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Emotion diffusion effect: Negative sentiment COVID-19 tweets of public organizations attract more responses from followers
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1001/jama.2020.6130openalex-snapshot
Association of Public Health Interventions With the Epidemiology of the COVID-19 Outbreak in Wuhan, China
inferredcites →https://doi.org/10.1002/asi.22768openalex-snapshot
Sentiment analysis of twitter audiences: Measuring the positive or negative influence of popular twitterers
inferredcites →https://doi.org/10.1002/j.1681-4835.2010.tb00300.xopenalex-snapshot
Twitter as a Rapid Response News Service: An Exploration in the Context of the 2008 China Earthquake
inferredcites →https://doi.org/10.1007/s10618-015-0412-3openalex-snapshot
Efficient temporal mining of micro-blog texts and its application to event discovery
inferredcites →https://doi.org/10.1016/j.ajic.2015.02.023openalex-snapshot
What can we learn about the Ebola outbreak from tweets?
inferredcites →https://doi.org/10.1016/j.cose.2019.101710openalex-snapshot
Improving cyberbullying detection using Twitter users’ psychological features and machine learning
inferredcites →https://doi.org/10.1016/j.dss.2021.113577openalex-snapshot
Do President Trump's tweets affect financial markets?
inferredcites →https://doi.org/10.1016/j.jbi.2016.06.007openalex-snapshot
Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts
inferredcites →https://doi.org/10.1016/j.tele.2020.101531openalex-snapshot
Building the sustainable city through Twitter: Creative skilled migrants and innovative technology use
inferredcites →https://doi.org/10.1073/pnas.1607151113openalex-snapshot
Combining natural language processing and network analysis to examine how advocacy organizations stimulate conversation on social media
inferredcites →https://doi.org/10.1089/tmj.2009.9955openalex-snapshot
Twittering Healthcare: Social Media and Medicine
inferredcites →https://doi.org/10.1093/poq/nfw007openalex-snapshot
Fair and Balanced? Quantifying Media Bias through Crowdsourced Content Analysis
inferredcites →https://doi.org/10.1093/pubmed/fdaa060openalex-snapshot
Not all world leaders use Twitter in response to the COVID-19 pandemic: impact of the way of Angela Merkel on psychological distress, behaviour and risk perception
inferredcites →https://doi.org/10.1108/intr-04-2017-0172openalex-snapshot
Communicating effectively about CSR on Twitter
inferredcites →https://doi.org/10.1109/access.2020.3027350openalex-snapshot
Cross-Cultural Polarity and Emotion Detection Using Sentiment Analysis and Deep Learning on COVID-19 Related Tweets
inferredcites →https://doi.org/10.1109/mis.2012.76openalex-snapshot
How Social Media Will Change Public Health
inferredcites →https://doi.org/10.1109/tcds.2018.2826921openalex-snapshot
The Perception of Emotion in Artificial Agents
inferredcites →https://doi.org/10.1111/j.1467-9868.2009.00715.xopenalex-snapshot
Semiparametric Estimation and Inference for Distributional and General Treatment Effects
inferredcites →https://doi.org/10.1145/2309996.2310017openalex-snapshot
Understanding factors that affect response rates in twitter
inferredcites →https://doi.org/10.1177/0956797614557867openalex-snapshot
Psychological Language on Twitter Predicts County-Level Heart Disease Mortality
inferredcites →https://doi.org/10.1177/2053951720938405openalex-snapshot
Going viral: How a single tweet spawned a COVID-19 conspiracy theory on Twitter
inferredcites →https://doi.org/10.1287/isre.2018.0791openalex-snapshot
The Impact of Twitter Adoption on Lawmakers’ Voting Orientations
inferredcites →https://doi.org/10.1371/journal.pone.0104136openalex-snapshot
Twitter in the Cross Fire—The Use of Social Media in the Westgate Mall Terror Attack in Kenya
inferredcites →https://doi.org/10.1371/journal.pone.0190571openalex-snapshot
Primed for death: Law enforcement-citizen homicides, social media, and retaliatory violence
inferredcites →https://doi.org/10.1613/jair.4272openalex-snapshot
Sentiment Analysis of Short Informal Texts
inferredcites →https://doi.org/10.21105/joss.00037openalex-snapshot
tidytext: Text Mining and Analysis Using Tidy Data Principles in R
inferredcites →https://doi.org/10.2196/12264openalex-snapshot
Using Twitter to Detect Psychological Characteristics of Self-Identified Persons With Autism Spectrum Disorder: A Feasibility Study
inferredcites →https://doi.org/10.2196/18527openalex-snapshot
Causal Effect of Honorary Titles on Physicians’ Service Volumes in Online Health Communities: Retrospective Study
inferredcites →https://doi.org/10.2196/26720openalex-snapshot
Texas Public Agencies’ Tweets and Public Engagement During the COVID-19 Pandemic: Natural Language Processing Approach
inferredcites →https://doi.org/10.2196/jmir.9082openalex-snapshot
Understanding a Nonlinear Causal Relationship Between Rewards and Physicians’ Contributions in Online Health Care Communities: Longitudinal Study
inferredcites →https://doi.org/10.2196/publichealth.5789openalex-snapshot
Sentiment Analysis of Health Care Tweets: Review of the Methods Used
inferredcites →https://doi.org/10.2200/s00416ed1v01y201204hlt016openalex-snapshot
Sentiment Analysis and Opinion Mining
inferredcites →https://doi.org/10.3390/ijerph16132314openalex-snapshot
