1 citations · 1 across the 8 of their papers we have counts for
8 papers
From Repetition to Recognition: Inductive Discovery of Disinformation Narratives
Max Upravitelev, Veronika Solopova, Jing Yang +4
In disinformation datasets, narratives are often understood as recurring interpretive patterns that group texts under narrative labels. Recent work formalized narrative mining as i…
Fine-tuning with Hierarchical Prompting for Robust Propaganda Classification Across Annotation Schemas
Lukas Stähelin, Veronika Solopova, Max Upravitelev +6
Propaganda detection in social media is challenging due to noisy, short texts and low annotation agreements. We introduce a new intent-focused taxonomy of propaganda techniques and…
Retrieving Climate Change Disinformation by Narrative
Max Upravitelev, Veronika Solopova, Charlott Jakob +3
Detecting climate disinformation narratives typically relies on fixed taxonomies, which do not accommodate emerging narratives. Thus, we re-frame narrative detection as a retrieval…
Multiperspectivity as a Resource for Narrative Similarity Prediction
Max Upravitelev, Veronika Solopova, Jing Yang +4
Predicting narrative similarity can be understood as an inherently interpretive task: different, equally valid readings of the same text can produce divergent interpretations and t…
What Are We Measuring in NLG? A Meta-Analysis of Evaluation Trends 2020-2025
Jing Yang, Nils Feldhus, Salar Mohtaj +10
As Natural Language Generation (NLG) dominates modern NLP, scalable evaluation remains a critical bottleneck. Consequently, LLM-as-a-judge (LaaJ) adoption has accelerated rapidly,…
Computational Measurement of Political Positions: A Review of Text-Based Ideal Point Estimation Algorithms
Patrick Parschan, Charlott Jakob
This article presents the first systematic review of unsupervised and semi-supervised computational text-based ideal point estimation (CT-IPE) algorithms, methods designed to infer…
PolBiX: Detecting LLMs' Political Bias in Fact-Checking through X-phemisms
Charlott Jakob, David Harbecke, Patrick Parschan +2
Large Language Models are increasingly used in applications requiring objective assessment, which could be compromised by political bias. Many studies found preferences for left-le…
XplaiNLP at CheckThat! 2025: Multilingual Subjectivity Detection with Finetuned Transformers and Prompt-Based Inference with Large Language Models
Ariana Sahitaj, Jiaao Li, Pia Wenzel Neves +5
This notebook reports the XplaiNLP submission to the CheckThat! 2025 shared task on multilingual subjectivity detection. We evaluate two approaches: (1) supervised fine-tuning of t…