Tweet to #SaveWomensSports: Visualizing Anti-Trans Stigma in Athletics
Haley Swartz, Clemson University
Introduction
In August 2021, Michelob ULTRA announced a $100 million commitment to support gender equality in sports (Michelob ULTRA Commits $100 Million to Support Gender Equality in Sports, 2021). The brand urges audiences to “save” women’s sports; however, Michelob’s understanding of saving women’s sports––a literal “save” on social media to allow for more visibility––is quite different than groups who urge us to #SaveWomensSports (Michelob ULTRA, 2021). While Michelob is interested in equal representation (utilizing #SaveItSeeIt and #WomensSports), organizations and private individuals promoting #SaveWomensSports support legislation that prohibits transgender women and girls from competing on teams that match their gender identity. Importantly, Michelob’s “Save It, See It” video, which announced the campaign, featured Cece Telfer, a transgender women’s track athlete, and the brand’s Twitter post included she/her pronouns for each featured athlete. Telfer’s line in the video, “You don’t have to like how I got here,” speaks directly to the ongoing culture war taking place as sports governing bodies, school systems, and state legislatures contemplate the inclusion of transgender women and girls in gendered competition. It is not surprising, then, that Michelob received swift and relentless criticism of their campaign. Organizations such as Save Women’s Sports (@SaveWomensSport) and Alliance Defending Freedom (@AllianceDefends) were quick to respond to @MichelobUltra, calling out the “promotion of males competing as females” (Figure 1 and Figure 2).
Figure 1: Tweet by @SaveWomensSport following the Michelob Ultra Campaign
Figure 2: Tweet by @AllianceDefends following the Michelob Ultra Campaign
While the original post by @MichelobUltra would not be considered viral, it is telling that of the 96 replies to the Tweet, 75 include anti-trans sentiment. For example, @Euralesister wrote “Centering a man in a campaign for women’s sports is gross. Erasing women yet again” and @JackHateCharles complained “I just watched your women in sports commercial with a man pretending to be a woman”––both comments misgender Telfer while framing her inclusion in the campaign, as well as in women’s sports in general, as wrong. Comments made on the campaign’s YouTube video are equally as telling. A word cloud generated by using comment text shows that commenters were largely concerned with the inclusion of Telfer in the video (Figure 3). This is seen in the use of words like “biological” and the repeated use of the gendered terms “men,” “male,” and “man.” Additionally, comments accused the company of “woke” politics and expressed dissatisfaction with Michelob’s products as a result.
Figure 3: Word cloud generated by using comment text from the Michelob ULTRA #SaveItSeeIt video.
This campaign and its attempt at advocacy––specifically by including a transgender athlete in a campaign about women’s sports––created space for audiences to not only engage with cultural politics, but to react to the brand’s political stance. Five days after the original #SaveItSeeIt Twitter post, Michelob ditched the hashtag and instead created weekly threads with links to women’s sports schedules. Brand crisis averted; however, the case of #SaveItSeeIt opens space to consider anti-trans sentiment in the effort to “save” women’s sports. Many advocates for “fairness” in women’s and girls’ sports focus on equity in representation, opportunity, and (for professional sports) pay between men and women athletes. Anti-trans lobbyists, like those replying negatively to the #SaveItSeeIt campaign, redirect these arguments, framing “fairness” as competition between cis-gendered women and girls only.
The aim of this project is to use social media data and data visualization––ranging from word frequency data transformed into simple word clouds, as shown above, to complex network analyses emerging from vast Twitter datasets––to build an understanding of anti-trans encounters with “saving” women’s sports. In addition to contributing to an understanding of how anti-trans arguments function within the space of women’s sports, this project models a feminist approach to engaging with networked technologies, data collection and synthesis, and data visualization. Recognizing that projects involving large sets of data, digital technologies, and social media networks are inherently “messy” (as Desiree Dighton and Laurie Gries have recognized), I demonstrate how approaching the research process with “strategic contemplation” (Royster and Kirsch, 2012) makes space for the study of politically charged conversations. The conversations, in the case of this project, are those surrounding the exclusion of transgender women and girls from competing on sports teams that match their gender identity.
