Sakketou, Flora ; Lahnala, Allison ; Vogel, Liane ; Flek, Lucie (2022)
Investigating User Radicalization: A Novel Dataset for Identifying Fine-Grained Temporal Shifts in Opinion.
13th International Conference on Language Resources and Evaluation. Marseille, France (20.-25.06.2022)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Sakketou, Flora ; Lahnala, Allison ; Vogel, Liane ; Flek, Lucie |
| Art des Eintrags: | Bibliographie |
| Titel: | Investigating User Radicalization: A Novel Dataset for Identifying Fine-Grained Temporal Shifts in Opinion |
| Sprache: | Englisch |
| Publikationsjahr: | Juni 2022 |
| Ort: | Paris |
| Verlag: | European Language Resources Association |
| Buchtitel: | Proceedings of the Thirteenth Language Resources and Evaluation Conference |
| Veranstaltungstitel: | 13th International Conference on Language Resources and Evaluation |
| Veranstaltungsort: | Marseille, France |
| Veranstaltungsdatum: | 20.-25.06.2022 |
| URL / URN: | https://aclanthology.org/2022.lrec-1.405 |
| Kurzbeschreibung (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. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Data and AI Systems |
| Hinterlegungsdatum: | 08 Feb 2023 09:17 |
| Letzte Änderung: | 23 Mai 2023 15:42 |
| PPN: | 507921690 |
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