Self-Representation on Twitter Using Emoji Skin Color Modifiers

Since 2015, it has been possible to modify certain emoji with a skin tone. The five different skin tones were introduced with the aim of representing more human diversity, but some commentators feared they might be used as a way to negatively represent other users/groups. This paper presents a quantitative analysis of the use of skin tone modifiers on emoji on Twitter, showing that users with darker-skinned profile photos employ them more often than users with lighter-skinned profile photos, and the vast majority of skin tone usage matches the color of a user's profile photo - i.e., tones represent the self, rather than the other. In the few cases where users do use opposite-toned emoji, we find no evidence of negative racial sentiment. Thus, the introduction of skin tones seems to have met the goal of better representing human diversity.Comment: Accepted in ICWSM 201

Identity Signals in Emoji do not Influence Perception of Factual Truth on Twitter

Prior work has shown that Twitter users use skin-toned emoji as an act of self-representation to express their racial/ethnic identity. We test whether this signal of identity can influence readers' perceptions about the content of a post containing that signal. In a large scale (n=944) pre-registered controlled experiment, we manipulate the presence of skin-toned emoji and profile photos in a task where readers rate obscure trivia facts (presented as tweets) as true or false. Using a Bayesian statistical analysis, we find that neither emoji nor profile photo has an effect on how readers rate these facts. This result will be of some comfort to anyone concerned about the manipulation of online users through the crafting of fake profiles

“One part politics, one part technology, one part history”:Racial representation in the Unicode 7.0 emoji set

Emoji are miniature pictographs that have taken over text messages, emails, and Tweets worldwide. Although contemporary emoji represent a variety of races, genders, and sexual orientations, the original emoji set came under fire for its racial homogeneity: minus two “ethnic” characters, the people emoji featured in Unicode 7.0 were represented as White. This article investigates the set of circumstances that gave rise to this state of affairs, and explores the implications for users of color whose full participation in the emoji phenomenon is constrained by their exclusion. This project reveals that the lack of racial representation within the emoji set is the result of colorblind racism as evidenced through two related factors: aversion to, and avoidance of, the politics of technical systems and a refusal to recognize that the racial homogeneity of the original emoji set was problematic in the first place

Examining Own-Race Bias: A Cooperation and Memory Study Using Diverse Emojis

Other-race-effect or own-race bias is a well-documented phenomenon in memory. Findings suggest that humans are better at recognizing and remembering faces of their own race than other races. Previous research suggests that these results are due to a lack of interracial contact or exposure to other racial groups. Evidence from previous studies has demonstrated that individuals process own-race faces differently than other-race faces, paying more attention to more salient features that become better encoded. While there is empirical support for both hypotheses, it has yet to be studied if the other-race effect for memory extends to representational human faces, for instance, emojis. Emojis are digital pictures used for electronic communication of emotions, expressions, and meaning. The current study examined if the other-race effect for recognition memory extended to people emojis. Black (n = 47) and White (n = 47) participants viewed both light/medium-light skin tone and dark/medium-dark skin tone emojis. Participants completed a cooperation task and a memory computer task. Results indicated that there was no difference in memory or cooperation for same-race or other-race faces. However, Black participants that held their racial identity in more positive regard were marginally more likely to remember dark and medium-dark emoji faces. Additionally, Black participants that were more satisfied with their skin color were significantly more likely to remember dark and medium-dark emoji faces. Overall, participants cooperated significantly more with emoji faces than human faces. White participants higher in empathy were marginally more likely to cooperate with Black and dark/medium-dark partners than those lower in empathy. These results suggest that individual differences can moderate own-race bias even for emoji faces

Emojional: Emoji Embeddings

Emojis are intended to illustrate or replace words in natural language. Although they are understandable across linguistic barriers, their implications are ever-evolving, and change depending on who we are talking to, and when. The underlying emotion is key. This is evident in language online, highlighted recently by the rise of cancel culture via online shaming. For example, the use of the clown emoji to signify someone is making a fool of themself, or the collective spamming of the snake emoji to “cancel” someone, both show seemingly innocent emojis being used as clear forms of aggression online. To capture these nuances, we have created novel emoji embeddings trained on their emotional content. The subsequent emoji embeddings are generally more accurate than the state-of-the-art embeddings on the task of sentiment analysis. These embeddings can be found in our GitHub repository: https://github.com/elenabarry/emojional

TikTok, Twitter, and Platform-Specific Technocultural Discourse in Response to Taylor Swift’s LGBTQ+ Allyship in ‘You Need to Calm Down’

For most of her career thus far, Taylor Swift’s cultural outputs have remained apolitical, often addressing heteronormative notions of romance, young adult life, and heartbreak. In 2019, Swift broke her politicised silence with ‘You Need to Calm Down’, a track which self-proclaims the artist as an ally to LGBTQ+ communities through her co-option of language historically used to silence marginalised voices, and the inclusion of LGBTQ+-identified celebrities in the accompanying music video. Through a critical technocultural discourse analysis (CTDA) approach, and incorporating digital ethnography, this article examines and compares the multimodal response to ‘You Need to Calm Down’ on TikTok and Twitter. CTDA multimodal analysis is utilised as a method to ascertain both the cultural situatedness of the track, its reception through digital spaces, and also how that reception is connected to the conventions of each platform. Through an analysis of over 20,000 tweets utilising the #YouNeedToCalmDown hashtag, and over 100 TikTok videos based on the track, I examine platform-specific discourse: the de-politicised mimetic creativity of TikTok in comparison to the more hegemonic interpretations found on Twitter. Discussion is organised around three themes of response to ‘You Need to Calm Down’: online communities and ambient affiliations, performative allyship, and cancel culture