63 citations · 345 across the 53 of their papers we have counts for
60 papers
Categorical Emotions or Appraisals - Which Emotion Model Explains Argument Convincingness Better?
Lynn Greschner, Meike Bauer, Sabine Weber +1
The convincingness of an argument does not only depend on its structure (logos), the person who makes the argument (ethos), but also on the emotion that it causes in the recipient…
Emotionally Charged, Logically Blurred: AI-driven Emotional Framing Impairs Human Fallacy Detection
Yanran Chen, Lynn Greschner, Roman Klinger +2
Logical fallacies are common in public communication and can mislead audiences; fallacious arguments may still appear convincing despite lacking soundness, because convincingness i…
Trust Me, I Can Convince You: The Contextualized Argument Appraisal Framework
Lynn Greschner, Sabine Weber, Roman Klinger
Emotions that somebody develops based on an argument do not only depend on the argument itself - they are also influenced by a subjective evaluation of the argument's potential imp…
Disambiguation of Emotion Annotations by Contextualizing Events in Plausible Narratives
Johannes Schäfer, Roman Klinger
Ambiguity in emotion analysis stems both from potentially missing information and the subjectivity of interpreting a text. The latter did receive substantial attention, but can we…
What if Deception Cannot be Detected? A Cross-Linguistic Study on the Limits of Deception Detection from Text
Aswathy Velutharambath, Kai Sassenberg, Roman Klinger
Can deception be detected solely from written text? Cues of deceptive communication are inherently subtle, even more so in text-only communication. Yet, prior studies have reported…
Self-Adaptive Paraphrasing and Preference Learning for Improved Claim Verifiability
Amelie Wührl, Roman Klinger
In fact-checking, structure and phrasing of claims critically influence a model's ability to predict verdicts accurately. Social media content in particular rarely serves as optima…
Fearful Falcons and Angry Llamas: Emotion Category Annotations of Arguments by Humans and LLMs
Lynn Greschner, Roman Klinger
Arguments evoke emotions, influencing the effect of the argument itself. Not only the emotional intensity but also the category influence the argument's effects, for instance, the…
Which Demographics do LLMs Default to During Annotation?
Johannes Schäfer, Aidan Combs, Christopher Bagdon +9
Demographics and cultural background of annotators influence the labels they assign in text annotation -- for instance, an elderly woman might find it offensive to read a message a…
How Entangled is Factuality and Deception in German?
Aswathy Velutharambath, Amelie Wührl, Roman Klinger
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 chec…
Can Factual Statements be Deceptive? The DeFaBel Corpus of Belief-based Deception
Aswathy Velutharambath, Amelie Wührl, Roman Klinger
If a person firmly believes in a non-factual statement, such as "The Earth is flat", and argues in its favor, there is no inherent intention to deceive. As the argumentation stems…
Understanding Fine-grained Distortions in Reports of Scientific Findings
Amelie Wührl, Dustin Wright, Roman Klinger +1
Distorted science communication harms individuals and society as it can lead to unhealthy behavior change and decrease trust in scientific institutions. Given the rapidly increasin…
What Makes Medical Claims (Un)Verifiable? Analyzing Entity and Relation Properties for Fact Verification
Amelie Wührl, Yarik Menchaca Resendiz, Lara Grimminger +1
Biomedical claim verification fails if no evidence can be discovered. In these cases, the fact-checking verdict remains unknown and the claim is unverifiable. To improve upon this,…
Emotion-Conditioned Text Generation through Automatic Prompt Optimization
Yarik Menchaca Resendiz, Roman Klinger
Conditional natural language generation methods often require either expensive fine-tuning or training a large language model from scratch. Both are unlikely to lead to good result…
Affective Natural Language Generation of Event Descriptions through Fine-grained Appraisal Conditions
Yarik Menchaca Resendiz, Roman Klinger
Models for affective text generation have shown a remarkable progress, but they commonly rely only on basic emotion theories or valance/arousal values as conditions. This is approp…
UNIDECOR: A Unified Deception Corpus for Cross-Corpus Deception Detection
Aswathy Velutharambath, Roman Klinger
Verbal deception has been studied in psychology, forensics, and computational linguistics for a variety of reasons, like understanding behaviour patterns, identifying false testimo…
Automatic Emotion Experiencer Recognition
Maximilian Wegge, Roman Klinger
The most prominent subtask in emotion analysis is emotion classification; to assign a category to a textual unit, for instance a social media post. Many research questions from the…
An Entity-based Claim Extraction Pipeline for Real-world Biomedical Fact-checking
Amelie Wührl, Lara Grimminger, Roman Klinger
Existing fact-checking models for biomedical claims are typically trained on synthetic or well-worded data and hardly transfer to social media content. This mismatch can be mitigat…
Experiencer-Specific Emotion and Appraisal Prediction
Maximilian Wegge, Enrica Troiano, Laura Oberländer +1
Emotion classification in NLP assigns emotions to texts, such as sentences or paragraphs. With texts like "I felt guilty when he cried", focusing on the sentence level disregards t…
Entity-based Claim Representation Improves Fact-Checking of Medical Content in Tweets
Amelie Wührl, Roman Klinger
False medical information on social media poses harm to people's health. While the need for biomedical fact-checking has been recognized in recent years, user-generated medical con…
Natural Language Inference Prompts for Zero-shot Emotion Classification in Text across Corpora
Flor Miriam Plaza-del-Arco, María-Teresa Martín-Valdivia, Roman Klinger
Within textual emotion classification, the set of relevant labels depends on the domain and application scenario and might not be known at the time of model development. This confl…
Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction
Enrica Troiano, Laura Oberländer, Roman Klinger
The most prominent tasks in emotion analysis are to assign emotions to texts and to understand how emotions manifest in language. An observation for NLP is that emotions can be com…
CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets
Isabelle Mohr, Amelie Wührl, Roman Klinger
Over the course of the COVID-19 pandemic, large volumes of biomedical information concerning this new disease have been published on social media. Some of this information can pose…
Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR)
Amelie Wührl, Roman Klinger
Text mining and information extraction for the medical domain has focused on scientific text generated by researchers. However, their direct access to individual patient experience…
x-enVENT: A Corpus of Event Descriptions with Experiencer-specific Emotion and Appraisal Annotations
Enrica Troiano, Laura Oberländer, Maximilian Wegge +1
Emotion classification is often formulated as the task to categorize texts into a predefined set of emotion classes. So far, this task has been the recognition of the emotion of wr…