Therapist Feedback and Implications on Adoption of an Artificial Intelligence-Based Co-Facilitator for Online Cancer Support Groups: Mixed Methods Single-Arm Usability Study.

BACKGROUND: The recent onset of the COVID-19 pandemic and the social distancing requirement have created an increased demand for virtual support programs. Advances in artificial intelligence (AI) may offer novel solutions to management challenges such as the lack of emotional connections within virtual group interventions. Using typed text from online support groups, AI can help identify the potential risk of mental health concerns, alert group facilitator(s), and automatically recommend tailored resources while monitoring patient outcomes. OBJECTIVE: The aim of this mixed methods, single-arm study was to evaluate the feasibility, acceptability, validity, and reliability of an AI-based co-facilitator (AICF) among CancerChatCanada therapists and participants to monitor online support group participants' distress through a real-time analysis of texts posted during the support group sessions. Specifically, AICF (1) generated participant profiles with discussion topic summaries and emotion trajectories for each session, (2) identified participant(s) at risk for increased emotional distress and alerted the therapist for follow-up, and (3) automatically suggested tailored recommendations based on participant needs. Online support group participants consisted of patients with various types of cancer, and the therapists were clinically trained social workers. METHODS: Our study reports on the mixed methods evaluation of AICF, including therapists' opinions as well as quantitative measures. AICF's ability to detect distress was evaluated by the patient's real-time emoji check-in, the Linguistic Inquiry and Word Count software, and the Impact of Event Scale-Revised. RESULTS: Although quantitative results showed only some validity of AICF's ability in detecting distress, the qualitative results showed that AICF was able to detect real-time issues that are amenable to treatment, thus allowing therapists to be more proactive in supporting every group member on an individual basis. However, therapists are concerned about the ethical liability of AICF's distress detection function. CONCLUSIONS: Future works will look into wearable sensors and facial cues by using videoconferencing to overcome the barriers associated with text-based online support groups. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/21453.

Emotional Framing in the Spreading of False and True Claims

The explosive growth of online misinformation, such as false claims, has affected the social behavior of online users. In order to be persuasive and mislead the audience, false claims are made to trigger emotions in their audience. This paper contributes to understanding how misinformation in social media is shaped by investigating the emotional framing that authors of the claims try to create for their audience. We investigate how, firstly, the existence of emotional framing in the claims depends on the topic and credibility of the claims. Secondly, we explore how emotionally framed content triggers emotional response posts by social media users, and how emotions expressed in claims and corresponding users' response posts affect their sharing behavior on social media. Analysis of four data sets covering different topics (politics, health, Syrian war, and COVID-19) reveals that authors shape their claims depending on the topic area to pass targeted emotions to their audience. By analysing responses to claims, we show that the credibility of the claim influences the distribution of emotions that the claim incites in its audience. Moreover, our analysis shows that emotions expressed in the claims are repeated in the users' responses. Finally, the analysis of users' sharing behavior shows that negative emotional framing such as anger, fear, and sadness of false claims leads to more interaction among users than positive emotions. This analysis also reveals that in the claims that trigger happy responses, true claims result in more sharing compared to false claims. © 2023 ACM.

Understanding citizens' satisfaction with the government response during the COVID-19 pandemic in China: comprehensive analysis of the government hotline

PurposeThe objective of this study was to analyse the influencing factors of citizens' dissatisfaction with government services during the COVID-19 pandemic to help government departments identify problems in the service process and possible countermeasures.Design/methodology/approachThe authors first used cosine interesting pattern mining (CIPM) to analyse citizens' complaints in different periods of the pandemic. Second, the potential evaluation indices of customer satisfaction were extracted from the hotline business system through a hypothesis analysis and modelled using multiple regression analysis. During the index transformation and standardization process, a machine-learning algorithm of clustering and emotion analysis was adopted. Finally, the authors used the random forest algorithm to evaluate the importance of the indicators and obtain the indicators more important to citizen satisfaction.FindingsThe authors found that the complaint topic, appeal time, urgency of citizens' complaints, citizens' emotions, level of detail in the case record, and processing timeliness and efficiency significantly influenced citizens' satisfaction. When the government addresses complaints in a more standardized and efficient manner, citizens are more satisfied.Originality/valueDuring the pandemic, government departments should be more patient with citizens, increase the speed of the case circulation and shorten the processing period of appeals. Staff should record appeals in a more standardized manner, highlighting themes and prioritizing urgent cases to appease citizens and relieve their anxiety.

