@inproceedings{pai-nemkova-2026-hate,
title = "Hate Speech as a Conflict-Period Marker: Evidence from {A}rabic Telegram in {S}udan",
author = "Pai, Tara and
Nemkova, Poli",
editor = "Atwell, Katherine and
Borah, Angana and
Dementieva, Daryna and
Kreiss, Elisa and
Kotonya, Neema and
Liu, Jiarui and
Olson, Liz and
Wan, Ruyuan and
Zhao, Jieyu",
booktitle = "Proceedings of the Fifth Workshop on {NLP} for Positive Impact ({NLP}4{PI})",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.nlp4pi-1.20/",
pages = "220--231",
ISBN = "979-8-89176-495-8",
abstract = "We investigate whether hate-speech intensity on public Telegram channels serves as a conflict signal, using 1,514 days of Arabic-language posts from Sudan aligned with ACLED conflict-event records. Applying a pretrained multilingual hate-speech classifier, we construct a daily intensity index and analyze its relationship to political violence using within-regime analysis and autocorrelation-robust moving-block bootstrap inference. Hate-speech intensity increases approximately fourfold over the months following war onset and remains elevated, but day-to-day fluctuations do not predict short-term changes in violence. Within the post-war period, the index is positively associated with explosions and remote violence ($r=+0.37$) and negatively associated with protests ($r=-0.25$) and riots ($r=-0.30$), with all three associations surviving multiple-comparison correction. These patterns also hold for continuous classifier-score measures, although the regime-level increase is closely intertwined with growth in posting volume and active-channel participation. We further show that pooling across conflict regimes inflates effect sizes, while naive inference on serially dependent time series overstates statistical significance. Hate-speech intensity should therefore be interpreted as a descriptive marker of the conflict-related Telegram environment rather than as an early-warning predictor of imminent violence."
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<abstract>We investigate whether hate-speech intensity on public Telegram channels serves as a conflict signal, using 1,514 days of Arabic-language posts from Sudan aligned with ACLED conflict-event records. Applying a pretrained multilingual hate-speech classifier, we construct a daily intensity index and analyze its relationship to political violence using within-regime analysis and autocorrelation-robust moving-block bootstrap inference. Hate-speech intensity increases approximately fourfold over the months following war onset and remains elevated, but day-to-day fluctuations do not predict short-term changes in violence. Within the post-war period, the index is positively associated with explosions and remote violence (r=+0.37) and negatively associated with protests (r=-0.25) and riots (r=-0.30), with all three associations surviving multiple-comparison correction. These patterns also hold for continuous classifier-score measures, although the regime-level increase is closely intertwined with growth in posting volume and active-channel participation. We further show that pooling across conflict regimes inflates effect sizes, while naive inference on serially dependent time series overstates statistical significance. Hate-speech intensity should therefore be interpreted as a descriptive marker of the conflict-related Telegram environment rather than as an early-warning predictor of imminent violence.</abstract>
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%0 Conference Proceedings
%T Hate Speech as a Conflict-Period Marker: Evidence from Arabic Telegram in Sudan
%A Pai, Tara
%A Nemkova, Poli
%Y Atwell, Katherine
%Y Borah, Angana
%Y Dementieva, Daryna
%Y Kreiss, Elisa
%Y Kotonya, Neema
%Y Liu, Jiarui
%Y Olson, Liz
%Y Wan, Ruyuan
%Y Zhao, Jieyu
%S Proceedings of the Fifth Workshop on NLP for Positive Impact (NLP4PI)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-495-8
%F pai-nemkova-2026-hate
%X We investigate whether hate-speech intensity on public Telegram channels serves as a conflict signal, using 1,514 days of Arabic-language posts from Sudan aligned with ACLED conflict-event records. Applying a pretrained multilingual hate-speech classifier, we construct a daily intensity index and analyze its relationship to political violence using within-regime analysis and autocorrelation-robust moving-block bootstrap inference. Hate-speech intensity increases approximately fourfold over the months following war onset and remains elevated, but day-to-day fluctuations do not predict short-term changes in violence. Within the post-war period, the index is positively associated with explosions and remote violence (r=+0.37) and negatively associated with protests (r=-0.25) and riots (r=-0.30), with all three associations surviving multiple-comparison correction. These patterns also hold for continuous classifier-score measures, although the regime-level increase is closely intertwined with growth in posting volume and active-channel participation. We further show that pooling across conflict regimes inflates effect sizes, while naive inference on serially dependent time series overstates statistical significance. Hate-speech intensity should therefore be interpreted as a descriptive marker of the conflict-related Telegram environment rather than as an early-warning predictor of imminent violence.
%U https://aclanthology.org/2026.nlp4pi-1.20/
%P 220-231
Markdown (Informal)
[Hate Speech as a Conflict-Period Marker: Evidence from Arabic Telegram in Sudan](https://aclanthology.org/2026.nlp4pi-1.20/) (Pai & Nemkova, NLP4PI 2026)
ACL