@inproceedings{alkaei-etal-2026-climate,
title = "Climate and Migration on {G}erman Political Party {Y}ou{T}ube: A Corpus with Stance, Propaganda, and Divisiveness Annotation",
author = "Alkaei, Zaher and
Stede, Manfred and
Abels, Christoph Maximilian",
editor = "Schimanski, Tobias and
Usmanova, Aida and
Morio, Gaku and
Ni, Jingwei and
Dutia, Kalyan and
Vaghefi, Seyed Saeid and
Stammbach, Dominik and
Leippold, Markus",
booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing Meets Climate Change ({C}limate{NLP} 2026)",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.climatenlp-1.19/",
pages = "282--297",
ISBN = "979-8-89176-465-1",
abstract = "We present a German-language corpus for studying divisiveness in political social-media discourse: 7,277 parent{--}child comment pairs sampled from the YouTube channels of two ideologically opposed German parties, the AfD and Die Linke, on two contested policy domains, climate change and migration. Each pair is annotated on three dimensions: interaction quality (a five-class constructive{--}destructive {\texttimes}agreement{--}disagreement scale), stance (of parent and child toward the topic), and the presence of 14 propaganda techniques. We detail the full construction pipeline{--} channel selection, comment and transcript collection, pseudonymization, conversation-tree reconstruction, topic filtering, and stratified sampling{--} and a reproducible three-task annotation protocol: a 700-pair human gold standard validates an LLM-assisted pipeline that scales annotation to the full corpus. Descriptive analysis reveals pronounced party- and topic-specific patterns{--} most notably that the Migration {\texttimes} AfD stratum is the most hostile (54.5{\%}ofits pairs are Destructive Agreement) while thread-level quality does not differ significantly between the two topics ({\ensuremath{\chi}}2(3) = 5.15, p =0.16). Only on climate are exchanges between opposing sides (a skeptic replying to a supporter) constructive on average; the same clash on migration remains destructive, and on both topics the most destructive exchanges occur between two opponents of the policy at issue."
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<abstract>We present a German-language corpus for studying divisiveness in political social-media discourse: 7,277 parent–child comment pairs sampled from the YouTube channels of two ideologically opposed German parties, the AfD and Die Linke, on two contested policy domains, climate change and migration. Each pair is annotated on three dimensions: interaction quality (a five-class constructive–destructive ×agreement–disagreement scale), stance (of parent and child toward the topic), and the presence of 14 propaganda techniques. We detail the full construction pipeline– channel selection, comment and transcript collection, pseudonymization, conversation-tree reconstruction, topic filtering, and stratified sampling– and a reproducible three-task annotation protocol: a 700-pair human gold standard validates an LLM-assisted pipeline that scales annotation to the full corpus. Descriptive analysis reveals pronounced party- and topic-specific patterns– most notably that the Migration × AfD stratum is the most hostile (54.5%ofits pairs are Destructive Agreement) while thread-level quality does not differ significantly between the two topics (\ensuremathχ2(3) = 5.15, p =0.16). Only on climate are exchanges between opposing sides (a skeptic replying to a supporter) constructive on average; the same clash on migration remains destructive, and on both topics the most destructive exchanges occur between two opponents of the policy at issue.</abstract>
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%0 Conference Proceedings
%T Climate and Migration on German Political Party YouTube: A Corpus with Stance, Propaganda, and Divisiveness Annotation
%A Alkaei, Zaher
%A Stede, Manfred
%A Abels, Christoph Maximilian
%Y Schimanski, Tobias
%Y Usmanova, Aida
%Y Morio, Gaku
%Y Ni, Jingwei
%Y Dutia, Kalyan
%Y Vaghefi, Seyed Saeid
%Y Stammbach, Dominik
%Y Leippold, Markus
%S Proceedings of the 3rd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-465-1
%F alkaei-etal-2026-climate
%X We present a German-language corpus for studying divisiveness in political social-media discourse: 7,277 parent–child comment pairs sampled from the YouTube channels of two ideologically opposed German parties, the AfD and Die Linke, on two contested policy domains, climate change and migration. Each pair is annotated on three dimensions: interaction quality (a five-class constructive–destructive ×agreement–disagreement scale), stance (of parent and child toward the topic), and the presence of 14 propaganda techniques. We detail the full construction pipeline– channel selection, comment and transcript collection, pseudonymization, conversation-tree reconstruction, topic filtering, and stratified sampling– and a reproducible three-task annotation protocol: a 700-pair human gold standard validates an LLM-assisted pipeline that scales annotation to the full corpus. Descriptive analysis reveals pronounced party- and topic-specific patterns– most notably that the Migration × AfD stratum is the most hostile (54.5%ofits pairs are Destructive Agreement) while thread-level quality does not differ significantly between the two topics (\ensuremathχ2(3) = 5.15, p =0.16). Only on climate are exchanges between opposing sides (a skeptic replying to a supporter) constructive on average; the same clash on migration remains destructive, and on both topics the most destructive exchanges occur between two opponents of the policy at issue.
%U https://aclanthology.org/2026.climatenlp-1.19/
%P 282-297
Markdown (Informal)
[Climate and Migration on German Political Party YouTube: A Corpus with Stance, Propaganda, and Divisiveness Annotation](https://aclanthology.org/2026.climatenlp-1.19/) (Alkaei et al., ClimateNLP 2026)
ACL