Temporal Analysis of Drifting Hashtags in Textual Data Streams: A Graph-Based Application

TL;DR

The paper addresses how hashtags drift across topics over time in text streams, focusing on the hashtag #mybodymychoice from 2018 to 2022. It adopts a graph-based approach, applying the Girvan-Newman community detection method on annual hashtag co-occurrence graphs to identify topic communities and their evolution. Key findings show that #mybodymychoice shifts from its original associations to topics such as vaccination, politics, and drug legalization, with 2021 showing the strongest drift toward Covid-19-related discourse and vaccination. The work provides a practical framework for tracking opinion and sentiment changes about entities on social media and demonstrates the utility of graph-based analysis for monitoring topic drift in textual data streams.

Abstract

Initially supported by Twitter, hashtags are now used on several social media platforms. Hashtags are helpful for tagging, tracking, and grouping posts on similar topics. In this paper, based on a hashtag stream regarding the hashtag #mybodymychoice, we analyze hashtag drifts over time using concepts from graph analysis and textual data streams using the Girvan-Newman method to uncover hashtag communities in annual snapshots between 2018 and 2022. In addition, we offer insights about some correlated hashtags found in the study. Our approach can be useful for monitoring changes over time in opinions and sentiment patterns about an entity on social media. Even though the hashtag #mybodymychoice was initially coupled with women's rights, abortion, and bodily autonomy, we observe that it suffered drifts during the studied period across topics such as drug legalization, vaccination, political protests, war, and civil rights. The year 2021 was the most significant drifting year, in which the communities detected and their respective sizes suggest that #mybodymychoice had a significant drift to vaccination and Covid-19-related topics.