Sentiment of EmojiSets: How Emoji Sequences Improve Sentiment Cognition

The capability to infer emotional insights from emojis found in social media has projected emoji analysis into the spotlight of current emoji-based research. Previous studies mainly used text-surrounding emojis to estimate sentimentality scores. However, trying to conclude the same score based solely on emojis is challenging. In this paper this challenge was welcomed, and with it we created a new concept. This revolutionary scoring method, named the EmojiSets Sentiment Score Rank, proposes using sets of emojis taken from tweets along with information from previous studies [1] to find a sentiment score. This bottom-up scoring approach gives each emoji a sentiment score. It then calculates the context-level sentiment score of a tweet solely dependent on the emojis found within it. To the best of the authors' knowledge, no such approach has been researched in the Emojis Sentiment Analysis area. We tested our model against over 1.2 million tweets concerning Covid-19 and compared it to the VADER model [7] to validate our assumption. Our model corrected around 72% of the tweets that the other model scored as neutral. To succor these findings, 32 human annotators were given the task of annotating 8040 randomly chosen tweets. When calculating similarity using the Jaccard Index, their results were consistent with our approach in over 70% of cases © 2022 IEEE.

COVID-19 case prediction using emotion trends via Twitter emoji analysis: A case study in Japan.

Introduction: The worldwide COVID-19 pandemic, which began in December 2019 and has lasted for almost 3 years now, has undergone many changes and has changed public perceptions and attitudes. Various systems for predicting the progression of the pandemic have been developed to help assess the risk of COVID-19 spreading. In a case study in Japan, we attempt to determine whether the trend of emotions toward COVID-19 expressed on social media, specifically Twitter, can be used to enhance COVID-19 case prediction system performance. Methods: We use emoji as a proxy to shallowly capture the trend in emotion expression on Twitter. Two aspects of emoji are studied: the surface trend in emoji usage by using the tweet count and the structural interaction of emoji by using an anomalous score. Results: Our experimental results show that utilizing emoji improved system performance in the majority of evaluations.

The use of emoji to establish student wellbeing: does the image reflect the reality?

Introduction: The impact of COVID-19 social restrictions on mental wellbeing of health professional students during placement is largely unknown. Conventional survey methods do not capture emotional fluctuations. Increasing use of smartphones suggests short message service (SMS) functionality could provide easy, rapid data. This project tested the feasibility and validity of gathering data on Therapeutic Radiography student mental wellbeing during clinical placement via emoji and SMS. Methods: Participants provided anonymous daily emoji responses via WhatsApp to a dedicated mobile phone. Additional weekly prompts sought textual responses indicating factors impacting on wellbeing. A short anonymous online survey validated responses and provided feedback on the method. Results: Participants (n = 15) provided 254 daily responses using 108 different emoji;these triangulated with weekly textual responses. Feedback concerning the method was positive. 'Happy' emoji were used most frequently;social interaction and fatigue were important wellbeing factors. Anonymity and opportunity to feedback via SMS were received positively;ease and rapidity of response engendered engagement throughout the 3-week study. Conclusions: The use of emoji for rapid assessment of cohort mental wellbeing is valid and potentially useful alongside more formal evaluation and support strategies. Capturing simple wellbeing responses from a cohort may facilitate the organisation of timely support interventions. © The Author(s), 2022. Published by Cambridge University Press.

Health persuasion through emoji: How emoji interacted with information source to predict health behaviors in COVID-19 situation.

By providing health information through visual communication, public health organizations can effectively guide and persuade people to adopt healthy behaviors, which is critical in the context of public health crises. In this study, drawing upon congruity theory and the premise of visual communication, we examined how information source and emoji may shape people's preventive and self-protective behaviors through perceived fear (PF) and perceived controllability (PC). Using a convenience sample of 210 participants, we conducted a 2 (emoji: with versus without) × 2 (information source: official versus unofficial) between-subject experiment. The results indicated that, compared with nonuse, the use of emoji in information resulted in higher PF, stronger preventive behavioral intention (PBI), and lower PC. In addition, a strong interaction effect was observed between emoji and the source of information on PBI. When emoji were added to health information released by an unofficial organization, the text outperformed that from an official agency in persuading people to adopt preventive behaviors. Furthermore, we determined that PF mediated the effect of emoji on PBI, but only for unofficial information sources. These results provide a reference for enhancing the effectiveness of health information including visual cues, such as emoji.

