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20202026
most citedSame Side Stance Classification Task: Facilitating Argument Stance Classification by Fine-tuning a BERT Model

6 citations · 9 across the 7 of their papers we have counts for

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cs.CL2026

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…

cs.CL2026

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…

cs.CL2025

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…

cs.CL2025

Hybrid Annotation for Propaganda Detection: Integrating LLM Pre-Annotations with Human Intelligence

Ariana Sahitaj, Premtim Sahitaj, Veronika Solopova +3

Propaganda detection on social media remains challenging due to task complexity and limited high-quality labeled data. This paper introduces a novel framework that combines human e…

cs.CL20251 cited

Towards Automated Fact-Checking of Real-World Claims: Exploring Task Formulation and Assessment with LLMs

Premtim Sahitaj, Iffat Maab, Junichi Yamagishi +3

Fact-checking is necessary to address the increasing volume of misinformation. Traditional fact-checking relies on manual analysis to verify claims, but it is slow and resource-int…

cs.CL20202 cited

Towards an Argument Mining Pipeline Transforming Texts to Argument Graphs

Mirko Lenz, Premtim Sahitaj, Sean Kallenberg +4

This paper targets the automated extraction of components of argumentative information and their relations from natural language text. Moreover, we address a current lack of system…