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1. Emoji use in tweets: Relationships with personality traits and word usage

   Kennison, S; Hurtado_Morales, A; Fritz, K; Chan-Tin, E (February 2024, n/a)

   na (Ed.)

   Emojis are digital pictographs used to express ideas and emotions. There are thousands of emojis, some depicting faces with expressions and others depicting objects, animals, and activities. We examined how emoji use on Twitter was related to users’ language use and Big Five personality traits. In the present research, we examined emoji use in tweets for seventy-six twitter accounts belonging to undergraduate students (34 women, 22 men) who also completed an online survey in which they reported personal characteristics including personality traits. With their consent, we retrieved tweets from accounts using the Twitter API and python script. We also analyzed word usage in tweets using the Linguistic Inquiry Word Count (LIWC). The results showed that frequent use of emojis was related to lower levels of openness, but not other Big Five traits. More frequent use of emojis was related to more frequent use of word related to: tone, positive emotion, sad, affect, feel, you pronouns, family, and the body. More frequent use of emojis was related to less frequent use of articles and words related to insight, money, risk, anger, sexual, ingest, informal, and swear words. 

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2. Emoji use in social media posts: relationships with personality traits and word usage

   https://doi.org/10.3389/fpsyg.2024.1343022  

   Kennison, S; Fritz, K; Hurtado_Morales, M; Chan-Tin, E (February 2024, Frontiers in Psychology)

   Al-Nofaie, H (Ed.)

   Prior research has demonstrated relationships between personality traits of social media users and the language used in their posts. Few studies have examined whether there are relationships between personality traits of users and how they use emojis in their social media posts. Emojis are digital pictographs used to express ideas and emotions. There are thousands of emojis, which depict faces with expressions, objects, animals, and activities. We conducted a study with two samples (n = 76 andn = 245) in which we examined how emoji use on X (formerly Twitter) related to users’ personality traits and language use in posts. Personality traits were assessed from participants in an online survey. With participants’ consent, we analyzed word usage in posts. Word frequencies were calculated using the Linguistic Inquiry Word Count (LIWC). In both samples, the results showed that those who used the most emojis had the lowest levels of openness to experience. Emoji use was unrelated to the other personality traits. In sample 1, emoji use was also related to use of words related to family, positive emotion, and sadness and less frequent use of articles and words related to insight. In sample 2, more frequent use of emojis in posts was related to more frequent use ofyoupronouns,Ipronouns, and more frequent use of negative function words and words related to time. The results support the view that social media users’ characteristics may be gleaned from the content of their social media posts. 

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3. Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification

   https://doi.org/10.1145/3308558.3313600  

   Chen, Zhenpeng; Shen, Sheng; Hu, Ziniu; Lu, Xuan; Mei, Qiaozhu; Liu, Xuanzhe (January 2019, WWW '19: The World Wide Web Conference)

   Sentiment classification typically relies on a large amount of labeled data. In practice, the availability of labels is highly imbalanced among different languages, e.g., more English texts are labeled than texts in any other languages, which creates a considerable inequality in the quality of related information services received by users speaking different languages. To tackle this problem, cross-lingual sentiment classification approaches aim to transfer knowledge learned from one language that has abundant labeled examples (i.e., the source language, usually English) to another language with fewer labels (i.e., the target language). The source and the target languages are usually bridged through off-the-shelf machine translation tools. Through such a channel, cross-language sentiment patterns can be successfully learned from English and transferred into the target languages. This approach, however, often fails to capture sentiment knowledge specific to the target language, and thus compromises the accuracy of the downstream classification task. In this paper, we employ emojis, which are widely available in many languages, as a new channel to learn both the cross-language and the language-specific sentiment patterns. We propose a novel representation learning method that uses emoji prediction as an instrument to learn respective sentiment-aware representations for each language. The learned representations are then integrated to facilitate cross-lingual sentiment classification. The proposed method demonstrates state-of-the-art performance on benchmark datasets, which is sustained even when sentiment labels are scarce. 

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4. Emojis predict dropouts of remote workers: An empirical study of emoji usage on GitHub

   https://doi.org/10.1371/journal.pone.0261262  

   Lu, Xuan; Ai, Wei; Chen, Zhenpeng; Cao, Yanbin; Mei, Qiaozhu (January 2022, PLOS ONE)

   Danforth, Christopher M. (Ed.)

   Emotions at work have long been identified as critical signals of work motivations, status, and attitudes, and as predictors of various work-related outcomes. When more and more employees work remotely, these emotional signals of workers become harder to observe through daily, face-to-face communications. The use of online platforms to communicate and collaborate at work provides an alternative channel to monitor the emotions of workers. This paper studies how emojis, as non-verbal cues in online communications, can be used for such purposes and how the emotional signals in emoji usage can be used to predict future behavior of workers. In particular, we present how the developers on GitHub use emojis in their work-related activities. We show that developers have diverse patterns of emoji usage, which can be related to their working status including activity levels, types of work, types of communications, time management, and other behavioral patterns. Developers who use emojis in their posts are significantly less likely to dropout from the online work platform. Surprisingly, solely using emoji usage as features, standard machine learning models can predict future dropouts of developers at a satisfactory accuracy. Features related to the general use and the emotions of emojis appear to be important factors, while they do not rule out paths through other purposes of emoji use. 

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5. Exploring an Affective and Responsive Virtual Environment to Improve Remote Learning

   https://doi.org/10.3390/virtualworlds2010004  

   Qi, Jianing; Tang, Hao; Zhu, Zhigang (March 2023, Virtual Worlds)

   Online classes are typically conducted by using video conferencing software such as Zoom, Microsoft Teams, and Google Meet. Research has identified drawbacks of online learning, such as “Zoom fatigue”, characterized by distractions and lack of engagement. This study presents the CUNY Affective and Responsive Virtual Environment (CARVE) Hub, a novel virtual reality hub that uses a facial emotion classification model to generate emojis for affective and informal responsive interaction in a 3D virtual classroom setting. A web-based machine learning model is employed for facial emotion classification, enabling students to communicate four basic emotions live through automated web camera capture in a virtual classroom without activating their cameras. The experiment is conducted in undergraduate classes on both Zoom and CARVE, and the results of a survey indicate that students have a positive perception of interactions in the proposed virtual classroom compared with Zoom. Correlations between automated emojis and interactions are also observed. This study discusses potential explanations for the improved interactions, including a decrease in pressure on students when they are not showing faces. In addition, video panels in traditional remote classrooms may be useful for communication but not for interaction. Students favor features in virtual reality, such as spatial audio and the ability to move around, with collaboration being identified as the most helpful feature. 

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