Positive Sentiment Research Articles

Identify Issues That Influence Customer Satisfaction Using Sentiment Analysis and Topic Modeling

The development of internet users in Indonesia continues to increase over time, this makes customers comfortable with digital transactions. Trinusa Travelindo Company or better known as Traveloka, which is engaged in the online travel agent sector, has proven its extraordinary achievement, namely becoming the E-tourism application that is ranked 1st most visited by the Indonesian people in 2022. However, these results are also directly proportional to the number of negative reviews created in the application review itself. This study aims to identify what issues affect customer satisfaction with topic modelling. This study uses the text mining method derived from the results of Traveloka application user reviews. The data source obtained in this study is secondary data by using the Traveloka application review data crawling technique with samples from January to May. Based on sentiment analysis, positive sentiment is more dominant (50.38%) compared to negative sentiment (49.62%). User reviews are grouped based on the results of sentiment analysis, then Topic Modelling is carried out to find out what issues affect customer satisfaction. The topic modelling of the data used is Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) with the help of phyton 6.3. to find out what words and topics often appear or as factors that influence customer satisfaction. The most influential negative issues include the refund and payment process, pay later services, and the ordering experience which is considered complicated. On the other hand, issues that contribute positively to customer satisfaction include product quality, service quality, and promos and discounts. This is reinforced by the results of topic modelling which show that these aspects are often the main concerns in user reviews on the Google Play store. Thus, this study suggests that in order to increase customer satisfaction, Traveloka needs to focus on improving the quality of service, products, and offering more attractive promos, while simplifying the refund process and ordering experience in the application, some still say it is complicated.

Digital threads of architectural heritage: navigating tourism destination image through social media reviews and machine learning insights

ABSTRACT While emphasizing sustainable development goals for cultural heritage preservation, tourism destination image based on the predefined traditional survey often fails to meet the dynamic tourist demands. Taking the Nanjing Museum of Modern History in China as a case study, this paper innovatively combined the Latent Dirichlet Allocation (LDA) and Importance-Performance Analysis (IPA) to quantitatively extract and assess the destination image from the social media data. The results demonstrated that the tourists mainly focused on 12 topics from individual, environmental, and social backgrounds, mostly on architectural style, transportation, and location. Tourists perceived most positive sentiments in historical culture (84.94%), and architectural style (70.54%), but most negative sentiments in ticket purchasing experience (27.60%), living and service facilities (14.09%). The IPA results reveal that historical culture is the strength, yet there is an overemphasis on historical events. This research marked a methodological advancement by combining LDA and IPA, enabling a detailed analysis of vast online reviews and providing critical insights for improving the tourism destination image in architectural heritage studies.

The Reverse Effect of Reviewers’ Sentiment Tendency: The Role of Readers’ Empathy and Trust

Streamers have become a crucial component of consumers’ live shopping experience. Reviewers will transfer their emotions about the streamer to the product-after review. We innovatively verified the reverse effect of reviewers’ sentiment tendency toward streamer on the product sustained sale in the streamer’s live streaming. Results from a field data set and three lab experiments show that reviewers’ positive sentiment tendency toward the streamer has a negative effect on the readers’ purchase intention when the streamer resale this product in his live streaming. While reviewers’ negative sentiment tendency toward the streamer can enhance readers’ purchase intention to the product resold in the streamer’s live streaming. These effects are separately mediated by readers’ trust in the review and their empathy to the streamer. And the two mediators are moderated by the streamer’s expertise. Our findings expand the literatures about reviews’ emotions and suggest implications for streamers, retailers, and review platforms faced with the task of managing live-shopping reviews.

Beyond Traditional Banking: Exploring the Influence of Technology on Depositors' Decision-Making in Bank Selection

Purpose: The banking sector is crucial for the economy, and with growing competition, banks must pinpoint factors influencing depositors' choices. This study aims to uncover how technology adoption by commercial banks plays a role in depositors' decisions. Methodology: A hybrid approached is attempted in this study to explore the phenomenon of interest. In the qualitative phase, focus group discussion was conducted on two heterogeneous groups, each with ten participants. The recorded audio was further transcribed using speech-to-text modules. A natural language processing tool has been used to identify words or phrases that are semantically related or have similar meanings to depositors’ sentiment analysis. The quantitative phase involved descriptive statistics, principal component analysis and regression analysis to explore insight numerically. Findings: Respondents expressed a generally positive sentiment toward technology in banking, influencing their selection decisions, in the qualitative phase. In the quantitative phase, significant technological aspects affecting depositors' post-selection evaluations are identified. Findings from both approaches converge on the same line. Practical implications: Findings of this study may aid decision-makers in banking industry to articulate distinct technological aspect implied in banking service delivery and better design service offerings to attract new depositors and gain competitive advantages. Originality/Value: This is the first study to focus solely on technology to explore how technology influences depositors' bank choices in the context of Bangladesh. Research Limitations: Significant parts of several conversations were sorted out for sentiment analysis. If it was possible to consider all relevant statements of the focus group discussion for sentiment analysis, it might convey a more pertinent sentiment score.

