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1. Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis

   https://doi.org/10.2196/30765  

   Monselise, Michal ; Chang, Chia-Hsuan ; Ferreira, Gustavo ; Yang, Rita ; Yang, Christopher C ( January 2021 , Journal of Medical Internet Research)

   Background As a number of vaccines for COVID-19 are given emergency use authorization by local health agencies and are being administered in multiple countries, it is crucial to gain public trust in these vaccines to ensure herd immunity through vaccination. One way to gauge public sentiment regarding vaccines for the goal of increasing vaccination rates is by analyzing social media such as Twitter. Objective The goal of this research was to understand public sentiment toward COVID-19 vaccines by analyzing discussions about the vaccines on social media for a period of 60 days when the vaccines were started in the Unitedmore »States. Using the combination of topic detection and sentiment analysis, we identified different types of concerns regarding vaccines that were expressed by different groups of the public on social media. Methods To better understand public sentiment, we collected tweets for exactly 60 days starting from December 16, 2020 that contained hashtags or keywords related to COVID-19 vaccines. We detected and analyzed different topics of discussion of these tweets as well as their emotional content. Vaccine topics were identified by nonnegative matrix factorization, and emotional content was identified using the Valence Aware Dictionary and sEntiment Reasoner sentiment analysis library as well as by using sentence bidirectional encoder representations from transformer embeddings and comparing the embedding to different emotions using cosine similarity. Results After removing all duplicates and retweets, 7,948,886 tweets were collected during the 60-day time period. Topic modeling resulted in 50 topics; of those, we selected 12 topics with the highest volume of tweets for analysis. Administration and access to vaccines were some of the major concerns of the public. Additionally, we classified the tweets in each topic into 1 of the 5 emotions and found fear to be the leading emotion in the tweets, followed by joy. Conclusions This research focused not only on negative emotions that may have led to vaccine hesitancy but also on positive emotions toward the vaccine. By identifying both positive and negative emotions, we were able to identify the public's response to the vaccines overall and to news events related to the vaccines. These results are useful for developing plans for disseminating authoritative health information and for better communication to build understanding and trust.« less
2. A System Analytics Framework for Detecting Infrastructure-Related Topics in Disasters Using Social Sensing

   Fan, Chao ; Mostafavi, Ali ; Gupta, Aayush ; Zhang, Cheng ( January 2018 , 25th International Workshop on Intelligent Computing in Engineering (EG-ICE))

   The objective of this paper is to propose and test a system analytics framework based on social sensing and text mining to detect topic evolution associated with the performance of infrastructure systems in disasters. Social media, like Twitter, as active channels of communication and information dissemination, provide insights into real-time information and first-hand experience from affected areas in mass emergencies. While the existing studies show the importance of social sensing in improving situational awareness and emergency response in disasters, the use of social sensing for detection and analysis of infrastructure systems and their resilience performance has been rather limited. Thismore »limitation is due to the lack of frameworks to model the events and topics (e.g., grid interruption and road closure) evolution associated with infrastructure systems (e.g., power, highway, airport, and oil) in times of disasters. The proposed framework detects infrastructure-related topics of the tweets posted in disasters and their evolutions by integrating searching relevant keywords, text lemmatization, Part-of-Speech (POS) tagging, TF-IDF vectorization, topic modeling by using Latent Dirichlet Allocation (LDA), and K-Means clustering. The application of the proposed framework was demonstrated in a study of infrastructure systems in Houston during Hurricane Harvey. In this case study, more than sixty thousand tweets were retrieved from 150-mile radius in Houston over 39 days. The analysis of topic detection and evolution from user-generated data were conducted, and the clusters of tweets pertaining to certain topics were mapped in networks over time. The results show that the proposed framework enables to summarize topics and track the movement of situations in different disaster phases. The analytics elements of the proposed framework can improve the recognition of infrastructure performance through text-based representation and provide evidence for decision-makers to take actionable measurements.« less
3. Comparison of Social Media, Syndromic Surveillance, and Microbiologic Acute Respiratory Infection Data: Observational Study

   https://doi.org/10.2196/14986  

   Daughton, Ashlynn R ; Chunara, Rumi ; Paul, Michael J ( January 2020 , JMIR Public Health and Surveillance)