Internet Governance: Social Mentality and Public Emotion Analysis on Online Media during the COVID-19 Epidemic in Mainland China

Based on a systematical discussion of the logical relationship between social mentality as a psychological basis of social actions and institutions and social governance, and the online emotion as the core element of the dynamic tendency of internet-based social mentality to form emotional energy to promote the operation of the internet society, this paper conducts an empirical study on the online social mentality and public sentiment guidance during the COVID-19 epidemic in mainland China. We use more than 1 million Weibo dynamic data of 104 accounts of three different types including official media, self-media, and big V media and conduct emotional calculation and judgment to address our objectives. The results show that the public sentiment presented by Weibo as the carrier is mainly positive, among which the official media play a positive role in guiding emotions, while the role played by big Vs' is limited during the COVID-19 epidemic. There exists different public sentiment stemmed from the regional differences brought by the heterogeneity of social governance, economic and social development beyond the media guidance. The study provides valuable internet governance experience on how the government can guide the public to respond to and deal with the crisis with a positive attitude when major public health emergencies occur in the future. © 2022 IEEE.

Text Emotion Analysis Using Deep Learning For COVID- 19 Dataset

Managing mental health and psychological well-being is just as critical as managing physical health throughout COVID-19. The difficulty of detecting, classifying, and quantifying emotions in text in any form are addressed in this study. We consider English text collected from social media sites such as Twitter and various Kaggle datasets that can provide information useful in a variety of ways, particularly opinion mining. However, analysing and categorising text based on emotions is a difficult task and might be thought of as a more advanced kind of Sentiment Analysis. This work provides a system for categorising text into three types of emotions: positive, negative, and neutral. This analysis can be utilized by authorities to better understand people's mental health and to make appropriate policy decisions to combat the coronavirus, which is hurting the world's social well-being and economy. © 2022 IEEE.

Omicron virus emotions understanding system based on deep learning architecture.

Emotions understanding has acquired a significant interest in the last few years because it has introduced remarkable services in many aspects regarding public opinion mining and recognition in the field of marketing, seeking product reviews, reviews of movies, and healthcare issues based on sentiment understanding. This conducted research has utilized the issue of Omicron virus as a case study to implement a emotions analysis framework to explore the global attitude and sentiment toward Omicron variant as an expression of Positive feeling, Neutral, and Negative feeling. Because since December 2021. Omicron variant has gained obvious attention and wide discussions on social media platforms that revealed lots of fears and anxiety feeling, due to its rapid spreading and infection ability between humans that could exceed the Delta variant infection. Therefore, this paper proposes to develop a framework utilizes techniques of natural languages processing (NLP) in deep learning methods using neural network model of Bidirectional-Long-Short-Term-Memory (Bi-LSTM) and deep neural network (DNN) to achieve accurate results. This study utilizes textual data collected and pulled from the Twitter platform (users' tweets) for the time interval from 11-Dec.-2021 to 18-Dec.-2021. Consequently, the overall achieved accuracy for the developed model is 0.946%. The produced results from carrying out the proposed framework for sentiment understanding have recorded Negative sentiment at 42.3%, Positive sentiment at 35.8%, and Neutral sentiment at 21.9% of overall extracted tweets. The acquired accuracy using data of validation for the deployed model  is 0.946%.