From Tweets to Streets: Observational Study on the Association Between Twitter Sentiment and Anti-Asian Hate Crimes in New York City from 2019 to 2022.

Anti-Asian hate crimes escalated during the COVID-19 pandemic; however, limited research has explored the association between social media sentiment and hate crimes toward Asian communities. This study aims to investigate the relationship between Twitter (rebranded as X) sentiment data and the occurrence of anti-Asian hate crimes in New York City from 2019 to 2022, a period encompassing both before and during COVID-19 pandemic conditions. We used a hate crime dataset from the New York City Police Department. This dataset included detailed information on the occurrence of anti-Asian hate crimes at the police precinct level from 2019 to 2022. We used Twitter's application programming interface for Academic Research to collect a random 1% sample of publicly available Twitter data in New York State, including New York City, that included 1 or more of the selected Asian-related keywords and applied support vector machine to classify sentiment. We measured sentiment toward the Asian community using the rates of negative and positive sentiment expressed in tweets at the monthly level (N=48). We used negative binomial models to explore the associations between sentiment levels and the number of anti-Asian hate crimes in the same month. We further adjusted our models for confounders such as the unemployment rate and the emergence of the COVID-19 pandemic. As sensitivity analyses, we used distributed lag models to capture 1- to 2-month lag times. A point increase of 1% in negative sentiment rate toward the Asian community in the same month was associated with a 24% increase (incidence rate ratio [IRR] 1.24; 95% CI 1.07-1.44; P=.005) in the number of anti-Asian hate crimes. The association was slightly attenuated after adjusting for unemployment and COVID-19 emergence (ie, after March 2020; P=.008). The positive sentiment toward Asian tweets with a 0-month lag was associated with a 12% decrease (IRR 0.88; 95% CI 0.79-0.97; P=.002) in expected anti-Asian hate crimes in the same month, but the relationship was no longer significant after adjusting for the unemployment rate and the emergence of COVID-19 pandemic (P=.11). A higher negative sentiment level was associated with more hate crimes specifically targeting the Asian community in the same month. The findings highlight the importance of monitoring public sentiment to predict and potentially mitigate hate crimes against Asian individuals.

WP6.6 - Sentiment Analysis of Social Media Posts by Medical Professional in the UK

Abstract Aims Notoriously described as being controversial in nature, we aim to objectively assess the language used amongst medical professionals in social media (SM). Methods Data was extracted from the Reddit SM website under the sub-page "r/DoctorsUK" forum, through a data-extraction code written in the python language. This was then analysed through a Natural Language Toolkit algorithm to classify language used into positive, negative and neutral sentiments with an overall polarity score ranging from -1 noting most negative to 1 being most positive. Results A total of 992 articles were examined over a 24-day period, as limited by Reddit data application programme interface (API) protocols. A total of 210 positive, 218 negative and 564 neutral articles were noted with an average of 43 articles posted per day. Headlines sentiment on average was overall marginally positive at 0.0154. Comment tree within each headline was also examined, noting a more positive 0.173. Through regression analysis, it was noted that headline and comment sentiments was directly correlated, and this was statistically significant(P=1.24*10-10). It was also noted that more negative headlines had higher “likes” and were therefore featured more prominently within the forum. Conclusions Although SM posts are overall positive in sentiment, it is noted that more negative sentiments are often up-voted and therefore featured more prominently due to their more provocative nature and emotions incited amongst readers. As SM posts elicits strong psychological and psychosocial reactions from a community, there is an urgency to more accurate data driven models in developing future policies to maintain an effective professional morale within the NHS.

