@inproceedings{viola-2026-identity-without,
title = "Identity Without Action: Rethinking Collective Action Models in Disinformation Research",
author = "Viola, Lorella",
editor = "Alves, Diego and
Bizzoni, Yuri and
Degaetano-Ortlieb, Stefania and
Kazantseva, Anna and
Pagel, Janis and
Szpakowicz, Stan",
booktitle = "Proceedings of the 10th Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature 2026",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.latechclfl-1.5/",
pages = "36--47",
ISBN = "979-8-89176-373-9",
abstract = "Despite the rapid growth of disinformation research, the fundamental reasons behind user engagement with such content remain poorly understood. Recently, several scholars have suggested that researchers should study engagement with disinformation as a form of collective action (CA). Drawing on Social IdentityTheory (SIT) and the Social Identity Model of Collective Action (SIMCA), this study empirically verifies this assumption by testing it across two distinct linguistic communities, English and Spanish. Specifically, it investigates whether mobilizing CA language functions as a uniform predictor of engagement, or if engagement is primarily driven by community specific identity dynamics. The experiment analysed a bilingual corpus of 4,035 X (formerly Twitter) posts associated with conspiracy theory and disinformation-related hashtags (e.g., {\#}Agenda2030, {\#}TheGreatReset). Using a mixed-methods approach combining BERTopic for narrative discovery, non-parametric statistical testing and Random Forest Regressor, we disentangled the effects of language presence from community behaviour. The results revealthat the Spanish community exhibits a higher baseline engagement compared to the English community indicating that engagement is primarily driven by macro-level community norms (i.e., identity) rather than micro-level linguistic triggers. We argue that rather than treating mobilizing language as a uniform predictor of engagement, future application of SIMCA in disinformation research should account for these identity-based baseline differences."
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<abstract>Despite the rapid growth of disinformation research, the fundamental reasons behind user engagement with such content remain poorly understood. Recently, several scholars have suggested that researchers should study engagement with disinformation as a form of collective action (CA). Drawing on Social IdentityTheory (SIT) and the Social Identity Model of Collective Action (SIMCA), this study empirically verifies this assumption by testing it across two distinct linguistic communities, English and Spanish. Specifically, it investigates whether mobilizing CA language functions as a uniform predictor of engagement, or if engagement is primarily driven by community specific identity dynamics. The experiment analysed a bilingual corpus of 4,035 X (formerly Twitter) posts associated with conspiracy theory and disinformation-related hashtags (e.g., #Agenda2030, #TheGreatReset). Using a mixed-methods approach combining BERTopic for narrative discovery, non-parametric statistical testing and Random Forest Regressor, we disentangled the effects of language presence from community behaviour. The results revealthat the Spanish community exhibits a higher baseline engagement compared to the English community indicating that engagement is primarily driven by macro-level community norms (i.e., identity) rather than micro-level linguistic triggers. We argue that rather than treating mobilizing language as a uniform predictor of engagement, future application of SIMCA in disinformation research should account for these identity-based baseline differences.</abstract>
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%0 Conference Proceedings
%T Identity Without Action: Rethinking Collective Action Models in Disinformation Research
%A Viola, Lorella
%Y Alves, Diego
%Y Bizzoni, Yuri
%Y Degaetano-Ortlieb, Stefania
%Y Kazantseva, Anna
%Y Pagel, Janis
%Y Szpakowicz, Stan
%S Proceedings of the 10th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-373-9
%F viola-2026-identity-without
%X Despite the rapid growth of disinformation research, the fundamental reasons behind user engagement with such content remain poorly understood. Recently, several scholars have suggested that researchers should study engagement with disinformation as a form of collective action (CA). Drawing on Social IdentityTheory (SIT) and the Social Identity Model of Collective Action (SIMCA), this study empirically verifies this assumption by testing it across two distinct linguistic communities, English and Spanish. Specifically, it investigates whether mobilizing CA language functions as a uniform predictor of engagement, or if engagement is primarily driven by community specific identity dynamics. The experiment analysed a bilingual corpus of 4,035 X (formerly Twitter) posts associated with conspiracy theory and disinformation-related hashtags (e.g., #Agenda2030, #TheGreatReset). Using a mixed-methods approach combining BERTopic for narrative discovery, non-parametric statistical testing and Random Forest Regressor, we disentangled the effects of language presence from community behaviour. The results revealthat the Spanish community exhibits a higher baseline engagement compared to the English community indicating that engagement is primarily driven by macro-level community norms (i.e., identity) rather than micro-level linguistic triggers. We argue that rather than treating mobilizing language as a uniform predictor of engagement, future application of SIMCA in disinformation research should account for these identity-based baseline differences.
%U https://aclanthology.org/2026.latechclfl-1.5/
%P 36-47
Markdown (Informal)
[Identity Without Action: Rethinking Collective Action Models in Disinformation Research](https://aclanthology.org/2026.latechclfl-1.5/) (Viola, LaTeCH-CLfL 2026)
ACL