Enhancing stance detection through sequential weighted multi-task learning
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Human-Computer Interaction,Media Technology,Communication,Information Systems
Link
https://link.springer.com/content/pdf/10.1007/s13278-023-01169-7.pdf
Reference80 articles.
1. Al-Ghadir AI, Azmi AM, Hussain A (2021) A novel approach to stance detection in social media tweets by fusing ranked lists and sentiments. Inf Fusion 67:29–40. https://doi.org/10.1016/j.inffus.2020.10.003
2. Aldayel A, Magdy W (2019a) Assessing sentiment of the expressed stance on social media. In: International conference on social informatics, pp 277–286. https://doi.org/10.1007/978-3-030-34971-4_19
3. Aldayel A, Magdy W (2019b) Your stance is exposed! analyzing possible factors forstance detection on social media. Proc ACM on Hum Comput Interact 3:1–20
4. AlDayel A, Magdy W (2021) Stance detection on social media: state of the art and trends. Inf Process Manag. https://doi.org/10.1016/j.ipm.2021.102597
5. Alec R, Jeffrey W, Rewon C, et al (2019) Language models are unsupervised multitask learners. OpenAI Blog 1
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