Carbon credit sentiments and green energy stocks

ABSTRACT This paper provides a new perspective on the role of climate attention in the media for returns of green energy stocks. Making use of a data set that includes various measures of sentiments and media attention in newspapers and social media related to 1) carbon credit and 2) stock markets, we disentangle the drivers of green stock returns. Our results for various green stock indices suggest that green stock returns are not only driven by stock-related news but also driven by sentiment related to carbon credit news. We also show that the underlying news source (social media vs. newspapers) matters. News related to uncertainty around carbon credit in conventional newspapers is accompanied by decreasing returns. In social media, future-oriented pessimistic emotions relate negatively and recent positive sentiment relates positively to green stock returns.

Sentiment Analysis Towards the Film Dirty Vote on Twitter Social Media Using the K-Nearest Neighbor Algorithm

The appearance of the dirty vote film has received public attention and went viral on social media after being released and watched by millions of people in a short time. The dirty vote film has become a topic of discussion, one of which is on the social media platform Twitter. This research was conducted to determine the views or tendencies of public opinion regarding dirty vote films on Twitter social media using K-Nearest Neighbor which will be classified into positive, neutral and negative sentiment. The sentiment data that was collected in the data crawling process was 4000 pieces of data. Then after preprocessing there were 3978 data. Labeling was carried out using text blob, it was found that the negative sentiment class was 3470 superior to the positive sentiment class of 451 and the neutral sentiment class was 57. The 10-fold cross validation test produced an average accuracy value of 87.5%. Testing was carried out with 80% training data consisting of 3182 data and 20% test data consisting of 796 test data. The results of sentiment analysis show that the K-Nearest Neighbor method can be used for sentiment analysis. The accuracy value obtained was 87%, precision was 87%, recall was 100%, and f1-score was 93%.

To escape the pain: Paths to voluntary turnover, social pain, and influences on the selection of a new job role

SummaryThe unfolding model of voluntary turnover established that there is no “one size fits all” turnover experience. Anecdotal evidence suggests that voluntary turnover experiences can be painful, yet the role of social pain (as distinct from dissatisfaction) in turnover and subsequent job selection has not yet received much scholarly attention. In Study 1, we re‐analyze 13 detailed case histories of employees who had recently quit one organization and entered another. Using a cluster analysis of the terms they used when describing their reasons for leaving and when describing what attracted them to their new roles, we find that experiencing social pain in the prior role affected the things people prioritized when selecting a new role. Furthermore, sentiment analysis revealed that higher social pain was linked with lower positive sentiment (hope, excitement, joy, etc.) during the job search process. Study 2 creates a self‐report measure of social pain, and Study 3 quantitatively reaffirms the relationship between social pain and subsequent affect. This set of studies provides initial evidence that social pain is different from the attitudinal constructs that have been traditionally incorporated into voluntary turnover models (e.g., satisfaction, commitment, embeddedness) and that social pain may have implications for turnover, retention, and employees' post‐turnover destinations.

Sentiment Analysis of Support for the DPR's Right to Inquiry on the Issue of 2024 Election Fraud Using the Support Vector Machine Method

This research aims to analyze public sentiment towards supporting the DPR's right to inquiry in the 2024 Election fraud issue using the Support Vector Machine (SVM) method. Data was obtained from the social media application X which has a wide user base and is relevant to the issue under study. Comments on the application are classified into positive and negative sentiments after going through the pre-processing stage. The SVM method was chosen because of its high ability in text classification based on appropriate kernels. This research shows how much influence the X application has in identifying public sentiment and the effectiveness of the SVM method in sentiment classification. It is hoped that the research results will provide in-depth insight into public sentiment regarding the issue of fraud in the 2024 elections and support better decision making in the context of politics and democracy in Indonesia.

Themes and sentiments in conversations about food waste on Twitter: Proposal of a framework using neural topic modeling

There is growing research interest in the role of social media in the food industry, and its potential has yet to be fully exploited. This study extracted and analyzed data from Twitter, now X, (2018–2023) using relevant keywords related to food waste (FW), identified frequently discussed topics, and determined the sentiments involved in tweets. With the aid of neural topic modeling techniques, this study found that the main topics discussed in this social network include “Food and Garden Waste Composting and Recycling,” “Global Food Waste and Management,” “Reducing Food Waste & Saving Money,” “Single-Use Plastic and Waste Management,” and “Creative Uses of Leftovers in Meals”. Some of the subject categories often discussed are FW Environmental concerns, FW events, Leftovers management, and FW household management. Most subject categories are associated with positive sentiments, which are associated with topics like tips and recommendations for reducing FW. Negative sentiments are related to discussions around the environmental and social impacts of FW. Our study found a gap in knowledge and awareness of food recycling practices. Our study extends previous research because the analyzed messages are not restricted to specific geographical regions. Moreover, our study also used a more varied dataset in terms of user profiles and employed a search string that comprised a more varied set of terms, making our search more thorough. A comprehensive framework of discussed topics and associated sentiments is proposed. Our study has crucial implications for the design of FW reduction campaigns and interventions in social media.

