@inproceedings{evgrafova-etal-2026-lovehate,
title = "{L}ove{H}ate: Stance Detection and Generation for Multiple Topics in User-generated Comments in {R}ussian and {E}nglish",
author = "Evgrafova, Natalia and
Hoste, Veronique and
Lefever, Els",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.636/",
doi = "10.63317/33jtqgqtp59s",
pages = "8014--8025",
abstract = "This paper introduces LoveHate, a new multi-topic corpus of user-generated arguments in Russian, collected from the historical data of the debate platform lovehate.ru. The dataset contains nearly 19,000 posts spanning 16 socially and politically relevant topics, each mapped to binary pro and con stances. We test multiple approaches to stance detection and stance generation across Russian and English data, including translated variants, using both classifier-based (Roberta, RuRoberta) and instruction-tuned generative (Llama, Qwen) models. Results demonstrate that language-specific pretraining yields the strongest performance for stance classification (F1 = 0.892 with RuRoberta), while multilingual generative models {--} when fine-tuned on sufficient data {--} can effectively generate stance in Russian without explicit Russian pretraining. Cross-domain experiments show that English datasets generalize better across corpora, whereas Russian data capture language- and culture-specific argumentation but are less effective for generalizable models. Generating topics remains a more challenging task for both Russian and English data. The dataset and accompanying results contribute to multilingual stance research and provide a valuable new resource for argument mining in Russian."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="evgrafova-etal-2026-lovehate">
<titleInfo>
<title>LoveHate: Stance Detection and Generation for Multiple Topics in User-generated Comments in Russian and English</title>
</titleInfo>
<name type="personal">
<namePart type="given">Natalia</namePart>
<namePart type="family">Evgrafova</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Veronique</namePart>
<namePart type="family">Hoste</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Els</namePart>
<namePart type="family">Lefever</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Fifteenth Language Resources and Evaluation Conference</title>
</titleInfo>
<name type="personal">
<namePart type="given">Stelios</namePart>
<namePart type="family">Piperidis</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Núria</namePart>
<namePart type="family">Bel</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Henk</namePart>
<namePart type="family">van den Heuvel</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nancy</namePart>
<namePart type="family">Ide</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Simon</namePart>
<namePart type="family">Krek</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Antonio</namePart>
<namePart type="family">Toral</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>ELRA Language Resource Association</publisher>
<place>
<placeTerm type="text">Palma de Mallorca, Spain</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This paper introduces LoveHate, a new multi-topic corpus of user-generated arguments in Russian, collected from the historical data of the debate platform lovehate.ru. The dataset contains nearly 19,000 posts spanning 16 socially and politically relevant topics, each mapped to binary pro and con stances. We test multiple approaches to stance detection and stance generation across Russian and English data, including translated variants, using both classifier-based (Roberta, RuRoberta) and instruction-tuned generative (Llama, Qwen) models. Results demonstrate that language-specific pretraining yields the strongest performance for stance classification (F1 = 0.892 with RuRoberta), while multilingual generative models – when fine-tuned on sufficient data – can effectively generate stance in Russian without explicit Russian pretraining. Cross-domain experiments show that English datasets generalize better across corpora, whereas Russian data capture language- and culture-specific argumentation but are less effective for generalizable models. Generating topics remains a more challenging task for both Russian and English data. The dataset and accompanying results contribute to multilingual stance research and provide a valuable new resource for argument mining in Russian.</abstract>
<identifier type="citekey">evgrafova-etal-2026-lovehate</identifier>
<identifier type="doi">10.63317/33jtqgqtp59s</identifier>
<location>
<url>https://aclanthology.org/2026.lrec-1.636/</url>
</location>
<part>
<date>2026-05</date>
<extent unit="page">
<start>8014</start>
<end>8025</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T LoveHate: Stance Detection and Generation for Multiple Topics in User-generated Comments in Russian and English
%A Evgrafova, Natalia
%A Hoste, Veronique
%A Lefever, Els
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F evgrafova-etal-2026-lovehate
%X This paper introduces LoveHate, a new multi-topic corpus of user-generated arguments in Russian, collected from the historical data of the debate platform lovehate.ru. The dataset contains nearly 19,000 posts spanning 16 socially and politically relevant topics, each mapped to binary pro and con stances. We test multiple approaches to stance detection and stance generation across Russian and English data, including translated variants, using both classifier-based (Roberta, RuRoberta) and instruction-tuned generative (Llama, Qwen) models. Results demonstrate that language-specific pretraining yields the strongest performance for stance classification (F1 = 0.892 with RuRoberta), while multilingual generative models – when fine-tuned on sufficient data – can effectively generate stance in Russian without explicit Russian pretraining. Cross-domain experiments show that English datasets generalize better across corpora, whereas Russian data capture language- and culture-specific argumentation but are less effective for generalizable models. Generating topics remains a more challenging task for both Russian and English data. The dataset and accompanying results contribute to multilingual stance research and provide a valuable new resource for argument mining in Russian.
%R 10.63317/33jtqgqtp59s
%U https://aclanthology.org/2026.lrec-1.636/
%U https://doi.org/10.63317/33jtqgqtp59s
%P 8014-8025
Markdown (Informal)
[LoveHate: Stance Detection and Generation for Multiple Topics in User-generated Comments in Russian and English](https://aclanthology.org/2026.lrec-1.636/) (Evgrafova et al., LREC 2026)
ACL