Temporal analysis of drifting hashtags in textual data streams: A graph-based application

• Published in: Expert systems with applications Vol. 257; p. 125007
• Main Authors: Garcia, Cristiano Mesquita, Britto, Alceu de Souza, Barddal, Jean Paul
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 10-12-2024
• Subjects: Hashtag drift; Temporal analysis; Text drift; Textual data streams; Temporal analysis; Textual data streams; Text drift; Hashtag drift
• ISSN: 0957-4174
• DOI: 10.1016/j.eswa.2024.125007

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.
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•The hashtag #mybodymychoice often used in different contexts other than its original.•Most frequent drifts regarded vaccination, politics, and drug legalization.•Girvan–Newman algorithm can help monitor changes over time in textual data streams.•The most significant drift happened in 2021, regarding the Covid-19 pandemic.