Investigating User Radicalization: A Novel Dataset for Identifying Fine-Grained Temporal Shifts in Opinion
This addresses the challenge of lacking suitable datasets for researchers studying user radicalization and polarization on social media, though it is incremental as it focuses on data creation rather than new methods.
The authors tackled the problem of modeling fine-grained opinion shifts on social media by introducing a novel annotated dataset that includes stance polarity and intensity labels per user over time, enabling detection of subtle fluctuations in both short-term and long-term contexts.
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.