Making Space with Data
In a webtext featured in the digital journal Kairos, Desiree Dighton argues that scholars of rhetoric and writing studies must engage with digital technologies and large sets of data. Dighton asserts, “Gaining experience with data analysis and visualization technologies will allow us to push against their affordances and perhaps contribute insight into the ways in which future tools might be designed in more just and rhetorically informed ways” (2019, para. 3). We know that data is neither objective nor neutral. The same is true especially in the collection of that data and formulation into data visualizations. As Dighton puts it, “The seemingly abstract, decontextualized qualities of data are perhaps what allows it to be such a convincing, though often imperceptible, vehicle of dominant cultural logics and social values” (“Feminist Critiques”). An ethical (and feminist) approach to data visualization should challenge claims of data objectivity and neutrality, question the universalism of data, and emphasize that knowledge is situated. The researcher (and designer) must be explicit about choices made in design as well as in the affordances of the visualization technology. This involves accounting for both human and non-human influence on the design of the visualization––influence that directly affects our perception of the data. In this article, I follow Dighton in not only making sense of data, but also in contemplating my role as a researcher in the shaping of it.
Dighton calls for scholars to “strive toward deeper ways of knowing digital data that make apparent its very human, cultural characteristics” and perhaps most importantly, “strive to attend to how the power hierarchies that structure our social world play a role in structuring our data, data analysis, and digital projects” (2019, para. 5). She recognizes that in foregrounding the human and our relationship to data in this way, projects might be “messier.” Here, Dighton extends the work of rhetoric and writing studies scholar Laurie Gries (2015), whose method of iconographic tracking takes on the messiness of research projects that Dighton describes. The foundation of this method is the key notion that, as Gries notes, rhetoric is “not as still as we may think,” and that it “prevails beyond its initial moment of production” (p. 7). Gries’ view of rhetoric is that it takes on a life of its own, often apart from the rhetor, as it transforms, circulates, and interacts across media and genre. Because of our increasingly networked society and the proliferation of digital forms of media, rhetoric becomes “more like an unfolding event––a distributed, material process of becoming in which divergent consequences are actualized with time and space” (Ibid.). Iconographic tracking traces rhetoric through its iterations and accretions––in Gries’ example, the vast variations of the Obama Hope image spread across media, time, and space––in order to attune to its movement, its interconnectedness, and its consequentiality.
While iconographic tracking is an important contribution to new materialist, visual rhetoric, and circulation studies––especially as a purposeful method for exploring the way images come to matter––Gries has inspired further scholarship in digital circulation of ideas and images. This includes research that traces the rhetorical life of hashtags as digital activism (Edwards and Lang, 2018; Ehrenfeld, 2020; Fredlund et al, 2020.; Lang 2019). For example, Edwards and Lang argue that a new materialist framework reveals how hashtags might “affect and produce affects in a diverse range of other bodies inside and outside of their networks of origin” and enables scholars to “understand that the circulation of a hashtag is made possible by a broader assemblage of lively elements” (p. 120). Following Gries’ clear method––which involves collecting, assembling, and narrowing large amounts of networked data and then attending to seven material processes that elucidate the life and meanings of an image (composition, production, transformation, distribution, circulation, collectivity, and consequentiality)––aids scholars in making sense of the complex messiness of things––ideas, images, texts––as they unfold in a networked landscape. Further, this new materialist methodology takes seriously the role of technology in transposing and transforming rhetoric as it moves.
Placing Dighton in conversation with Gries highlights the importance of working within and through the affordances of technology throughout the research process. This involves interrogating the tools of data collection, tools of data synthesis, and the tools of circulation in a mediated environment. I would like to add to this eye toward technology a feminist method of “strategic contemplation” as described by Jaqueline Jones Royster and Gesa E. Kirsch (2012), which enlists researchers to “linger deliberately” within the research stages, attending both to outward and inward processes throughout (p. 84). Strategic contemplation invites scholars “to pause, to wonder, to reflect, to see what else they might not have considered, and to articulate these moments in language” (p. 86), making space for the researcher to dwell in the process. This contemplative space is useful especially for politically charged projects, where the use of certain ideas and phrases are heavy with history, with hegemonic norms, and with polarizing opinions. Infusing digital methods with strategic contemplation, particularly in following social media conversations loaded with anti-trans stigma, allows the researcher to dig into data and refocus attention to trace digital encounters. In a discussion about the terms used in debates over trans inclusion, Sara Ahmed articulates, “To start to try and make sense of them by starting with them, would be like turning up in the middle of a conversation, hearing a reaction, and not know what came before that provoked a reaction” (2021, para. 9). In order to understand the full force of “saving” women’s sports as it unfolds on social media, it is imperative to take stock of conversations, of ideas as they circulate, and of the ways that groups and individuals deploy the concept of “saving.” Further, this article explores how researchers might use data analysis and visualization to represent such conversations.
