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Identity Activism is a new phenomenon afforded by the massive popularity of social media. It consists of the prominent display of a social movement symbol within a space reserved for description of the self. The 2022 Russian invasion of Ukraine provides a contemporary (yet unfortunate) opportunity to observe this phenomenon. Here, we introduce and explore this concept in the context of the recent Twitter trend of displaying the Ukraine flag emoji in bios and names to signal support of Ukraine. We explore several questions, including: how has the popularity of this trend changed over time, are users who display the flag more likely to be connected to others who do, and what types of users are and are not participating. We find that Ukraine flag emoji prevalence in both names and bios increased many-fold in late February 2022, with it becoming the 11th most prevalent emoji in bios and the 3rd most prevalent emoji in names during March. We also find evidence that users who display the flag in their bio or name are more likely to follow and be followed by others who also do so, as compared to users who do not. Finally, we observe that users who share politically left-leaning messages were most likely to display the emoji. Those who share account information from alternative social media sites and non-personal accounts appear least likely. These findings give us insight into how users participate in Identity Activism, what connections exist between participating users, and, in this particular case, what types of users participate.
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Pronoun Lists in Profile Bios Display Increased Prevalence, Systematic Co-Presence with Other Keywords and Network Tie Clustering among US Twitter Users 2015-2022
Over the past few years, pronoun lists have become more prevalent in online spaces. Currently, various LGBT+ activists, universities, and corporations encourage people to share their preferred pronouns. Little research exists examining the characteristics of individuals who do publicly share their preferred pronouns. Using Twitter bios from the US between early 2015 and June 30, 2022, we explored users’ expression of preferred pronouns. First, we noted the prevalence of users with pronoun lists within their bio has increased substantially. Second, we observed that certain linguistic tokens systematically co-occurred with pronoun lists. Specifically, tokens associated with left-wing politics, gender or sexual identity, and social media argot co-occurred disproportionately often alongside pronoun lists, while tokens associated with right-wing politics, religion, sports, and finance co-occurred infrequently. Additionally, we discovered clustering among Twitter users with pronouns in their bios. Specifically, we found an above-average proportion of the followers and friends of Twitter users with pronouns in their bio also had pronouns in their bios. Twitter users who did not share their preferred pronouns, on the other hand, were disproportionately unlikely to be connected with Twitter users who did.
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- Award ID(s):
- 1927227
- PAR ID:
- 10435786
- Date Published:
- Journal Name:
- Journal of Quantitative Description: Digital Media
- Volume:
- 3
- ISSN:
- 2673-8813
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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ABSTRACT In child sexual abuse (CSA) testimony, attorneys may ask children questions containing pronoun anaphora (e.g., “Where was your Dad?” “What did he do?”heis a pronoun anaphora for referent,Dad). To answer these questions, children must recall the pronoun's referent and appropriate answer to the question. This may be too complex a cognitive task for young children, especially when there are multiple question‐and‐answer (Q–A) turns between the referent and pronoun, leading to misunderstanding. We examined Q–A pairs containing pronouns in 40 CSA testimonies of 5 to 10‐year‐olds. Many attorneys' questions (24%) contained pronouns. Attorneys averaged 4.11 Q–A turns between the referent and pronoun. With each additional Q–A turn, the likelihood of misunderstanding increased. Children's age was also associated with a significant decrease in misunderstanding, meaning older children exhibited fewer misunderstandings. To reduce misunderstanding, those questioning children should clarify the referent quickly when their question contains pronoun anaphora.more » « less
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he pronoun “they” can refer to an individual who identifies as nonbinary, but it also is commonly used as a plural pronoun. How do listeners identify whether “they” is being used in a singular or plural sense? Arnold, Mayo, & Dong (in press) report three experiments in that test the role of explicitly introducing gender identity via pronouns, e.g. “This is Alex, and they use they/them pronouns.” Participants read short stories like “Alex went running with Liz and they fell down.” Answers to “Who fell down” indicated whether participants interpreted they as Alex or Alex-and-Liz. Singular interpretations of they were more likely when participants hear an explicit statement that Alex uses they/them pronouns, and in supporting discourse contexts. This paper is a companion to the main article, and reports analyses of individual difference measures. Participants self-reported familiarity with individuals who identify as nonbinary, which was expected to increase singular interpretations, but mostly it did not. In experiment 2 we also measured print exposure, but we found that it did not affect interpretation of singular they. In short, we saw virtually no effects of individual difference predictors.more » « less
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Arnold, J. E., Mayo, H., & Dong, L. (2020). Individual differences (or the lack of them) in comprehension of singular they. Technical Report #3. UNC Language Processing Lab, Department of Psychology & Neuroscience, University of North Carolina – Chapel Hill, Chapel Hill, North Carolina. The pronoun “they” can refer to an individual who identifies as nonbinary, but it also is commonly used as a plural pronoun. How do listeners identify whether “they” is being used in a singular or plural sense? Arnold, Mayo, & Dong (in press) report three experiments in that test the role of explicitly introducing gender identity via pronouns, e.g. “This is Alex, and they use they/them pronouns.” Participants read short stories like “Alex went running with Liz and they fell down.” Answers to “Who fell down” indicated whether participants interpreted they as Alex or Alex-and-Liz. Singular interpretations of they were more likely when participants hear an explicit statement that Alex uses they/them pronouns, and in supporting discourse contexts. This paper is a companion to the main article, and reports analyses of individual difference measures. Participants self-reported familiarity with individuals who identify as nonbinary, which was expected to increase singular interpretations, but mostly it did not. In experiment 2 we also measured print exposure, but we found that it did not affect interpretation of singular they. In short, we saw virtually no effects of individual difference predictors.more » « less
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Gender bias in language technologies has been widely studied, but research has mostly been restricted to a binary paradigm of gender. It is essential also to consider non-binary gender identities, as excluding them can cause further harm to an already marginalized group. In this paper, we comprehensively evaluate popular language models for their ability to correctly use English gender-neutral pronouns (e.g., singular they, them) and neo-pronouns (e.g., ze, xe, thon) that are used by individuals whose gender identity is not represented by binary pronouns. We introduce Misgendered, a framework for evaluating large language models’ ability to correctly use preferred pronouns, consisting of (i) instances declaring an individual’s pronoun, followed by a sentence with a missing pronoun, and (ii) an experimental setup for evaluating masked and auto-regressive language models using a unified method. When prompted out-of-the-box, language models perform poorly at correctly predicting neo-pronouns (averaging 7.6% accuracy) and gender-neutral pronouns (averaging 31.0% accuracy). This inability to generalize results from a lack of representation of non-binary pronouns in training data and memorized associations. Few-shot adaptation with explicit examples in the prompt improves the performance but plateaus at only 45.4% for neo-pronouns. We release the full dataset, code, and demo at https://tamannahossainkay.github.io/misgendered/.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.51685/jqd.2023.003
