Dynamic Social Media Monitoring for Fast-Evolving Online Discussions

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 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.