Temporal Analysis of Drifting Hashtags in Textual Data Streams: A
  Graph-Based Application
Cristiano M. Garcia Alceu de Souza Britto Jr Jean Paul Barddal
Social media has played an important role since its emergence. People use the
internet to express opinions about anything, making social media platforms a
social sensor. Initially supported by Twitter, the hashtags are now in use on
several social media platforms. Hashtags are helpful to tag, track, and group
posts on similar topics. In this paper, 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. More
specifically, we analyzed the #mybodymychoice hashtag between 2018 and 2022. In
addition, we offer insights about some hashtags found in the study.
Furthermore, 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 suggest that #mybodymychoice
significantly drifted to vaccination and Covid-19-related topics.