Using Twitter data to explore public discourse to antiracism movements.

Rachel L. Walker, Linda K. Kaye

Research output: Contribution to journalArticle (journal)peer-review

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Abstract

We used public Twitter data to explore Black Lives Matter (BLM)-related hashtags to explore collectivism and sentiment categories.
Tweets containing BLM hashtags were collected at two time points; during Black History Month (BHM) and a non-BHM. At each
time point, two data extractions were performed; one searching tweets containing BHM hashtags and another without. A 2 × 2 design was used to assess BHM hashtag and time point on emotional tone and personal pronoun use. Findings showed main effects of hashtag on all variables. Hashtags promoted greater use of collective pronouns, lesser use of singular pronouns, greater positivity, and lesser negative tone. Time point had no effect on plural pronoun use, and impacted differentially on emotional tone depending on hashtag use. Positive associations were found between plural pronoun use and positive tone but only when hashtags were used. Overall, our findings highlight the importance of online discourse to understand collectivism and sentiment in respect of antiracism movements
Original languageEnglish
Pages (from-to)1-20
JournalTechnology, Mind and Behavior
Volume3
Issue number2
Early online date30 Apr 2022
DOIs
Publication statusPublished - 30 Apr 2022

Keywords

  • BlackLivesMatter
  • Black History Month
  • Twitter
  • hashtags
  • social identity theory

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