Sentiment Analysis Techniques: A Comparative Study of Logistic Regression, Random Forest, and Naive Bayes on General English and Nigerian Texts

This research investigates sentiment analysis on two distinct datasets: a general English dataset and a Nigerian dataset (Gangs of Lagos movie review), using three machine learning algorithms: Logistic Regression, Random Forest, and Naive Bayes with python programming language and its libraries. The study aims to evaluate and compare the performance of these models across different linguistic and cultural contexts. Results indicate that Logistic Regression consistently outperforms the other models, achieving the highest accuracy and balanced performance across sentiment classes. Random Forest provides comparable results but struggles with positive sentiment detection in the Nigerian dataset. Naive Bayes shows the lowest overall accuracy, with significant challenges in recall for certain sentiment classes. These findings highlight the importance of model selection and tuning tailored to specific datasets for effective sentiment analysis.

Analisis Sentimen Media Sosial Terhadap Aplikasi Perbankan Untuk Mengetahui Kepuasan Pengguna Aplikasi: Studi Kasus Pada Livin by Mandiri dan BCA Mobile

In this study, the researchers aimed to explore how sentiment expressed on social media platforms towards e-servqual provided by Livin' by Mandiri and BCA Mobile applications. This research uses sentiment analysis by collecting data from Twitter. Naïve Bayes is the method chosen for this research due to its user-friendly nature and ability to handle large datasets. Regarding the results of sentiment analysis on the e-servqual dimensions for the BCA Mobile application, overall opinions tend to be negative, although there are some positive opinions, especially on the variables of user friendliness, personal nedss, and efficiency. However, sentiments towards the dimensions of site organization, responsiveness, and reliability are mostly filled with negative sentiments. In contrast, sentiments towards the Livin by Mandiri application mostly contain positive sentiments. In the e-servqual dimension, Personal Needs, user frendlines and Responsiveness, and Site Organization all show positive sentiments, while, Efficiency, and Reliability show negative sentiments. In addition, based on the results of sentiment analysis, this research also examines topic modeling which is used to see what topics often appear in both applications.

Patient and care partner perspectives and preferences related to myasthenia gravis treatment: A qualitative study.

Due to the high symptom and treatment burden in myasthenia gravis (MG), understanding patient and care partner perspectives and preferences is crucial. This study used voice analysis and virtual focus groups to understand patient and care partner experiences with MG-related symptoms, treatments, and preferences. The voice analysis via social media listening used artificial intelligence-powered tools to gather and structure public digital conversations on MG. Focus groups included people living with MG and care partners who completed a questionnaire and participated in a 1-h virtual session facilitated using a semi-structured interview guide. Qualitative data were aggregated, transcribed, and thematically analyzed. The voice analysis examined 11,554 posts from 8321 individuals, discussing MG symptoms, treatments, and burden. Of 7563 symptom-related posts, 5902 (78%) conveyed negative, 1427 (19%) neutral, and 234 (3%) positive sentiment. The most frequently mentioned symptoms were categorized as dysarthria, muscle weakness, and dysphagia. MG treatment sentiment analysis identified 6667 posts (67%) as neutral, 2887 (29%) as negative, and 350 (4%) as positive. For the focus groups, 15 individuals (12 patients and 3 care partners) completed the questionnaire and 14 participated in the virtual focus group sessions. The 15 participants who completed the questionnaire prioritized treatment convenience, symptom control for improved quality of life, and preventing potential MG crises in their current treatment. New treatment expectations included increased effectiveness, less frequent dosing, faster onset, and fewer side effects. Participants were also receptive to wearable medication delivery systems placed on the body and valued direct involvement in treatment decisions. Patients and care partners are often negatively impacted by MG symptoms and value convenient and fast-acting treatments that control symptoms with minimal side effects. Considering patient preferences may help optimize treatment decisions and improve patients' overall well-being and satisfaction in their care.

