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Tracking and collecting fast-evolving online discussions provides vast datafor 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 satisfythe growing need to monitor dynamic conversations and to study fast-changingtopics. We propose a dynamic keyword search method to maximize the coverage ofrelevant information in fast-evolving online discussions. The method uses wordembedding models to represent the semantic relations between keywords andpredictive models to forecast the future time series. We also implement avisual user interface to aid in the decision-making process in each round ofkeyword updates. This allows for both human-assisted tracking andfully-automated data collection. In simulations using historical #MeToo data in2017, our human-assisted tracking method outperforms the traditional staticbaseline method significantly, with 37.1% higher F-1 score than traditionalstatic monitors in tracking the top trending keywords. We conduct acontemporary case study to cover dynamic conversations about the recentPresidential Inauguration and to test the dynamic data collection system. Ourcase studies reflect the effectiveness of our process and also points to thepotential challenges in future deployment.

Dynamic Social Media Monitoring for Fast-Evolving Online Discussions