The use of emoji to establish student wellbeing: does the image reflect the reality?

Introduction:The impact of COVID-19 social restrictions on mental wellbeing of health professional students during placement is largely unknown. Conventional survey methods do not capture emotional fluctuations. Increasing use of smartphones suggests short message service (SMS) functionality could provide easy, rapid data. This project tested the feasibility and validity of gathering data on Therapeutic Radiography student mental wellbeing during clinical placement via emoji and SMS.Methods:Participants provided anonymous daily emoji responses via WhatsApp to a dedicated mobile phone. Additional weekly prompts sought textual responses indicating factors impacting on wellbeing. A short anonymous online survey validated responses and provided feedback on the method.Results:Participants (n = 15) provided 254 daily responses using 108 different emoji;these triangulated with weekly textual responses. Feedback concerning the method was positive. ‘Happy’ emoji were used most frequently;social interaction and fatigue were important wellbeing factors. Anonymity and opportunity to feedback via SMS were received positively;ease and rapidity of response engendered engagement throughout the 3-week study.Conclusions:The use of emoji for rapid assessment of cohort mental wellbeing is valid and potentially useful alongside more formal evaluation and support strategies. Capturing simple wellbeing responses from a cohort may facilitate the organisation of timely support interventions.

Are emojis ready to promote the WHO 5 moments for hand hygiene in healthcare?

BACKGROUND: Hand hygiene is universally recognized as a cornerstone measure for the prevention of healthcare-associated infections. Although the WHO "My five Moments for hand hygiene" poster has been used for more than a decade to delineate hand hygiene indications and promote action, adherence levels among healthcare workers are still notoriously low and disquieting. To compensate for the lack of effective hand hygiene communication, we aimed to evaluate emojis as possible surrogates for the non-verbal aspects of hand hygiene behaviour. METHODS: Following a thorough review of the Unicode version 12.0, the most applicable emojis to the terms used in the WHO 5 Moments poster were extracted. We developed a self-administered questionnaire to assess the view of infection prevention and control (IPC) practitioners regarding the use of emojis to show the WHO 5 Moments. Completed questionnaires were collected and analysed to determine the suitability of the existing emojis to illustrate a unified emoji poster. Data were analysed using R (version 3.6.3). RESULTS: A total of 95 IPC practitioners completed the questionnaire from May to October 2019 from different countries. Of these, 69 (74%) were female, and the mean age of the participants was 44.6 ± 10.87 years. We found appropriate emojis for six of the words used in the poster, including for touching (72%), for patient (63%), for clean (53%), for procedure (56%), for body fluid (58%), and for exposure risk (71%). The existing emojis proposed for the words "hygiene", "aseptic", and "surrounding" seemed to be less satisfactory. CONCLUSIONS: In summary, the findings of this study indicate that the existing emojis may not be able to substitute the words used in the WHO 5 Moments poster. Emojis might be helpful to address hand hygiene indications in healthcare that may eventually play a role in promoting this measure. However, emojis should be further studied to choose the most appropriate ones and avoid ambiguity and misinterpretation. More emojis to convey health related messages are needed. We recommend further research in this area to evaluate the effect of using emojis in healthcare-related behaviours.

Emoji use in China: popularity patterns and changes due to COVID-19.

