Investigating User Radicalization: A Novel Dataset for Identifying Fine-Grained Temporal Shifts in Opinion

Flora Sakketou, Allison Lahnala, Liane Vogel, Lucie Flek


Abstract
There is an increasing need for the ability to model fine-grained opinion shifts of social media users, as concerns about the potential polarizing social effects increase. However, the lack of publicly available datasets that are suitable for the task presents a major challenge. In this paper, we introduce an innovative annotated dataset for modeling subtle opinion fluctuations and detecting fine-grained stances. The dataset includes a sufficient amount of stance polarity and intensity labels per user over time and within entire conversational threads, thus making subtle opinion fluctuations detectable both in long term and in short term. All posts are annotated by non-experts and a significant portion of the data is also annotated by experts. We provide a strategy for recruiting suitable non-experts. Our analysis of the inter-annotator agreements shows that the resulting annotations obtained from the majority vote of the non-experts are of comparable quality to the annotations of the experts. We provide analyses of the stance evolution in short term and long term levels, a comparison of language usage between users with vacillating and resolute attitudes, and fine-grained stance detection baselines.
Anthology ID:
2022.lrec-1.405
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
3798–3808
Language:
URL:
https://aclanthology.org/2022.lrec-1.405
DOI:
Bibkey:
Cite (ACL):
Flora Sakketou, Allison Lahnala, Liane Vogel, and Lucie Flek. 2022. Investigating User Radicalization: A Novel Dataset for Identifying Fine-Grained Temporal Shifts in Opinion. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 3798–3808, Marseille, France. European Language Resources Association.
Cite (Informal):
Investigating User Radicalization: A Novel Dataset for Identifying Fine-Grained Temporal Shifts in Opinion (Sakketou et al., LREC 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.lrec-1.405.pdf
Code
 caisa-lab/spinos-dataset