In what follows, I demonstrate how the use of strategic contemplation in data analysis and visualization makes space for the study of timely, politically charged online conversations. Because of the constraints of time, I have limited the scope of this project to the conversations surrounding the exclusion of transgender women and girls from sports that match their gender identity taking place in October of 2021. Using three data draws, completed on October 4, October 20, and October 28, I analyze 8,875 tweets using data pulled from NodeXL. I then interpret the data by using data visualizations produced in NodeXL itself as well as in Tableau, a powerful data visualization software. Data visualization provides ample opportunity for analysis and interpretation. A key finding from the visualization of these particular data sets is that #SaveWomensSports conversations are not as ubiquitous as they seem; these conversations that influence public policy and legislative decisions must be taking place outside of Twitter.
Saving Whom?
In my initial data collection phase, I crawled through social media accounts that promote #SaveWomensSports, focusing on accounts in the US. There are several organizations in the UK that are invested in this sort of anti-trans movement in sports, but I am more interested in focusing on activism here as it relates to policies and legislation in state and local governments.
I used NodeXL, a free and open-access Excel add-in, to gather, analyze, and visualize network data from the Twitter hashtag #SaveWomensSports. Developed by the Social Media Research Foundation, a nonprofit dedicated to fostering open tools, data, and scholarship in social media, NodeXL is a versatile platform for network analysis. Using NodeXL, a researcher can both collect data from social sites and apps as well as generate insightful network visualizations and reports from that data. An important feature of NodeXL is its accessibility to those without programming expertise, which allows for the swift production of network statistics, metrics, and visualizations within the familiar environment of an Excel spreadsheet, making sense out of the complex messiness of social media data. Further, its interface includes straightforward filtering and display options that bring critical network structures into focus. When it comes to digital ephemera like hashtags, NodeXL helps researchers to trace and track use across conversations; it is an automated iteration of Gries’ data collection method that creates space for the researcher to linger in the data.
Using NodeXL’s Twitter search function, I collected data from conversations using #SaveWomensSports and produced network visualizations of that data with the Hanel-Koren fast multiscale algorithm. The result includes multiple node graphs that trace connections between users who like, retweet, and reply to tweets using #SaveWomensSports (Figure 4). By dwelling in these visualizations, I discovered that @SaveWomensSport, stemming from the Save Women’s Sports coalition founded by Beth Stelzer (@BethStelzer), seems to be a central figure in the rhetoric of “saving” as well as anti-trans rhetoric––such as the use of the hashtag #sexnotgender, which points to broader arguments about determining which bodies count as female. A node connection graph of the #SaveWomensSports search reveals that main conversations occur within a few key user networks, with smaller conversations loosely connected (Figure 5). Visualizing the data in this way makes apparent the connections and relationships across conversations. The Harel-Koren graphs create a network for each conversation; each node in the network represents a user who interacted with an original post. Nodes are connected by vector lines; some lines cross conversations to show users who interact across conversations. Lingering within this space, I focused on the user account @SaveWomensSport, a frequent contributor to conversations, to find key account interactions using the Twitter User Network function of NodeXL (Figure 6). From this visualization, I could also isolate the accounts that most often interacted with @SaveWomensSport, providing insight into the audience of this active anti-trans account and its content.
Figure 4: NodeXL visualization of conversations including #SaveWomensSports using the Harel-Koren Fast Multiscale graph.
Figure 5: NodeXL visualization of node connections within conversations including #SaveWomensSports using the Harel-Koren Fast Multiscale graph.
Figure 6: NodeXL visualization of Twitter user network for @SaveWomensSport.
At the time of my initial data collection on October 4, 2021, the conversations surrounding #SaveWomensSports were largely surrounding the inclusion of Laurel Hubbard, the transgender athlete from Australia, in the weightlifting competition during the Beijing Summer Olympics. Visualizations of the words and word pairs from this data set point to this news event; however, through analysis of hashtag use, I discovered other conversations occurring at the same time, including further international conversations from the UK and Canada as well as hashtags explicitly expressing anti-trans sentiment. A word cloud generated from the hashtags included in #SaveWomensSports tweets from September 26, 2021 to October 4, 2021 illustrates this data (Figure 7). Word clouding is one method included in a strategy that Derek Mueller calls “word watching”: tracing concepts and terminologies, as well as their interrelations, in patchy, complex, and diffused landscapes (77). These patterns emerge by using digital technologies to mine and count keywords. As a visualization tool, word clouding is a simple way to render word counts visually traceable. There are many free and easy-to-use word cloud generators available; I used WordClouds.com to generate the initial word clouds for my data set. While Mueller admits that word clouds are “methodologically basic” and a “temporary abstraction” of complex text, he asserts that they “function as powerful setups” that help us to understand relationships in language use (78-79). Put another way, word clouds are a useful starting point in visualizing language. When applied to Twitter hashtags, as in Figure 7, a word cloud highlights concurrent subject matter and conversations––connections between hashtags and between accounts that produce them.
