Tracking and collecting fast-evolving online discussions provides vast data for studying social media usage and its role in people's public lives. However, collecting social media data using a static set of keywords fails to satisfy the growing need to monitor dynamic conversations and to study fast-changing topics. We propose a dynamic keyword search method to maximize the coverage of relevant information in fast-evolving online discussions. The method uses word embedding models to represent the semantic relations between keywords and predictive models to forecast the future time series. We also implement a visual user interface to aid in the decision-making process in each round of keyword updates. This allows for both human-assisted tracking and fully-automated data collection. In simulations using historical #MeToo data in 2017, our human-assisted tracking method outperforms the traditional static baseline method significantly, with 37.1% higher F-1 score than traditional static monitors in tracking the top trending keywords. We conduct a contemporary case study to cover dynamic conversations about the recent Presidential Inauguration and to test the dynamic data collection system. Our case studies reflect the effectiveness of our process and also points to the potential challenges in future deployment.

中文翻译：

---

动态社交媒体监控，用于快速发展的在线讨论

跟踪和收集快速发展的在线讨论可提供大量数据，用于研究社交媒体的使用及其在人们的公共生活中的作用。但是，使用一组静态关键字收集社交媒体数据无法满足监控动态对话和研究快速变化的主题的日益增长的需求。我们提出了一种动态关键字搜索方法，以在快速发展的在线讨论中最大程度地覆盖相关信息。该方法使用词嵌入模型来表示关键字之间的语义关系，并使用预测模型来预测未来的时间序列。我们还实现了可视化的用户界面，以帮助您在每一轮关键字更新中进行决策。这样既可以进行人工辅助跟踪，也可以进行全自动数据收集。在2017年使用历史#MeToo数据进行的模拟中，我们的人工跟踪方法明显优于传统的静态基准方法，在跟踪热门趋势关键字方面，F-1得分比传统的静态监控器高37.1％。我们进行了一个当代案例研究，以涵盖有关最近的总统就职典礼的动态对话，并测试动态数据收集系统。我们的案例研究不仅反映了我们流程的有效性，而且还指出了未来部署中的潜在挑战。