Emojis are small pictograms that are frequently embedded within micro-texts to more directly express emotional meanings. To understand the changes in the emoji usage of internet users during the COVID-19 outbreak, we analysed a large dataset collected from Weibo, the most popular Twitter-like social media platform in China, from December 1, 2019, to March 20, 2020. The data contained 38,183,194 microblog posts published by 2,239,472 unique users in Wuhan. We calculated the basic statistics of users' usage of emojis, topics, and sentiments and analysed the temporal patterns of emoji occurrence. After examining the emoji co-occurrence structure, we finally explored other factors that may affect individual emoji usage. We found that the COVID-19 outbreak greatly changed the pattern of emoji usage; i.e., both the proportion of posts containing emojis and the ratio of users using emojis declined substantially, while the number of posts remained the same. The daily proportion of Happy emojis significantly declined to approximately 32%, but the proportions of Sad- and Encouraging-related emojis rose to 24% and 34%, respectively. Despite a significant decrease in the number of nodes and edges in the emoji co-occurrence network, the average degree of the network increased from 34 to 39.8, indicating that the diversity of emoji usage increased. Most interestingly, we found that male users were more inclined towards using regular textual language with fewer emojis after the pandemic, suggesting that during public crises, male groups appeared to control their emotional display. In summary, the COVID-19 pandemic remarkably impacted individual sentiments, and the normal pattern of emoji usage tends to change significantly following a public emergency. Supplementary Information: The online version contains supplementary material available at 10.1007/s10489-022-03195-y.

Covid-19 WhatsApp sticker memes in Oman

In this paper, the form and function of personalized Covid-19 WhatsApp sticker memes created and shared as social laments by citizens in Oman are examined. The compiled data set of 288 WhatsApp stickers was taken from a larger ethnographic project on Arabs and Covid-19. To collect and analyze the data, perspectives from visual semiotics were integrated with participatory and geosemiotic approaches to ground the stickers socially and globally. Six functions of Covid-19 WhatsApp stickers in Oman were identified: expressing political dissent, creating public signs, promoting religious agenda, indexing frustration, expressing levity, and constructing counter-discourse. Based on this analysis, it is suggested that by creating and using WhatsApp stickers during the 2020-2021 Covid-19 pandemic, Omani citizens positioned themselves as agentive participants in charge of their own lives, thus, solidifying a decade-long request for a new form of public-government relationship. The paper adds to research on Arabic digital communication and pandemic discourse.

Exploring Public Attitude Towards Children by Leveraging Emoji to Track Out Sentiment Using Distil-BERT a Fine-Tuned Model

Sentiment analysis is a computational method that extracts emotional keywords from different texts through initial emotion analysis (e.g., Happy, Sad, Positive, Negative & Neutral). A recent study by a human rights organization found that 30% of children in Bangladesh are being abused on online in the COVID-19 epidemic by various obscene comments. The main goal of our research is to collect textual data from social media and classify the way children are harassed by various abusive comments online through the use of emoji in a text-mining method and to expose to society the risks that children face online. Another goal of this study is to set a precedent through a detailed study of child abuse and neglect in the big data age. To make the work effective, 3373 child abusive comments are collected manually from online (e.g. Facebook, Newspapers and various Blogs). At present, there is still a very limited number of Bengali child sentiment analysis studies. Fine-tuned general purpose language representation models, such as the BERT family model (BERT, Distil-BERT), and glove word embedding based CNN and Fast-Text models have been used to successfully complete the study. We show that Distil-BERT defeated BERT, Fast-Text, and CNN by 96.09% (relative) accuracy, while Bert, Fast-Text and CNN have 93.66%, 95.73%, and 95.05%, respectively. But observations show that the accuracy of the Distil-BERT does not differ much from the rest of the models. From our analysis, it can be said that the pre-trained models performed outstanding and in addition, child sentiment analysis can serve as a potential motivator for the government to formulate child protection policies and build child welfare systems. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Evaluating Intertwined Effects of Emoji and Message Framing to Vaccine Communication Strategy

