The emojification of sentiment on social media: Collection and analysis of a longitudinal Twitter sentiment dataset

Yin, Wang; Alkhalifa, Rabab; Zubiaga, Arkaitz

doi:10.48550/arxiv.2108.13898

Cited by 1 publication

(1 citation statement)

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“…While data labelling is expensive and hard to afford at scale, possible solutions may include use of distant supervision (Purver & Battersby, 2012) for data collection and labelling or labelling denser datasets for specific time periods which are temporally distant from each other, despite leaving gaps between the time periods under consideration. Distant supervision has been widely used for other tasks such as sentiment analysis (Go et al, 2009), leading to datasets covering in some cases over 7 years (Yin et al, 2021), however its applicability to stance detection has not been studied as much.…”

Section: Datasetsmentioning

confidence: 99%

Capturing stance dynamics in social media: open challenges and research directions

Alkhalifa

Zubiaga

2022

Int J Digit Humanities

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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.

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Section: Datasetsmentioning

confidence: 99%