Medical device companies crisis communication response to stakeholders during COVID-19: pre-crisis stage to new normal

Purpose: During COVID-19, this study aims to evaluate the crisis communication strategies (CCS) of Fortune 500 medical device businesses. These companies' CCS adoption is evaluated using data from the microblogging site Twitter. Design/methodology/approach: A total of 11,569 tweets were collected over the course of a year, from 31 December 2019 to 31 December 2020, and analysed using COVID-19's pre-crisis, crisis and new normal stages. The data acquired from Twitter is assessed using latent Dirichlet allocation-based topic modelling, valence aware dictionary for sentiment reasoning sentiment analysis and emotion recognition analysis and then further examined using fuzzy set qualitative comparative analysis to build a configurational model. The findings were compared to Cheng's (2018, 2020) integrated strategy toolkit for organisational CCS, which included 28 strategies. Findings: With positive sentiments across stages, companies chose "information providing”, "monitoring” and "good intentions” as the CCS. In the crisis and new normal stages of COVID, the emotion of "depression” was observed. Research limitations/implications: Researchers would be able to assess the CCS used through visual aids in the future by conducting a cross-industry examination using image analytics. Furthermore, by prolonging the study's duration, long-term changes in the CCS can be investigated. Practical implications: Companies should send real-time information to their stakeholders via social media during a pandemic, conveying good intentions and positive sentiments while remaining neutral. Originality/value: To the best of the authors' knowledge, this is one of the first studies to investigate the CCS patterns used by medical device businesses to communicate via social media during a pandemic. © 2023, Emerald Publishing Limited.

UNDERSTANDING AND UTILIZING STUDENTS' ATTITUDES TOWARD PARTICIPATION IN DISCUSSIONS BY USING EMOTION ANALYSIS

We propose a method that uses an emotion analysis for PBL education. The emotion analysis is a method of analyzing a person's emotions from the person's remarks or facial expressions. In this method, teachers understand the situation of students from the results of the emotion analysis and give accurate advice. PBL education often involves group activities. The students conducted groups discuss, propose ideas, select ideas, and make the products. However, not all students are able to participate in discussions and express their opinions. It is the teacher's duty to provide guidance to such students. Therefore, we propose the use of the emotion analysis techniques to identify and guide students who have problems, such as those who cannot participate in discussions. The method is possible for one teacher to grasp multiple groups at the same time and to help developing the students' ability to learn. Under COVID-19, face-to-face classes were restricted. Online classes using Zoom etc. have also been introduced in PBL education. In online classes, it is difficult to grasp the situation of students. This was a big difference from face-to-face classes. So we looked at ways to keep track of the situation for all students. This is because the gap between students who are willing to take classes and those who are reluctant to take classes has widened due to the shift to online classes. As a result of the adaption to the classes, the number of students who actively participate in the classes has increased. The effectiveness of the proposed method was confirmed. © 2022 SEFI 2022 - 50th Annual Conference of the European Society for Engineering Education, Proceedings. All rights reserved.

Evolving Face Mask Guidance During a Pandemic and Potential Harm to Public Perception: Infodemiology Study of Sentiment and Emotion on Twitter.

BACKGROUND: Throughout the COVID-19 pandemic, US Centers for Disease Control and Prevention policies on face mask use fluctuated. Understanding how public health communications evolve around key policy decisions may inform future decisions on preventative measures by aiding the design of communication strategies (eg, wording, timing, and channel) that ensure rapid dissemination and maximize both widespread adoption and sustained adherence. OBJECTIVE: We aimed to assess how sentiment on masks evolved surrounding 2 changes to mask guidelines: (1) the recommendation for mask use on April 3, 2020, and (2) the relaxation of mask use on May 13, 2021. METHODS: We applied an interrupted time series method to US Twitter data surrounding each guideline change. Outcomes were changes in the (1) proportion of positive, negative, and neutral tweets and (2) number of words within a tweet tagged with a given emotion (eg, trust). Results were compared to COVID-19 Twitter data without mask keywords for the same period. RESULTS: There were fewer neutral mask-related tweets in 2020 (ß=-3.94 percentage points, 95% CI -4.68 to -3.21; P<.001) and 2021 (ß=-8.74, 95% CI -9.31 to -8.17; P<.001). Following the April 3 recommendation (ß=.51, 95% CI .43-.59; P<.001) and May 13 relaxation (ß=3.43, 95% CI 1.61-5.26; P<.001), the percent of negative mask-related tweets increased. The quantity of trust-related terms decreased following the policy change on April 3 (ß=-.004, 95% CI -.004 to -.003; P<.001) and May 13 (ß=-.001, 95% CI -.002 to 0; P=.008). CONCLUSIONS: The US Twitter population responded negatively and with less trust following guideline shifts related to masking, regardless of whether the guidelines recommended or relaxed mask usage. Federal agencies should ensure that changes in public health recommendations are communicated concisely and rapidly.