Figure 7: Word cloud representing hashtags included in #SaveWomensSports tweets.
One of the interesting connections I found in this initial social media dive is @IWF, the Twitter account for the Independent Women’s Forum, which focuses on many women’s issues from a conservative lens. Using the Social Bearing App from the Bellingkat Toolkit––a free resource to search and analyze tweets––I was able to find when and where the @IWF account engaged with #SaveWomensSports.[1] Additionally, as I increased the number of tweets analyzed, I could gauge the centrality of that hashtag to the account’s conversations. Similar to many of the accounts I surveyed, #SaveWomensSports gained relevance earlier in 2021, peaking over the summer. Seen in the word clouds below generated within Social Bearing, the use of #SaveWomensSports is apparent in the last 200 tweets (left, below); however, as I increased the number of tweets analyzed, drawing back to almost 1800 tweets over 85 days (Figure 8, right), #SaveWomensSports becomes one of the most used hashtags.
Figure 8: Word clouds created in Social Bearing, representing the shift in use of hashtags over time for Twitter user @IWF.
This visualization based on my manipulation of the data points––in this case, increasing the number of tweets analyzed––allows us to see the relevance of #SaveWomensSports in user tweets. While #SaveWomensSports was a frequently used hashtag for the @IWF account at the time of analysis, we can see from its size increase relative to the other hashtags represented that it was one of the most used hashtags over the entire observation period. If I had isolated the data to the latest 200 tweets, I would miss how salient #SaveWomensSports is in @IWF conversations. The hashtag may not have been prevalent in the @IWF account’s latest conversations, but zooming out demonstrates that #SaveWomensSports interacts with media coverage, legislative endeavors, and lobby pushes of key organizations such as the Independent Women’s Forum and Save Women’s Sports.
As I have demonstrated in this section, researchers make sense of large, complex digital datasets not only by generating network graphs to show conversations and connections and creating simple word clouds to represent word (or hashtag) frequency, but by lingering within these visualizations to trace digital encounters, further honing the data to allow patterns to emerge. In the following section, I consider how #SaveWomensSports conversations interact with tweets about anti-trans legislation, using the Tableau software as a visualization tool.
#HB25
In the midst of my data collection, the Texas state legislature passed––and Governor Greg Abbott signed into law––House Bill 25, which requires public school students in Texas to compete in interscholastic athletic competitions based on their biological sex. Texas is the eighth state to pass laws restricting transgender athletes, but legislators in 32 states have introduced similar bills. The Texas bill articulates that schools “may not allow a student to compete in interscholastic athletic competition sponsored or authorized by the district or school that is designated for the biological sex opposite to the student's biological sex”––a wordy mess that instead of stating that players must compete according to biological sex, states that they must not compete for a team designated for the opposite sex (Swanson et. al, 2021, p. 2). This seems to particularly discipline people who are transgender. Additionally, the bill defines biological sex as that which is stated on an official birth certificate or government record that is “entered at or near the time of the student’s birth” and not modified unless done so because of clerical error. The Texas act took effect on January 18, 2022 without injunction or appeal.
As the Texas House and Senate both passed HB25 on October 18, 2021, a hashtag search via NodeXL for both #HB25 and #SaveWomensSports, performed on October 20, 2021 and October 28, 2021 are both illuminating. Interestingly, I found that the overwhelming majority of tweets using #HB25 were in networks that are against the legislation rather than voicing support for it. But before I make observations about the totality of the NodeXL data draws, I first pause and consider each separately. When attempting to analyze large data sets such as those obtained from NodeXL, the Tableau data visualization software is unparalleled. I engaged in Tableau training concurrently as I collected and engaged with the data for this project. Tableau is a powerful tool, and throughout my exploration of this software I was struck by the ability to manipulate data in many different ways––both ethically and otherwise. With a bit of creative play to dig through and dwell in the messiness of the data, word analysis emerged as an effective path towards making inferences from the copious #HB25 tweets. Using data separately from the October 20th data set (#HB25 Bill) and the October 28th data set (#HB25 Signed), I created Tableau dashboards showing word clouds, the top words used, and the top word pair used in #HB25 tweets.