Aspect-based sentiment analysis: A dual-task learning architecture using imbalanced maximized-area under the curve proximate support vector machine and reinforcement learning

Aspect-based sentiment analysis (ABSA) deals with understanding sentiments about specific attributes. Aspect term extraction (ATE) plays an essential role in aspect sentiment classification (ASC) by specifying the main aspects for sentiment accuracy. This paper introduces a dual-task framework for ATE and ASC models in which, the ATE model uses the imbalanced maximized-area under the curve proximate support vector machine (ImAUC-PSVM) method to mitigate the imbalance problem and the output of ATE serves as the reward in the training process of ASC supported by RL (reinforcement learning). The ImAUC-PSVM uses the conventional PSVM (proximate support vector machine) strength for sentiment tasks and the AUC (area under the curve) optimization for imbalance addressing. The ASC model employs multiple kernels for word embedding, with a transductive long short-term memory (TLSTM) for sentence representation that considers samples near the test point for better model refinement. The policy of the RL-based training strategy of the ASC model is represented by a multilayer perceptron (MLP) that categorizes sentences into positive, negative and neutral sentiments. In this RL context, aspect terms serve as rewards in the training process. The scope loss function (SLF) is also integrated within RL to rectify dataset imbalances. To optimize ATE and ASC models, an artificial bee colony (ABC) technique is incorporated for hyperparameter optimization. Experiments and evaluations conducted on English (Restaurant and Laptop) and Hindi datasets show the superiority of the proposed model over existing models of the literature. Moreover, transfer learning (TL) is applied to a Twitter dataset to assess its effect on ATE. The F-measures of the ATE model are obtained as 90.12 and 84.10 and the accuracies of the ASC model are calculated as 84.10 and 82.02 on Restaurant and Laptop datasets, respectively.

Implications of public acceptance assessments for site selections in the small modular reactors using system dynamics

This study investigates the feasibility of siting a nuclear power plant (NPP) within a densely populated university campus. Site selection for NPPs is a complex process involving numerous interconnected factors. To address this complexity, we employ system dynamics (SD) modeling, a methodology renowned for its ability to quantify both technological and social aspects. Our simulation results indicate that public acceptance (PA) is a critical determinant in site selection. In the fourth month of the simulation, the model's values were identical in both PA and non-PA scenarios. However, subsequent simulations revealed a significant divergence, with PA-implicated values reaching 85.398 compared to 24.240 in the non-PA scenario. This disparity underscores the primacy of PA in the decision-making process. Given the extended timeline of NPP construction, the gap between PA and non-PA outcomes is likely to widen further, particularly in the context of positive public sentiment toward the NPP. The insights gleaned from this research can be applied to other industries, such as oil refineries, where the potential for atmospheric pollution due to toxic gas leaks necessitates careful site selection.

Gender Differences in Letters of Recommendations and Personal Statements for Neurotology Fellowship over 10 Years: A Deep Learning Linguistic Analysis.

Personal statements (PSs) and letters of recommendation (LORs) are critical components of the neurotology fellowship application process but can be subject to implicit biases. This study evaluated general and deep learning linguistic differences between the applicant genders over a 10-year span. Retrospective cohort. Two institutions. PSs and LORs were collected from 2014 to 2023 from two institutions. The Valence Aware Dictionary and Sentiment Reasoner (VADER) natural language processing (NLP) package was used to compare the positive or negative sentiment in LORs and PSs. Next, the deep learning tool, Empath, categorized the text into scores, and Wilcoxon rank sum tests were performed for comparisons between applicant gender. Among 177 applicants over 10 years, 120 were males and 57 were females. There were no differences in word count or VADER sentiment scores between genders for both LORs and PSs. However, among Empath sentiment categories, male applicants had more words of trust ( p = 0.03) and leadership ( p = 0.002) in LORs. Temporally, the trends show a consistently higher VADER sentiment and Empath "trust" and "leader" in male LORs from 2014 to 2019, after which there was no statistical significance in sentiment scores between genders, and females even have higher scores of trust and leadership in 2023. Linguistic content overall favored male applicants because they were more frequently described as trustworthy and leaders. However, the temporal analysis of linguistic differences between male and female applicants found an encouraging trend suggesting a reduction of gender bias in recent years, mirroring an increased composition of women in neurotology over time.

Investigation of Exercise Interventions on Postoperative Recovery in Lung Cancer Patients: A Qualitative Study Using Web Crawling Technology.