Under the circumstance of continuous variation of COVID-19 virus, verified the temporariness of the vaccines made by various countries. One cannot expect permanent protection by accepting only one dose of vaccine. In order to prepare and respond to the pandemic, many countries are applying different strategies to increase vaccination rates. The WHO appeals to the world to take the vaccine booster shot for community immunity. Relevant authorities then have to provide and spread visual health messages on the booster shot to keep the public informed. This study examine how unofficial organizations can guide and persuade people to adopt relevant health actions more effectively (such as continuous vaccination) by introducing emoji with different emotional valences in different message framing. An online experiment adopted a 2 (emoji: positive versus negative) × 2 (message framing: gain framing versus loss framing) design to investigate the effects of contrary emoji on people’s self-efficacy to continuously take the booster shot. In total of 240 university students were recruited to participate in this study. Within two types of message framing, the experiment simulated 4 pieces of health messages on the COVID-19 booster shot released by an unofficial organization, together with emoji of two emotional valences. The results showed that health messages with negative emoji result in stronger self-efficacy to user. Moreover, there is an interaction effect between emoji and message framing on self-efficacy. This study is intended to provide meaningful insights for health communicators, visual designers and health practitioners concerned. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Is There Meaning in the Emoji Sequences Used on Social Media?: The Architecture of a Model for Emoji Sequences Analysis

With the emergence and widespread use of emojis in social network messages, some authors have discussed the possibility of grammar related to emoticons. In this field, some authors believe that emojis can be used to write sentences;others believe that emojis are just digital gestures without relevant grammar. This research proposes a model to analyse the co-occurrence of emoji in Twitter messages and the semantic context of using these sequences. The way is to understand whether these co-occurrences are meaningful, whether they vary from topic to topic, and whether they will change over time. The co-occurrence of emoticons will be stored in the graph database. Key phrases will be extracted and stored along with the tweet in Elasticsearch’s database from the tweet’s text. The tweets to be analysed are related to Brexit, the U.S. election and Covid-19. We will identify prominent emojis, the most commonly used emojis that appear simultaneously and are not repeated, and the associated key phrases for each discussion topic. The study outlined in this paper aims to understand if there is meaning in co-occurrences of emojis - sequences of two emojis - in messages exchanged on social networks as a starting point for a future study on syntax in emojis. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Tweet Analysis for Enhancement of COVID-19 Epidemic Simulation: A Case Study in Japan.

The COVID-19 pandemic, which began in December 2019, progressed in a complicated manner and thus caused problems worldwide. Seeking clues to the reasons for the complicated progression is necessary but challenging in the fight against the pandemic. We sought clues by investigating the relationship between reactions on social media and the COVID-19 epidemic in Japan. Twitter was selected as the social media platform for study because it has a large user base in Japan and because it quickly propagates short topic-focused messages ("tweets"). Analysis using Japanese Twitter data suggested that reactions on social media and the progression of the COVID-19 epidemic may have a close relationship. Analysis of the data for the past waves of COVID-19 in Japan revealed that the relevant reactions on Twitter and COVID-19 progression are related repetitive phenomena. We propose using observations of the reaction trend represented by tweet counts and the trend of COVID-19 epidemic progression in Japan and a deep neural network model to capture the relationship between social reactions and COVID-19 progression and to predict the future trend of COVID-19 progression. This trend prediction would then be used to set up a susceptible-exposed-infected-recovered model for simulating potential future COVID-19 cases. Experiments to evaluate the potential of using tweets to support the prediction of how an epidemic will progress demonstrated the value of using epidemic-related social media data. Our findings provide insights into the relationship between user reactions on social media, particularly Twitter, and epidemic progression, which can be used to fight pandemics.

Digital Visual Communication for Public Health: Design Proposal for a Vaccinated Emoji.

In the 21st century, the internet and particularly social media have become essential platforms for the spread of health information (including misinformation and disinformation). One of the distinguishing features of communication on these platforms is the widespread use of emojis. Though seemingly trivial emojis are now used by many if not most public health figures and organizations alongside important health updates. Much of that information has had to do with vaccination. Vaccines are a critical public health tool but one surrounded by falsehoods, phobias, and misinformation fueling vaccine hesitancy. Part of that has to do with their lack of positive representation on social media (eg, the syringe emoji is a plain needle, which for many people is an uncomfortable image). We thus argue that vaccination deserves an entirely new emoji to communicate vaccine confidence and discuss a design proposal for a vaccinated emoji that has gained traction in the global public health community.

