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Dynamic Social Media Monitoring for Fast-Evolving Online Discussions

Srikanth, Maya and Liu, Anqi and Adams-Cohen, Nicholas and Cao, Jian and Alvarez, R. Michael and Anandkumar, Animashree (2021) Dynamic Social Media Monitoring for Fast-Evolving Online Discussions. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. Association for Computing Machinery , New York, NY, pp. 3576-3584. ISBN 978-1-4503-8332-5.

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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 trajectory of keywords. 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, achieving 37.1% improvement in F-1 score in the task of 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.

Item Type:Book Section
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Alvarez, R. Michael0000-0002-8113-4451
Additional Information:© 2021 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License. This work was supported in part by Oracle Cloud credits and related resources provided by the Oracle for Research program.
Record Number:CaltechAUTHORS:20210510-121148191
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Official Citation:Maya Srikanth, Anqi Liu, Nicholas Adams-Cohen, Jian Cao, R. Michael Alvarez, and Anima Anandkumar. 2021. Dynamic Social Media Monitoring for Fast-Evolving Online Discussions. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’21), August 14–18, 2021, Virtual Event, Singapore. ACM, New York, NY, USA, 9 pages.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109034
Deposited By: Tony Diaz
Deposited On:10 May 2021 19:30
Last Modified:20 Sep 2021 18:52

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