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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. 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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3. Decoding the New World Language: Analyzing the Popularity, Roles, and Utility of Emojis

   https://doi.org/10.1145/3308560.3316541  

   Mei, Qiaozhu (January 2019, WWW '19 Companion: Companion Proceedings of the 2019 World Wide Web Conference)

   Emojis have quickly become a universal language that is used by worldwide users, for everyday tasks, across language barriers, and in different apps and platforms. The prevalence of emojis has quickly attracted great attentions from various research communities such as natural language processing, Web mining, ubiquitous computing, and human-computer interaction, as well as other disciplines including social science, arts, psychology, and linguistics. This talk summarizes the recent efforts made by my research group and our collaborators on analyzing large-scale emoji data. 

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4. #lets-discuss: Analyzing student affect in course forums using emoji.

   Blobstein, A.; Gal, K.; Karger, D.; Facciotti, M.; Kim, H.; Almahmoud, J.; Sripathi, K. (July 2022, Proceedings of the 15th International Conference on Educational Data Mining)

   Mitrovic, A.; Bosch, N. (Ed.)

   Emoji are commonly used in social media to convey attitudes and emotions. While popular, their use in educational contexts has been sparsely studied. This paper reports on the students’ use of emoji in an online course forum in which students annotate and discuss course material in the margins of the online textbook. For this study, instructors created 11 custom emoji-hashtag pairs that enabled students to quickly communicate affects and reactions in the forum that they experienced while interacting with the course material. Example reporting includes, inviting discussion about a topic, declaring a topic as interesting, or requesting assistance about a topic. We analyze emoji usage by over 1,800 students enrolled in multiple offerings of the same course across multiple academic terms. The data show that some emoji frequently appear together in posts associated with the same paragraphs, suggesting that students use the emoji in this way to communicating complex affective states. We explore the use of computational models for predicting emoji at the post level, even when posts are lacking emoji. This capability can allow instructors to infer information about students’ affective states during their ”at home” interactions with course readings. Finally, we show that partitioning the emoji into distinct groups, rather than trying to predict individual emoji, can be both of pedagogical value to instructors and improve the predictive performance of our approach using the BERT language model. Our procedure can be generalized to other courses and for the benefit of other instructors. 

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5. Classification of Health-Related Social Media Posts: Evaluation of Post Content–Classifier Models and Analysis of User Demographics

   https://doi.org/10.2196/14952  

   Rivas, Ryan; Sadah, Shouq A; Guo, Yuhang; Hristidis, Vagelis (January 2020, JMIR Public Health and Surveillance)

   Background The increasing volume of health-related social media activity, where users connect, collaborate, and engage, has increased the significance of analyzing how people use health-related social media. Objective The aim of this study was to classify the content (eg, posts that share experiences and seek support) of users who write health-related social media posts and study the effect of user demographics on post content. Methods We analyzed two different types of health-related social media: (1) health-related online forums—WebMD and DailyStrength—and (2) general online social networks—Twitter and Google+. We identified several categories of post content and built classifiers to automatically detect these categories. These classifiers were used to study the distribution of categories for various demographic groups. Results We achieved an accuracy of at least 84% and a balanced accuracy of at least 0.81 for half of the post content categories in our experiments. In addition, 70.04% (4741/6769) of posts by male WebMD users asked for advice, and male users’ WebMD posts were more likely to ask for medical advice than female users’ posts. The majority of posts on DailyStrength shared experiences, regardless of the gender, age group, or location of their authors. Furthermore, health-related posts on Twitter and Google+ were used to share experiences less frequently than posts on WebMD and DailyStrength. Conclusions We studied and analyzed the content of health-related social media posts. Our results can guide health advocates and researchers to better target patient populations based on the application type. Given a research question or an outreach goal, our results can be used to choose the best online forums to answer the question or disseminate a message. 

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