   Background Internet data can be used to improve infectious disease models. However, the representativeness and individual-level validity of internet-derived measures are largely unexplored as this requires ground truth data for study. Objective This study sought to identify relationships between Web-based behaviors and/or conversation topics and health status using a ground truth, survey-based dataset. Methods This study leveraged a unique dataset of self-reported surveys, microbiological laboratory tests, and social media data from the same individuals toward understanding the validity of individual-level constructs pertaining to influenza-like illness in social media data. Logistic regression models were used to identify illness in Twitter postsmore »using user posting behaviors and topic model features extracted from users’ tweets. Results Of 396 original study participants, only 81 met the inclusion criteria for this study. Of these participants’ tweets, we identified only two instances that were related to health and occurred within 2 weeks (before or after) of a survey indicating symptoms. It was not possible to predict when participants reported symptoms using features derived from topic models (area under the curve [AUC]=0.51; P=.38), though it was possible using behavior features, albeit with a very small effect size (AUC=0.53; P≤.001). Individual symptoms were also generally not predictable either. The study sample and a random sample from Twitter are predictably different on held-out data (AUC=0.67; P≤.001), meaning that the content posted by people who participated in this study was predictably different from that posted by random Twitter users. Individuals in the random sample and the GoViral sample used Twitter with similar frequencies (similar @ mentions, number of tweets, and number of retweets; AUC=0.50; P=.19). Conclusions To our knowledge, this is the first instance of an attempt to use a ground truth dataset to validate infectious disease observations in social media data. The lack of signal, the lack of predictability among behaviors or topics, and the demonstrated volunteer bias in the study population are important findings for the large and growing body of disease surveillance using internet-sourced data.« less
4. Data-driven inferences of agency-level risk and response communication on COVID-19 through social media-based interactions

   https://doi.org/10.5055/jem.0589  

   Ahmed, PhD Candidate ; Sadri, PhD ; Amini, PhD ( October 2021 , Journal of Emergency Management)

   Risk perception and risk averting behaviors of public agencies in the emergence and spread of COVID-19 can be retrieved through online social media (Twitter), and such interactions can be echoed in other information outlets. This study collected time-sensitive online social media data and analyzed patterns of health risk communication of public health and emergency agencies in the emergence and spread of novel coronavirus using data-driven methods. The major focus is toward understanding how policy-making agencies communicate risk and response information through social media during a pandemic and influence community response—ie, timing of lockdown, timing of reopening, etc.—and disease outbreak indicators—ie,more »number of confirmed cases and number of deaths. Twitter data of six major public organizations (1,000-4,500 tweets per organization) are collected from February 21, 2020 to June 6, 2020. Several machine learning algorithms, including dynamic topic model and sentiment analysis, are applied over time to identify the topic dynamics over the specific timeline of the pandemic. Organizations emphasized on various topics—eg, importance of wearing face mask, home quarantine, understanding the symptoms, social distancing and contact tracing, emerging community transmission, lack of personal protective equipment, COVID-19 testing and medical supplies, effect of tobacco, pandemic stress management, increasing hospitalization rate, upcoming hurricane season, use of convalescent plasma for COVID-19 treatment, maintaining hygiene, and the role of healthcare podcast in different timeline. The findings can benefit emergency management, policymakers, and public health agencies to identify targeted information dissemination policies for public with diverse needs based on how local, federal, and international agencies reacted to COVID-19.« less
5. Examining risk and crisis communications of government agencies and stakeholders during early stages of COVID-19 on Twitter

   https://doi.org/10.1016/j.chb.2020.106568  

   Wang, Yan ; Hao, Haiyan ; Platt, Lisa S. ( September 2020 , Computers in human behavior)

   During COVID-19, social media has played an important role for public health agencies and government stakeholders (i.e. actors) to disseminate information regarding situations, risks, and personal protective action inhibiting disease spread. However, there have been notable insufficient, incongruent, and inconsistent communications regarding the pandemic and its risks, which was especially salient at the early stages of the outbreak. Sufficiency, congruence and consistency in health risk communication have important implications for effective health safety instruction as well as critical content interpretability and recall. It also impacts individual- and community-level responses to information. This research employs text mining techniques and dynamic networkmore »analysis to investigate the actors’ risk and crisis communication on Twitter regarding message types, communication sufficiency, timeliness, congruence, consistency and coordination. We studied 13,598 pandemic-relevant tweets posted over January to April from 67 federal and state-level agencies and stakeholders in the U.S. The study annotates 16 categories of message types, analyzes their appearances and evolutions. The research then identifies inconsistencies and incongruencies on four critical topics and examines spatial disparities, timeliness, and sufficiency across actors and message types in communicating COVID-19. The network analysis also reveals increased communication coordination over time. The findings provide unprecedented insight of Twitter COVID-19 information dissemination which may help to inform public health agencies and governmental stakeholders future risk and crisis communication strategies related to global hazards in digital environments.« less