Emoji and Self-Identity in Twitter Bios

Longinos, Giorgos; Wilson, Steven R.; Magdy, Walid

doi:10.18653/v1/2020.nlpcss-1.22

Cited by 11 publications

(10 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By manually annotating Twitter accounts for skin tone based on profile photos, Robertson et al [74] found that users, regardless of geographical location, generally employ skin-toned emoji which match their profile photo skin tone, strongly suggesting a self-representational function in production. The use of particular emoji in Twitter bios has been shown to closely track personal traits and tastes, which are shared across groups of users [56]. Meanwhile, studies of how emoji are interpreted have focused more on their semantics and pragmatics than their social signaling function, but have nevertheless found differences in interpretation across social groups.…”

Section: Self-representation Social Media and Emojimentioning

confidence: 99%

Black or White but Never Neutral: How Readers Perceive Identity from Yellow or Skin-toned Emoji

Roberston

Magdy

Goldwater

2021

Proc. ACM Hum.-Comput. Interact.

Self Cite

View full text Add to dashboard Cite

Research in sociology and linguistics shows that people use language not only to express their own identity but to understand the identity of others. Recent work established a connection between expression of identity and emoji usage on social media, through use of emoji skin tone modifiers. Motivated by that finding, this work asks if, as with language, readers are sensitive to such acts of self-expression and use them to understand the identity of authors. In behavioral experiments (n=488), where text and emoji content of social media posts were carefully controlled before being presented to participants, we find in the affirmative-emoji are a salient signal of author identity. That signal is distinct from, and complementary to, the one encoded in language. Participant groups (based on self-identified ethnicity) showed no differences in how they perceive this signal, except in the case of the default yellow emoji. While both groups associate this with a White identity, the effect was stronger in White participants. Our finding that emoji can index social variables will have experimental applications for researchers but also implications for designers: supposedly "neutral" defaults may be more representative of some users than others. CCS Concepts: • Human-centered computing → Empirical studies in HCI; • Applied computing → Sociology;

show abstract

Section: Self-representation Social Media and Emojimentioning

confidence: 99%

Black or White but Never Neutral: How Readers Perceive Identity from Yellow or Skin-toned Emoji

Roberston

Magdy

Goldwater

2021

Proc. ACM Hum.-Comput. Interact.

Self Cite

View full text Add to dashboard Cite

show abstract

“…One of these social media platforms, Twitter limits its users to just 280 characters to express themselves. In this case, the fact that emojis are able to have the meaning of a sentence beyond words in some usages, increase the impact of emotions and thoughts, and make those which are intended to be expressed more powerful has caused users to prefer emojis more frequently (Jaeger, S. R., & Ares, 2017;Li et al, 2020). It is found that at times the clubs want to increase the impact of emotions and thoughts that are meant to be conveyed by using multiple emoji in one tweet.…”

Section: Discussionmentioning

confidence: 99%

“…The sports clubs competing in major football leagues (e.g. England Premier League, Bundesliga, La Liga) include emojis that have a common meaning and are accepted by everyone in their posts to reach their fans who speak different languages in different countries-continents (Li et al, 2020). A study conducted on the use of Twitter by sports clubs in Germany found that sports clubs often use the Twitter platform for three different purposes: providing and distributing information directly to and communicating with their fans (Oelrichs, 2020).…”

Section: Introductionmentioning

confidence: 99%

Common Language of New Era in Sport Clubs: Emojis

Yüce

Aydoğdu

Katırcı

2021

JTM

View full text Add to dashboard Cite

Defined as an easy and automated way of expressing emotions in the digital age, emojis are emerging as a new language in the social media world and sports clubs also. For sports clubs, it is of vital importance to communicate and establish effective relations with fans or followers. Hence, almost all professional sports clubs use social media and shape their social media accounts to interact with fans/followers and effectively maintain marketing communication efforts. The aim of this study was to determine the content of emoji usage in tweets of Turkish sports clubs (Besiktas JK, Fenerbahce SK, Galatasaray SK, Istanbul Başakşehir FK, Trabzonspor SK). Since Twitter is one of the most heavily used social media networks of sports clubs, so in this study was preferred. Content analysis method was used to examine emojis used by sports clubs. The study found that sports clubs use emojis that create positive and neutral connotations. Emojis used are heavily determined to be visuals depicting the colors and symbols of sports clubs. The study is the first to examine sports clubs' emojis used. Hence, the study included important results for the management of communication and marketing strategies of sports clubs on social media.

show abstract

“…For this, we follow the approach in Sharma, Ferrara, and Liu (2022) and apply a keyword list to a user's bio in order to classify users. The theoretical reasoning behind this approach is that the user bios are supposed to reflect the personal and social identity of a user (Li et al 2020;Pathak, Madani, and Joseph 2021;Rogers and Jones 2021). As such, user bios are predictive of one's identity, including gender (Burger et al 2011;Pathak, Madani, and Joseph 2021), personal interests (Ding and Jiang 2014), and political orientation (Pathak, Madani, and Joseph 2021;Rogers and Jones 2021), while we here adapt the approach to supporters of conspiracy theories.…”

Section: Identifying Qanon Supportersmentioning

confidence: 99%

Finding Qs: Profiling QAnon Supporters on Parler

Bär¹,

Pröllochs²,

Feuerriegel³

2022

Preprint

View full text Add to dashboard Cite

The social media platform "Parler" has emerged into a prominent fringe community where a significant part of the user base are self-reported supporters of QAnon, a far-right conspiracy theory alleging that a cabal of elites controls global politics. QAnon is considered to have had an influential role in the public discourse during the 2020 U.S. presidential election. However, little is known about QAnon supporters on Parler and what sets them aside from other users. Building up on social identity theory, we aim at profiling the characteristics of QAnon supporters on Parler. We analyze a large-scale dataset with more than 600,000 profiles of English-speaking users on Parler. Based on users' profiles, posts, and comments, we then extract a comprehensive set of user features, linguistic features, network features, and content features. This allows us to perform user profiling and understand to what extent these features discriminate between QAnon and non-QAnon supporters on Parler. Our analysis is three-fold:(1) We quantify the number of QAnon supporters on Parler, finding that 34,913 users (5.5 % of all users) openly report to support the conspiracy. (2) We examine differences between QAnon vs. non-QAnon supporters. We find that QAnon supporters differ statistically significantly from non-QAnon supporters across multiple dimensions. For example, they have, on average, a larger number of followers, followees, and posts, and thus have a large impact on the Parler network.(3) We use machine learning to identify which user characteristics discriminate QAnon from non-QAnon supporters. We find that user features, linguistic features, network features, and content features, can -to a large extent -discriminate QAnon vs. non-QAnon supporters on Parler. In particular, we find that user features are highly discriminatory, followed by content features and linguistic features.

show abstract

Emoji and Self-Identity in Twitter Bios

Cited by 11 publications

References 19 publications

Black or White but Never Neutral: How Readers Perceive Identity from Yellow or Skin-toned Emoji

Black or White but Never Neutral: How Readers Perceive Identity from Yellow or Skin-toned Emoji

Common Language of New Era in Sport Clubs: Emojis

Finding Qs: Profiling QAnon Supporters on Parler

Contact Info

Product

Resources

About