Particularly relevant to this project is the fact that #SaveWomensSports does not appear in the most frequently used words (or hashtags, for that matter) for either data set. The #HB25 Bill data set included both #protecttranskids and #letkidsplay, and the top word pair was “anti trans”––indicating that most of the tweets using #HB25 were against the passing of the bill in the Texas legislature. Surrounding the signing of House Bill 25 by Governor Greg Abbott, words became more charged. Seen in the top words used for this data set are words like “draconian” and “cruel.” The top word pair was again “anti trans,” and this pair is interestingly joined by the pair “quietly signed,” which indicates the sentiment that Abbott had nefarious intentions in signing the bill (to be sure, the bill is politically charged and is motivating for Abbott’s base).
Figure 9: Word analysis of tweets containing #HB25 posted from October 11, 2021 to October 20, 2021. Visualization created in Tableau.
Figure 10: Word analysis of tweets containing #HB25 posted from October 21, 2021 to October 28, 2021. Visualization created in Tableau.
To understand how to interpret the combined data set, encompassing #HB25 tweets from October 11, 2021 to October 28, 2021, it is important to apprehend the frequency and distribution of tweets across these dates. This is best visualized with a line graph created with Tableau, which charts the number of occurrences of #HB25 by day, coded by the type of tweet.
Figure 11: Line graph depicting frequency of tweets containing #HB25 posted from October 11, 2021 to October 28, 2021. Visualization created in Tableau.
Figure 12: Word analysis of tweets containing #HB25 posted from October 11, 2021 to October 28, 2021. Visualization created in Tableau.
The resulting visualization shows that most of the #HB25 tweets were retweets that occurred around the time that the Texas legislature was voting on House Bill 25. Original tweets were not a significant part of the volume of #HB25, and we could surmise from the word analyses that many of the retweets and mentions were made in protest to the bill. Additionally, the volume of tweets created during the legislative voting sessions overwhelms the number of tweets surrounding Governor Abbott’s signing of the bill. This means that when the data sets are combined, we lose sight of the charged language used to describe this new Texas law. Dighton notes that this method of focused attention on smaller sections of data (or, spaces of strategic contemplation) is essential to a more thorough engagement with our research. She argues that scholars should allow “small data to inform large data analysis,” explaining, “Rather than leading with large-scale pattern analysis and deriving outcomes from algorithmic analysis, the more embodied, situated accounts in our small data analysis could become the orientation guiding the analysis of the whole” (“Conclusion”). Small data here allows the sentiments of #HB25 to emerge.
#SaveWomensSports
As the aim of this project is to use social media data and data visualization to build an understanding of anti-trans encounters with “saving” women’s sports, I explore the use of #SaveWomensSports––with its connotations of anti-trans sentiment––during the legislative process of Texas House Bill 25. Similar to my process for #HB25, I performed a Tweet search on NodeXL on October 20, 2021 and October 28, 2021. I first evaluated these data sets separately, but I did not find any significant differences in the data; therefore, I combined the data sets in order to analyze how the hashtag #SaveWomensSports interacted with the Twitter environment during the legislative process for the Texas law. The result is a Tableau dashboard featuring three visualizations: a hashtag cloud, a line graph representing tweet frequency over time, and a bar graph depicting top content creators and content type.
Figure 13: Tableau Dashboard visualizing the use of #SaveWomensSports in tweets posted from October 11, 2021 to October 28, 2021.
The #SaveWomensSports visualizations provide key insights into the types of conversations occurring as well as the ways in which those conversations occur. For example, the hashtag cloud, which represents the top 10 hashtags used in tweets with #SaveWomensSports over the time period, shows that people who tagged #SaveWomensSports were involved in conversations about the Texas law (#txlege, #texas, #texasvalues, #hb25). Conversations also included the anti-trans hashtag #sexnotgender as well as other calls for “saving” women’s sports: #savegirlssports and #fairplayforwomen. Nonetheless, the use and frequency of these hashtags must be put into context. By categorizing tweets into “original posts” (including tweets, mentions, and replies––all actions in which the user creates original content) and “retweets” (including retweets and mentions in replies), we begin to observe that many posts are simply recycled content that amplifies the original message. Further, the top content creators (or “tweeters”) are mostly tweet amplifiers.[2] Even @SaveWomensSport posted less original content than retweets over the time period.
