  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.
