Temporal Analysis of Drifting Hashtags in Textual Data Streams: A Graph-Based Application
This provides a method for monitoring opinion changes on social media, but it is incremental as it applies existing graph techniques to a specific hashtag dataset.
The paper analyzed drifting hashtags over time using graph-based methods on the #mybodymychoice stream from 2018 to 2022, finding that the hashtag shifted from women's rights to topics like vaccination and Covid-19 in 2021, with 2021 identified as the most significant drifting year.
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.