Rapid recovery after lung cancer surgery is challenging. Exercise is a low-cost, effective method to expedite recovery. Despite numerous exercise interventions, many fail to consider patient perspectives, leading to low adherence and short-term effects. Understanding lung cancer patients' perspectives on postoperative exercise and exploring their exercise-related concerns and needs are crucial for enhancing the effectiveness of exercise-based rehabilitation programs. This study aims to analyze lung cancer patients' perspectives on postoperative exercise in their daily lives, exploring their concerns and needs related to postoperative exercise to help healthcare professionals develop personalized exercise plans. An internet crawling technique collected online inquiries from Baidu webpages about postoperative physical activity in lung cancer patients, using "lung cancer", "surgery", and"exercise" as keywords. The data was encoded, categorized, and analyzed using a large-scale semantic analysis platform in natural language processing and information retrieval to examine term frequency, sentiment tendencies, and attributes in the inquiry texts. Initially, 2727 queries were retrieved; after screening, deduplication, and cleansing, 201 unique queries were identified. Queries related to "modes of exercise" constituted the largest proportion. The most frequently occurring words in the word frequency analysis were "lung", "cancer", "should", "can", "long", "early", and "surgery", "exercise", "respiratory". Postoperative lung cancer patients demonstrate significant interest in whether they should engage in exercise, as well as in the appropriate types and duration of such activities, indicating a strong need for detailed guidance and knowledge related to exercise. The sentiment analysis showed a positive score of 87.5% and a negative score of 12.5%, indicating that postoperative lung cancer patients view exercise positively and have an enthusiastic attitude towards it. Among the positive sentiment attributes, "good" was the most frequently mentioned term, whereas "bad" and "surprising" were the most prevalent terms within the negative sentiment attributes. Postoperative physical activity receives limited attention from lung cancer patients, who emphasize their preferences for exercise modalities. Their inquiries often reflect psychological concerns, such as fear and helplessness caused by symptoms. Understanding patients' perspectives on postoperative physical activity within their real-life contexts can help integrate psychological support into exercise plans. This integration could guide healthcare professionals in developing more personalized postoperative exercise regimens for lung cancer patients.

Thematic and sentiment analysis of replies to depression help seeking related posts on Sina Weibo

Depression is the most prevalent mental disorder globally, but traditional sources of depression help seeking such as psychiatry are laced with social stigma, resulting in a low worldwide depression professional help seeking rate. In recent years, many with depression have turned to the internet and specifically to social media as an alternative medium to seek help for depression, but the effects of online help have been discovered to be ambiguous. The present study aimed to investigate how depression help seeking related posts are responded to on Chinese social media in terms of two dimensions: themes and sentiments. Employing machine learning statistical techniques including topic modeling and sentiment analysis, this paper assessed the themes and sentiments within 8027 responses to 648 depression help seeking related posts created between October 1, 2018 and June 30, 2024. Topic modeling results reported that four prevalent themes underlie replies to depression help seeking related posts: Sad emotional support, Empathizing, Advising professional help, and Sharing. These prevalent themes all reflected supportive attitudes towards depression help seeking related posts through diverse ways. Sentiment analysis, on the other hand, revealed that still, 18.34% of responses held negative sentiments toward depression help seeking related posts. However, the prevalent sentiment in responses was also discovered to be positive, in line with the results of topic modeling. 78.04% of replies were found to hold positive sentiment, 3.63% of replies were found to hold neutral sentiment, and the mean sentiment score of responses was also positive. The findings of the present study provide insight into how depression help seeking is reacted to in Chinas social media landscape, indicating possible factors that may hinder the effects of seeking help for depression on social media and suggesting the potential need for systems to foster a more supportive online environment towards depression help seeking on Chinese social media.

Social sharing of emotion during the collective crisis of COVID-19.

We collected data from two sources - social media and online questionnaires - to investigate the emotional consequences of social sharing during the COVID-19 pandemic. Study 1 tracked and analysed sentiment of tweets posted over the course of a month in the crisis period and found that users who tweeted more frequently about COVID-19 expressed decreasing negative sentiment and increasing positive sentiment over time. Granger causality tests confirmed that this association was better interpreted in the forward direction (sharing levels predicting sentiment) than in the reverse direction (sentiment predicting sharing levels). Study 2 focused on immediate emotional consequences of sharing COVID-19-related events and found that participants reported improved overall affect about an event after sharing it, especially when that event was a personal experience rather than a news story. Reported positive feelings about both kinds of events were also significantly higher after sharing. Taken together, both studies suggested that social sharing is linked with emotional relief and may therefore help people to deal with their negative experiences during a persistent collective crisis.