Emotion Prediction of Comments in Twitch.Tv Livestream Environment

Livestreaming platforms are discernibly the most comprehensive sources of data in real time. Such websites enable users to broadcast content like the games which they are playing, while providing them the opportunity to interact with viewers watching the livestream. Twitch.tv is one of the most popular livestreaming platforms across the globe with millions of monthly active streamers and viewers. Owing to the COVID-19 pandemic, there has been a shift in the conventional lifestyle of the people, with them turning towards online alternatives like Twitch.tv for leisure. This change has led to an increase in the engagement of users in these livestreaming platforms by manifolds. Concurrently, a lot of data is generated from this sudden inflow, which can prove very useful in understanding the general consensus of the crowd. This data is very important, and there is a need to construe the true emotion of the people in real time, which is reflected in the comments made by them in the chat section of livestream. The streamers on Twitch.tv can consequently refine their content immediately based on the feedback that they can infer from the responses given by the users. But, due to the sheer volume of data and convoluted nature of the chat due to the use of emojis, emotes, and emoticons, there are bound to be inconsistencies, human errors, and other esoteric references which are exceedingly complex to dissect, making the task of language processing difficult and leading to incoherent results. Taking into account the hindrance posed by these issues, we have taken up the task to achieve fairly accurate emotion prediction by putting forward machine learning and deep learning techniques. This will involve the creation of a labelled dataset that can be used for training and evaluating the algorithms. Given how context-specific most comments are on the platform, this will be an extensive task. The project will also require the creation of an end-to-end system that performs emotion analysis and giving results in real time through feedback-loops. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The Effect of COVID-19 on Tourism Affection: A Pleasure-Arousal-Dominance Framework Perspective

Based on the data of COVID-19 epidemic and online tourism review, this paper explores the impact of COVID-19 on tourists' tourism emotion. First, based on the existing theory, the hypothetical relationship between COVID-19 and tourists' PAD (Pleasure-Around-Dominion) emotion is established. Then, the PAD emotion variables in tourism reviews are extracted through the emotion analysis method, and then an empirical econometric model is established. Finally, the model is estimated using the double difference method, and a series of robustness tests are conducted. The empirical results show that COVID-19 has a significant negative impact on tourists' PAD sentiment. © 2022 IEEE.

Digital Chuimsae: Evolution of Korean Traditional Performing Arts

A traditional Korean performance is completed through the participation of the audience called ‘Chuimsae'. The audience and performers are not separated like Western-style performances, but become one and communicate with each other periodically. During the performance, the audience naturally sings, shouts, and cheers for the characters. ‘Chuimsae', which has been passed down for hundreds of years as a performance culture, also affects the way Korean pop music is performed, which is now represented by the genre of K-pop. The corona pandemic is causing enormous damage to the domestic and international performance industry. In particular, Korean traditional performances have many limitations because they are freely performed at close and physically close distances without any distinction between performers and audiences. In our study, text emotion analysis technology that combines smartphone messenger and machine learning technology is used as a tool for audience communication (Digital Chuimsae). During the performance, the audience simply writes any word or sentence they want to say, and the AI technology analyzes it and displays it as a numerical value of emotion. This will directly or indirectly affect various materials that will be used as background images or elements of composition during performances. For example, if a performer's mood falls into the category of ‘enjoyable', it operates on a form that can change the number and size of particles according to the degree. Audiences will be able to digitally implement ‘Chuimsae' in real time while watching the performance in a safe place from the coronavirus. We conducted a test drive using this system, and each performance showed the result of being transformed into a different form by the audience. It is expected that there will be continuous development as a new performance platform in the traditional Korean performance industry, which is currently suspended due to the corona pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Emotion Analysis to Identify Risk of Committing Suicide Using Statistical Learning

