@inproceedings{wang-etal-2024-revealing,
title = "Revealing {COVID}-19`s Social Dynamics: Diachronic Semantic Analysis of Vaccine and Symptom Discourse on {T}witter",
author = "Wang, Zeqiang and
Wu, Jiageng and
Wang, Yuqi and
Wang, Wei and
Yang, Jie and
Johnson, Jon and
Sastry, Nishanth and
De, Suparna",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.193/",
doi = "10.18653/v1/2024.findings-emnlp.193",
pages = "3383--3394",
abstract = "Social media is recognized as an important source for deriving insights into public opinion dynamics and social impacts due to the vast textual data generated daily and the {\textquoteleft}unconstrained' behavior of people interacting on these platforms. However, such analyses prove challenging due to the semantic shift phenomenon, where word meanings evolve over time. This paper proposes an unsupervised dynamic word embedding method to capture longitudinal semantic shifts in social media data without predefined anchor words. The method leverages word co-occurrence statistics and dynamic updating to adapt embeddings over time, addressing the challenges of data sparseness, imbalanced distributions, and synergistic semantic effects. Evaluated on a large COVID-19 Twitter dataset, the method reveals semantic evolution patterns of vaccine- and symptom-related entities across different pandemic stages, and their potential correlations with real-world statistics. Our key contributions include the dynamic embedding technique, empirical analysis of COVID-19 semantic shifts, and discussions on enhancing semantic shift modeling for computational social science research. This study enables capturing longitudinal semantic dynamics on social media to understand public discourse and collective phenomena."
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<abstract>Social media is recognized as an important source for deriving insights into public opinion dynamics and social impacts due to the vast textual data generated daily and the ‘unconstrained’ behavior of people interacting on these platforms. However, such analyses prove challenging due to the semantic shift phenomenon, where word meanings evolve over time. This paper proposes an unsupervised dynamic word embedding method to capture longitudinal semantic shifts in social media data without predefined anchor words. The method leverages word co-occurrence statistics and dynamic updating to adapt embeddings over time, addressing the challenges of data sparseness, imbalanced distributions, and synergistic semantic effects. Evaluated on a large COVID-19 Twitter dataset, the method reveals semantic evolution patterns of vaccine- and symptom-related entities across different pandemic stages, and their potential correlations with real-world statistics. Our key contributions include the dynamic embedding technique, empirical analysis of COVID-19 semantic shifts, and discussions on enhancing semantic shift modeling for computational social science research. This study enables capturing longitudinal semantic dynamics on social media to understand public discourse and collective phenomena.</abstract>
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%0 Conference Proceedings
%T Revealing COVID-19‘s Social Dynamics: Diachronic Semantic Analysis of Vaccine and Symptom Discourse on Twitter
%A Wang, Zeqiang
%A Wu, Jiageng
%A Wang, Yuqi
%A Wang, Wei
%A Yang, Jie
%A Johnson, Jon
%A Sastry, Nishanth
%A De, Suparna
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F wang-etal-2024-revealing
%X Social media is recognized as an important source for deriving insights into public opinion dynamics and social impacts due to the vast textual data generated daily and the ‘unconstrained’ behavior of people interacting on these platforms. However, such analyses prove challenging due to the semantic shift phenomenon, where word meanings evolve over time. This paper proposes an unsupervised dynamic word embedding method to capture longitudinal semantic shifts in social media data without predefined anchor words. The method leverages word co-occurrence statistics and dynamic updating to adapt embeddings over time, addressing the challenges of data sparseness, imbalanced distributions, and synergistic semantic effects. Evaluated on a large COVID-19 Twitter dataset, the method reveals semantic evolution patterns of vaccine- and symptom-related entities across different pandemic stages, and their potential correlations with real-world statistics. Our key contributions include the dynamic embedding technique, empirical analysis of COVID-19 semantic shifts, and discussions on enhancing semantic shift modeling for computational social science research. This study enables capturing longitudinal semantic dynamics on social media to understand public discourse and collective phenomena.
%R 10.18653/v1/2024.findings-emnlp.193
%U https://aclanthology.org/2024.findings-emnlp.193/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.193
%P 3383-3394
Markdown (Informal)
[Revealing COVID-19’s Social Dynamics: Diachronic Semantic Analysis of Vaccine and Symptom Discourse on Twitter](https://aclanthology.org/2024.findings-emnlp.193/) (Wang et al., Findings 2024)
ACL
- Zeqiang Wang, Jiageng Wu, Yuqi Wang, Wei Wang, Jie Yang, Jon Johnson, Nishanth Sastry, and Suparna De. 2024. Revealing COVID-19’s Social Dynamics: Diachronic Semantic Analysis of Vaccine and Symptom Discourse on Twitter. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 3383–3394, Miami, Florida, USA. Association for Computational Linguistics.