Strategic Contemplation: “Saving” Women’s Sports Amidst Anti-Trans Legislation
The NodeXL data obtained during the month of October 2021 allowed me to dwell within the research process as I engaged with the anti-trans encounters in the concept of “saving” women’s sports. The circumstances of the passing of significant legislation during the data collection period enriched the research I could do, but it also left me wondering how it is that trans support and allyship is so prominent online while in the real-world state and local governments entertain (and pass) anti-trans legislation. Where are those conversations? Perhaps those conversations are not happening on Twitter. Perhaps they are happening in person, within groups that hold power and sway in state and local government. Perhaps they are happening more in legislative chambers than on social media. Or perhaps they are happening on sites like Parlor––sites that allow the dehumanizing language that often coincides with anti-trans sentiment. It is important to note that the push for “saving” women’s sports is a small subset of the agenda for trans-exclusionary radical feminists and people who identify as gender critical, a cozy way of expressing anti-trans sentiment.
By allowing small data to inform large data analysis, as Dighton urges, I am able to engage with a situated account of the conversations surrounding the exclusion of transgender girls and women from competing in women’s and girls’ sports. Working within and through the affordances of technology, from NodeXL in the data-gathering phase to Tableau in the analysis phase, the complex messiness of social media becomes a bit clearer. These moves are not without bias; indeed, no data is neutral. From the timing of the data pulls to the different ways of analyzing the data (for example, my choice to analyze the two #HB25 data pulls separately), decisions made in the research process directly affect analysis, interpretation, and conclusions reached in this project. While I have been unsuccessful in understanding the full force of “saving” women’s sports as these conversations unfold on Twitter, the task of taking stock of conversations and ideas as they circulate helps to reveal the spaces and the cases where individuals and organizations deploy the concept of “saving” within the bounds of the legislative process. As anti-trans legislation continues to emerge in states throughout the nation, tracing social media conversations through data visualization can shed light on how these conversations move, where they are occurring, and how prevalent they actually are.
The methodological move I model here––isolating the data and zooming in to a subject of interest––calls attention to the fact that “what is seen is made” (Drucker, 2014, p. 57). Johanna Drucker considers the ethical implications of such visual representations, arguing that visualizations conceal the contingency and constructedness of data. Yet, she asserts, “the interpretive and empirical need not exclude each other” (p. 196). In fact, if we interpret visualizations as “the constructed expression of perceived phenomena” (p. 130), we can recognize our role in shaping research outcomes. Many have argued that researchers must admit to this power, feminist scholars in particular (see, for example, D’Ignazio & Klein, 2020; Fancher, 2021; Hutchinson Campos & Novotny, 2021). Beyond this recognition, we must also understand that visualizations as constructed expressions open the possibility to make new connections and reframe our thoughts. Perhaps research in this realm can expose disparities between the perceived popularity of conservative legislative agendas and actual public opinion, as in the cases of abortion access or cannabis legality. The corners of the internet are notoriously radical; nonetheless, digging into, dwelling in, and visualizing the data allows researchers to contemplate connections between online conversations and real-world happenings.
References
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Michelob ULTRA. (2021, August 26). Michelob ULTRA | Save it, see it. https://www.youtube.com/watch?v=DEbwRoFILmk
Michelob ULTRA. (2021, August 26). Michelob ULTRA commits $100 million to support gender equality in sports. PR Newswire. https://www.prnewswire.com/news-releases/michelob-ultra-commits-100-million-to-support-gender-equality-in-sports-301363513.html
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Texas H.B. No. 25. (2021). Retrieved from https://capitol.texas.gov/tlodocs/873/billtext/html/HB00025I.htm
[1] As of April 4, 2023, Twitter (now known as “X”) revoked Social Bearing’s access of the Twitter API, thus rendering the tool effectively useless.
[2] One exception is the top content creator, @nievessebastia1, who is a prolific Twitter user from Madrid. I was concerned that this user might be a bot, but the Botometer from Bellingcat Toolkit assured me that @nievessebastia1 is a real person who uses Twitter almost exclusively on the weekends. The user includes #SaveWomensSports and the dinosaur emoji (trans-exclusionary radical feminists often use the dinosaur emoji to identify themselves) in their profile, and tweets often about the movement for trans laws in Spain and around the globe.
Haley Swartz (she/her) is a PhD candidate in Rhetorics, Communication and Information Design at Clemson University. She is a writing, media, and communication educator with experience in secondary and higher education. Her dissertation research focuses on gender, technology, and health literacies, and she has published original research in Women’s Studies in Communication and Peitho.