Given the alarming numbers of incidences of suicide in today's society, and especially after social distancing and confinement prevention measures brought by the COVID-19 pandemic, mental health experts require tools to support the identification of individuals at risk of committing suicide. This paper proposes a new methodology to detect suicidal tendencies in Twitter users relying on analysis of emotions. Using statistical learning models, the proposed methodology identifies the risk level through emotion analysis in the text. Results show that supervised non-parametric and unsupervised methods detected extreme levels of suicide risk on the dataset. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Comparing Emotion and Sentiment Analysis Tools on Italian anti-vaccination for COVID-19 posts

Since the beginning of the vaccination campaign against Covid-19 in our country, resistance to vaccination has emerged on the part of a not negligible portion of the Italian population. Emotions (such as sadness, fear, etc.) and the polarity (positive/negative) of an opinion published on social media are essential for analyzing people's position towards a topic. For this reason, we applied two Natural Language Processing tools, FEEL-IT and SentIta, to a few thousands of social networks posts against the COVID-19 vaccine or specifically the booster shot. We find out some significant insights about the prevalent emotions among users and propose to combine the outputs of the tools in order to increase the classification performance of an opinion according to three possible sentiments (positive/neutral/negative). © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Emotion Prediction of Comments in Twitch.Tv Livestream Environment

Livestreaming platforms are discernibly the most comprehensive sources of data in real time. Such websites enable users to broadcast content like the games which they are playing, while providing them the opportunity to interact with viewers watching the livestream. Twitch.tv is one of the most popular livestreaming platforms across the globe with millions of monthly active streamers and viewers. Owing to the COVID-19 pandemic, there has been a shift in the conventional lifestyle of the people, with them turning towards online alternatives like Twitch.tv for leisure. This change has led to an increase in the engagement of users in these livestreaming platforms by manifolds. Concurrently, a lot of data is generated from this sudden inflow, which can prove very useful in understanding the general consensus of the crowd. This data is very important, and there is a need to construe the true emotion of the people in real time, which is reflected in the comments made by them in the chat section of livestream. The streamers on Twitch.tv can consequently refine their content immediately based on the feedback that they can infer from the responses given by the users. But, due to the sheer volume of data and convoluted nature of the chat due to the use of emojis, emotes, and emoticons, there are bound to be inconsistencies, human errors, and other esoteric references which are exceedingly complex to dissect, making the task of language processing difficult and leading to incoherent results. Taking into account the hindrance posed by these issues, we have taken up the task to achieve fairly accurate emotion prediction by putting forward machine learning and deep learning techniques. This will involve the creation of a labelled dataset that can be used for training and evaluating the algorithms. Given how context-specific most comments are on the platform, this will be an extensive task. The project will also require the creation of an end-to-end system that performs emotion analysis and giving results in real time through feedback-loops. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Interplay between Discrete Emotions and Preventive Behavior in Health Crises: Big Data Analysis of COVID-19.

Understanding the interplay between discrete emotions and COVID-19 prevention behaviors will help healthcare professionals and providers to implement effective risk communication and effective risk decision making. This study analyzes data related to COVID-19 posted by the American public on Twitter and identifies three discrete negative emotions (anger, anxiety, and sadness) of the public from massive text data. Next, econometric analyses (i.e., the Granger causality test and impulse response functions) are performed to evaluate the interplay between discrete emotions and preventive behavior based on emotional time series and Google Shopping Trends time series, representing public preventive behavior. Based on the textual analysis of tweets from the United States, the following conclusions are drawn: Anger is a Granger cause of preventive behavior and has a slightly negative effect on the public's preventive behavior. Anxiety is a Granger cause of preventive behavior and has a positive effect on preventive behavior. Furthermore, preventive behavior is a Granger cause of anxiety and has a negative and lagging effect on anxiety. Exploring how discrete emotions, such as anger and anxiety, affect preventive behaviors will effectively demonstrate how discrete emotions play qualitatively different roles in promoting preventive behaviors. Moreover, understanding the impact of preventive behaviors on discrete emotions is useful for better risk communication.