Identification of the Differential Effect of City-Level on the Gini Coefficient of Health Service Delivery in Online Health Community
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4157476openalex-extracted
Socio-Demographic Differences in Access to the Improving Access to Psychological Therapies Programme: A National Cohort Study
inferred← citeshttps://doi.org/10.1007/978-981-96-3774-4_1openalex-snapshot
Investigating Influential COVID-19 Perspectives: A Multifaceted Analysis of Twitter Discourse
inferred← citeshttps://doi.org/10.1016/j.ejor.2022.12.020openalex-snapshot
Constrained optimization for stratified treatment rules in reducing hospital readmission rates of diabetic patients
inferred← citeshttps://doi.org/10.1016/j.ejpsy.2025.100308openalex-snapshot
Association between neutrophil-to-lymphocyte ratio and bipolar disorder: A systematic review and meta-analysis
inferred← citeshttps://doi.org/10.1016/j.ins.2022.02.047openalex-snapshot
Constrained optimization for stratified treatment rules with multiple responses of survival data
inferred← citeshttps://doi.org/10.1016/j.ins.2022.08.024openalex-snapshot
Treatment effect identification using two-level designs with partially ignorable missing data
inferred← citeshttps://doi.org/10.1016/j.ipm.2023.103481openalex-snapshot
Identifying causal effects of the clinical sentiment of patients' nursing notes on anticipated fall risk stratification
inferred← citeshttps://doi.org/10.1016/j.nut.2024.112497openalex-snapshot
Tweeting about fatphobia and body shaming: A retrospective infodemiological study
inferred← citeshttps://doi.org/10.1017/s0033291723001010openalex-snapshot
Socio-demographic differences in access to psychological treatment services: evidence from a national cohort study
inferred← citeshttps://doi.org/10.1038/s41366-024-01689-yopenalex-snapshot
Whose tweets about obesity and weight loss gain the most attention: celebrities, political, or medical authorities?
inferred← citeshttps://doi.org/10.1038/s41598-025-90759-wopenalex-snapshot
Social media crisis communication and public engagement during COVID-19 analyzing public health and news media organizations’ tweeting strategies
inferred← citeshttps://doi.org/10.1108/oir-06-2025-0472openalex-snapshot
Emotions and sentiment in social media information sharing: a systematic review across domains and platforms
inferred← citeshttps://doi.org/10.1109/icabcd54961.2022.9856072openalex-snapshot
Sentiment Analysis of South African News Company
inferred← citeshttps://doi.org/10.2105/ajph.2023.307303openalex-snapshot
Stratified Simple Random Sampling Versus Volunteer Community-Wide Sampling for Estimates of COVID-19 Prevalence
inferred← citeshttps://doi.org/10.2196/59249openalex-snapshot
Analyzing Patient Experience on Weibo: Machine Learning Approach to Topic Modeling and Sentiment Analysis
inferred← citeshttps://doi.org/10.2196/preprints.40643openalex-snapshot
Sentiment Analysis on Tweets Related to Alopecia Areata, Hidradenitis Suppurativa, and Psoriasis: Revealing the Patient Experience of Disease (Preprint)
inferred← citeshttps://doi.org/10.2196/preprints.48274openalex-snapshot
Crisis communication on social media: A comparative study of COVID-19 tweets by four international public health organizations and four news media organizations and the public responses to the tweets (Preprint)
inferred← citeshttps://doi.org/10.3389/fmed.2022.996378openalex-snapshot
Sentiment analysis of tweets on alopecia areata, hidradenitis suppurativa, and psoriasis: Revealing the patient experience
inferred← citeshttps://doi.org/10.3389/fsoc.2023.1155849openalex-snapshot
Mapping obesity and diabetes’ representation on Twitter: the case of Italy
inferred← citeshttps://doi.org/10.3390/app151910359openalex-snapshot
The History of the #Rarediseaseday Campaign in Spanish on Twitter: Longitudinal Analysis of Hashtag Use and Social Network Analysis
inferred← citeshttps://doi.org/10.3390/bdcc6040130openalex-snapshot
A Space-Time Framework for Sentiment Scope Analysis in Social Media
inferred← citeshttps://doi.org/10.3390/healthcare11091275openalex-snapshot
A Study of Factors Influencing the Volume of Responses to Posts in Physician Online Community
inferred← citeshttps://doi.org/10.3390/info16080706openalex-snapshot
Empirical Research on the Influencing Factors and Causal Relationships of Enterprise Positive Topic Heat on Online Social Platforms
inferred← citeshttps://doi.org/10.3390/math11163461openalex-snapshot
Rumor Detection in Social Media Based on Multi-Hop Graphs and Differential Time Series
inferred← citeshttps://doi.org/10.3390/vaccines10122085openalex-snapshot
Balanced Reporting and Boomerang Effect: An Analysis of Croatian Online News Sites Vaccination Coverage and User Comments during the COVID-19 Pandemic
inferred← citeshttps://doi.org/10.48009/3_iis_2023_124openalex-snapshot
DETECTING COVID-19 MISINFORMATION ON SOCIAL MEDIA
inferred← citeshttps://doi.org/10.7717/peerj-cs.1693openalex-snapshot
Modeling emotional contagion in the COVID-19 pandemic: a complex network approach
inferred← citeshttps://doi.org/10.7812/tpp/23.142openalex-snapshot
COVID-19 Vaccine Information and Infertility Posts on X: Insights on a Misinformation Pandemic
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