Time-Aware Predictions of Moments of Change in Longitudinal User Posts on Social Media-Reference-Cited by-同舟云学术

Time-Aware Predictions of Moments of Change in Longitudinal User Posts on Social Media

Published:2023 Issue: Volume: Page:293-305
ISSN:0302-9743
Container-title:Advanced Analytics and Learning on Temporal Data
language:
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Author:

Hills Anthony,Tsakalidis Adam,Liakata Maria

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-49896-1_19

Reference34 articles.

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2. Amir, S., Coppersmith, G., Carvalho, P., Silva, M.J., Wallace, B.C.: Quantifying mental health from social media with neural user embeddings. In: Machine Learning for Healthcare Conference, pp. 306–321. PMLR (2017)

3. Azim, T., Gyanendro Singh, L., Middleton, S.E.: Detecting moments of change and suicidal risks in longitudinal user texts using multi-task learning. In: Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology, pp. 213–218. Association for Computational Linguistics, Seattle, USA, July 2022. https://doi.org/10.18653/v1/2022.clpsych-1.19. https://aclanthology.org/2022.clpsych-1.19

4. Bagroy, S., Kumaraguru, P., De Choudhury, M.: A social media based index of mental well-being in college campuses. In: Proceedings of the 2017 CHI Conference on Human factors in Computing Systems, pp. 1634–1646 (2017)

5. Bayram, U., Benhiba, L.: Emotionally-informed models for detecting moments of change and suicide risk levels in longitudinal social media data. In: Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology, pp. 219–225. Association for Computational Linguistics, Seattle, USA, July 2022. https://doi.org/10.18653/v1/2022.clpsych-1.20. https://aclanthology.org/2022.clpsych-1.20