Triggers and Tweets: Implicit Aspect-Based Sentiment and Emotion Analysis of Community Chatter Relevant to Education Post-COVID-19

This research proposes a well-being analytical framework using social media chatter data. The proposed framework infers analytics and provides insights into the public’s well-being relevant to education throughout and post the COVID-19 pandemic through a comprehensive Emotion and Aspect-based Sentiment Analysis (ABSA). Moreover, this research aims to examine the variability in emotions of students, parents, and faculty toward the e-learning process over time and across different locations. The proposed framework curates Twitter chatter data relevant to the education sector, identifies tweets with the sentiment, and then identifies the exact emotion and emotional triggers associated with those feelings through implicit ABSA. The produced analytics are then factored by location and time to provide more comprehensive insights that aim to assist the decision-makers and personnel in the educational sector enhance and adapt the educational process during and following the pandemic and looking toward the future. The experimental results for emotion classification show that the Linear Support Vector Classifier (SVC) outperformed other classifiers in terms of overall accuracy, precision, recall, and F-measure of 91%. Moreover, the Logistic Regression classifier outperformed all other classifiers in terms of overall accuracy, recall, an F-measure of 81%, and precision of 83% for aspect classification. In online experiments using UAE COVID-19 education-related data, the analytics show high relevance with the public concerns around the education process that were reported during the experiment’s timeframe. © 2022 by the authors.

Building and Testing Fine-Grained Dataset of COVID-19 Tweets for Worry Prediction

The COVID-19 outbreak has resulted in the loss of human life worldwide and has increased worry concerning life, public health, the economy, and the future. With lockdown and social distancing measures in place, people turned to social media such as Twitter to share their feelings and concerns about the pandemic. Several studies have focused on analyzing Twitter users’ sentiments and emotions. However, little work has focused on worry detection at a fine-grained level due to the lack of adequate datasets. Worry emotion is associated with notions such as anxiety, fear, and nervousness. In this study, we built a dataset for worry emotion classification called “WorryCov”. It is a relatively large dataset derived from Twitter concerning worry about COVID-19. The data were annotated into three levels (“no-worry”, “worry”, and “high-worry”). Using the annotated dataset, we investigated the performance of different machine learning algorithms (ML), including multinomial Naïve Bayes (MNB), support vector machine (SVM), logistic regression (LR), and random forests (RF). The results show that LR was the optimal approach, with an accuracy of 75%. Furthermore, the results indicate that the proposed model could be used by psychologists and researchers to predict Twitter users’ worry levels during COVID-19 or similar crises. © 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Let the Big Data Speak: Collaborative Model of Topic Extract and Sentiment Analysis COVID-19 Based on Weibo Data

Micro-blog is an important medium of emergency communication. The topic and emotion analysis of micro-blog is of great significance in identifying and predicting potential problems and risks. In this paper, a collaborative analysis model of emotion and topic mining is constructed to analyze the users’ sentiment and the topics they care about, Firstly, we use SO-PMI to construct domain sentiment lexicon and extract topics with LDA. Then we use the collaborative model to analyze sentiment and topic. The results showed that the model we proposed can present the features of sentiment and topic of user concerns. And through text clustering and sentiment analysis, it is found that the attitude of users towards the COVID-19 has gone through three stages, namely, a period of fluctuating tension and anxiety, a period of slowly rising solidarity and a period of stable self-confidence with little fluctuation, on the whole, positive is greater than negative, positive than negative state. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Multimodal Emotion Analysis of Robotic Assistance in Elderly Care

Due to the increasing aging of the population, the number of elderly people requiring care is growing in most European countries. However, the number of caregivers working in nursing homes and on daily care services is declining in countries like Germany or Italy. This limits the time for interpersonal communication. Furthermore, as a result of the Covid-19 pandemic, social distancing during contact restrictions became more important, causing an additional reduction of personal interaction. This social isolation can strongly increase emotional stress. Robotic assistance could contribute to addressing this challenge on three levels: (1) supporting caregivers to respond individually to the needs of patients and residents in nursing homes;(2) observing patients' health and emotional state;(3) complying with high hygiene standards and minimizing human contact if required. To further the research on emotional aspects and the acceptance of robotic assistance in care, we conducted two studies where elderly participants interacted with the social robot Misa. Facial expression and voice analysis were used to identify and measure the emotional state of the participants during the interaction. While interpersonal contact plays a major role in elderly care, the findings reveal that robotic assistance generates added value for both caregivers and patients and that they show emotions while interacting with them. © 2022 ACM.