Abstract
Social media platforms provide a goldmine for mining public opinion on issues of wide societal interest and impact. Opinion mining is a problem that can be operationalised by capturing and aggregating the stance of individual social media posts as supporting, opposing or being neutral towards the issue at hand. While most prior work in stance detection has investigated datasets that cover short periods of time, interest in investigating longitudinal datasets has recently increased. Evolving dynamics in linguistic and behavioural patterns observed in new data require adapting stance detection systems to deal with the changes. In this survey paper, we investigate the intersection between computational linguistics and the temporal evolution of human communication in digital media. We perform a critical review of emerging research considering dynamics, exploring different semantic and pragmatic factors that impact linguistic data in general, and stance in particular. We further discuss current directions in capturing stance dynamics in social media. We discuss the challenges encountered when dealing with stance dynamics, identify open challenges and discuss future directions in three key dimensions: utterance, context and influence.
Highlights
With the proliferation of social media and blogs that enable anyone to post and share content, professional accounts from news organisations and governments aren’t any longer the sole reporters of events of public interest (Kapoor et al, 2018)
Unlike semantic changes which capture word fluctuations over time, temporal contextual variability may occur in corpus-based predictive models. In this survey paper discuss the impact of temporal dynamics in the development of stance detection models, by reviewing relevant literature in both stance detection and temporal dynamics of social media
Our survey delves into three main factors affecting the temporal stability of stance detection models, which includes utterance, context and influence
Summary
With the proliferation of social media and blogs that enable anyone to post and share content, professional accounts from news organisations and governments aren’t any longer the sole reporters of events of public interest (Kapoor et al, 2018). The other direction, which is the focus of this paper, defines stance detection as a three-way classification task where the stance of each post is one of supporting, opposing or neutral (Augenstein et al, 2016), indicating the viewpoint of a post towards a particular issue. This enables mining public opinion as the aggregate of stances of a large collection of posts. We present current progress in addressing these factors, discuss existing datasets with their potential and limitations for investigating stance dynamics, as well as identify open challenges and future research directions.
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