptions publicly available.