分析推特上新冠话题语义随时间演变，揭示公众讨论与现实数据的关联。
Revealing COVID-19's Social Dynamics: Diachronic Semantic Analysis of Vaccine and Symptom Discourse on Twitter
- 提出无监督动态词嵌入方法，捕捉社交媒体语义变迁。
- 发现疫苗和症状相关词汇在疫情不同阶段的语义演化模式。
- 适合关注社会舆情分析、计算社会科学的研究者参考。
社交媒体因每日生成的海量文本和用户自由互动行为，成为洞察公众舆论动态与社会影响的重要来源。然而，由于词语含义随时间演变（语义漂移）现象的存在，此类分析面临挑战。本文提出一种无需预设锚点词的无监督动态词嵌入方法，通过词共现统计与动态更新机制，自适应地调整嵌入表示，以应对数据稀疏、分布不均及语义协同效应等问题。在大规模新冠推特数据集上评估，该方法揭示了疫苗与症状相关实体在疫情各阶段的语义演化路径，并发现其与真实世界统计数据之间的潜在关联。主要贡献包括动态嵌入技术、新冠语义漂移的实证分析，以及对计算社会科学中语义漂移建模的探讨。本研究实现了对社交媒体长期语义动态的捕捉，有助于理解公共话语与集体行为。
原文摘要 · Abstract (English)
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.
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