Rumour Detection in Online Social Networks: Recent Trends

Rani, Neetu; Das, Prasenjit; Bharadwaj, Amit

doi:10.2139/ssrn.3564070

Cited by 2 publications

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“…The stance detection task overlaps with, and is closely related to, different classification tasks such as sentiment analysis (Chakraborty et al, 2020), troll detection (Tomaiuolo et al, 2020), rumour and fake news detection (Zubiaga et al, 2018;Rani et al, 2020;Collins et al, 2020), and argument mining (Lawrence & Reed, 2020). In addition, stance can be impacted by the discursive and dynamic nature of the task (Mohammad et al, 2017;Somasundaran & Wiebe, 2009;Simaki et al, 2017).…”

Section: Capturing Dynamics In Stance Detectionmentioning

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: Capturing Dynamics In Stance Detectionmentioning

confidence: 99%

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

Alkhalifa

Zubiaga

2022

Int J Digit Humanities

View full text Add to dashboard Cite

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“…The stance detection task overlaps with, and is closely related to, different classification tasks such as sentiment analysis [17], troll detection [90], rumour and fake news detection [104,72,18], and argument mining [54]. In addition, stance can be impacted by its discursive and dynamic nature of the task [63,84,80].…”

Section: Capturing Dynamics In Stance Detectionmentioning

confidence: 99%

Capturing Stance Dynamics in Social Media: Open Challenges and Research Directions

Alkhalifa¹,

Zubiaga²

2021

Preprint

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Social media platforms provide a goldmine for mining public opinion on issues of wide societal interest. 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 with limited time coverage, interest in investigating longitudinal datasets has recently increased. Evolving dynamics in linguistic and behavioural patterns observed in new data require in turn 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 in emerging research considering dynamics, exploring different semantic and pragmatic factors that impact linguistic data in general, and stance particularly. We further discuss current directions in capturing stance dynamics in social media. We organise the challenges of dealing with stance dynamics, identify open challenges and discuss future directions in three key dimensions: utterance, context and influence.

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Rumour Detection in Online Social Networks: Recent Trends

Cited by 2 publications

References 0 publications

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

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

Capturing Stance Dynamics in Social Media: Open Challenges and Research Directions

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