Walking on air or hopping mad? Understanding the impact of emotions, sentiments and reactions on ratings in online customer reviews of mobile apps

Online customer reviews (OCR) are an established source of competitive intelligence. Since OCR reflect perceptions of past users about product features and performance, prospective customers of mobile apps often resort to OCR before they decide to download an app. In addition to the textual content of the OCR, potential users of the app often consult the star ratings provided by the reviewers. In this paper, we determine if the star rating of mobile payment apps suitably captures the appropriate customer perception reflected in the textual content of the OCR for the payment apps. We conduct a three-dimensional analysis of 146,914 reviews from two popular mobile payment apps and uncover the sentiment and emotions captured in the review text, and the reactions in the emojis. Our analysis shows that the consolidated sentiment and the happiness emotion obtained from the OCR of mobile payment apps impacts the star ratings more strongly, than other emotions embedded in the text, and reactions in the emojis. The findings also hold good during the Covid-19 pandemic. We propose a novel perception score that captures the nuances in the OCR more effectively and suggest how mobile app providers can use it to arrange the OCR for better compatibility with content. The results of this research will be useful for mobile app providers that depend on OCR for popularizing their apps and will provide them a novel metric based on content, that can leverage OCR better to influence future downloads.

Semantic and Pragmatic Potential of Emojis in Context of a COVID-19 Pandemic

In this paper, an attempt is made to analyze the semantic and pragmatic potential of emoji signs that explicate the "covid" meaning. The Internet thesaurus emoji with a common semantic component "medicine / coronavirus pandemic", circulating in the space of the Russian-speaking and English-speaking segments of the Internet serve as the material to be investigated. The authors of the article pose and analyze a number of significant issues to understand the role of emoji within the framework of pandemic discourse: the terminological variability of the terms smile. emoticon and emoji in the context of the described problems is clarified and specified;the structural and content potential of the emoji sign is characterized;the possibilities of the sign in the process of its use by of mass Internet culture carriers are determined;groups of emoji signs in the English-speaking and Russian-speaking segments of the Internet are distinguished and characterized. The authors use a field approach with the allocation of the core and periphery in the context of the description of the thematic thesaurus of emoji signs. In addition, attention is paid to the structural and semantic features of the emoji sign. which, ultimately, cause the appearance of a simple or complex pictogram. It is concluded that emoji signs are included in many social spheres of the Internet space within the general information context of the COVID-19 epidemic.

The use of emoji to establish student wellbeing: does the image reflect the reality?

Introduction: The impact of COVID-19 social restrictions on mental wellbeing of health professional students during placement is largely unknown. Conventional survey methods do not capture emotional fluctuations. Increasing use of smartphones suggests short message service (SMS) functionality could provide easy, rapid data. This project tested the feasibility and validity of gathering data on Therapeutic Radiography student mental wellbeing during clinical placement via emoji and SMS. Methods: Participants provided anonymous daily emoji responses via WhatsApp to a dedicated mobile phone. Additional weekly prompts sought textual responses indicating factors impacting on wellbeing. A short anonymous online survey validated responses and provided feedback on the method. Results: Participants (n = 15) provided 254 daily responses using 108 different emoji;these triangulated with weekly textual responses. Feedback concerning the method was positive. 'Happy' emoji were used most frequently;social interaction and fatigue were important wellbeing factors. Anonymity and opportunity to feedback via SMS were received positively;ease and rapidity of response engendered engagement throughout the 3-week study. Conclusions: The use of emoji for rapid assessment of cohort mental wellbeing is valid and potentially useful alongside more formal evaluation and support strategies. Capturing simple wellbeing responses from a cohort may facilitate the organisation of timely support interventions.