Kavli Affiliate: Wei Gao
| First 5 Authors: Prasanta Bhattacharya, Hong Zhang, Yiming Cao, Wei Gao, Brandon Siyuan Loh
| Summary:
Stance detection has emerged as a popular task in natural language processing
research, enabled largely by the abundance of target-specific social media
data. While there has been considerable research on the development of stance
detection models, datasets, and application, we highlight important gaps
pertaining to (i) a lack of theoretical conceptualization of stance, and (ii)
the treatment of stance at an individual- or user-level, as opposed to
message-level. In this paper, we first review the interdisciplinary origins of
stance as an individual-level construct to highlight relevant attributes (e.g.,
psychological features) that might be useful to incorporate in stance detection
models. Further, we argue that recent pre-trained and large language models
(LLMs) might offer a way to flexibly infer such user-level attributes and/or
incorporate them in modelling stance. To better illustrate this, we briefly
review and synthesize the emerging corpus of studies on using LLMs for
inferring stance, and specifically on incorporating user attributes in such
tasks. We conclude by proposing a four-point agenda for pursuing stance
detection research that is theoretically informed, inclusive, and practically
impactful.
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