Dynamic Social Media Monitoring for Fast-Evolving Online Discussions
Maya Srikanth, Anqi Liu, Nicholas Adams-Cohen, Jian Cao, R. Michael, Alvarez, Anima Anandkumar

TL;DR
This paper introduces a dynamic keyword search approach using word embeddings and predictive models to improve social media monitoring of fast-changing online discussions, outperforming static methods in coverage and accuracy.
Contribution
The paper presents a novel dynamic keyword search method with a human-in-the-loop interface, enhancing social media data collection for rapidly evolving topics.
Findings
Human-assisted method achieves 37.1% higher F-1 score than static baseline.
Method effectively tracks trending keywords in real-time discussions.
Case studies demonstrate system's practical utility and future challenges.
Abstract
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…
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Taxonomy
TopicsComplex Network Analysis Techniques · Advanced Text Analysis Techniques · Web Data Mining and Analysis
