More Like this

1. 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
2. Mining Twitter to assess the determinants of health behavior toward human papillomavirus vaccination in the United States

   https://doi.org/10.1093/jamia/ocz191  

   Zhang, Hansi ; Wheldon, Christopher ; Dunn, Adam G. ; Tao, Cui ; Huo, Jinhai ; Zhang, Rui ; Prosperi, Mattia ; Guo, Yi ; Bian, Jiang ( November 2019 , Journal of the American Medical Informatics Association)

   The study sought to test the feasibility of using Twitter data to assess determinants of consumers’ health behavior toward human papillomavirus (HPV) vaccination informed by the Integrated Behavior Model (IBM).

   We used 3 Twitter datasets spanning from 2014 to 2018. We preprocessed and geocoded the tweets, and then built a rule-based model that classified each tweet into either promotional information or consumers’ discussions. We applied topic modeling to discover major themes and subsequently explored the associations between the topics learned from consumers’ discussions and the responses of HPV-related questions in the Health Information National Trends Survey (HINTS).

   more »

   We collected 2 846 495 tweets and analyzed 335 681 geocoded tweets. Through topic modeling, we identified 122 high-quality topics. The most discussed consumer topic is “cervical cancer screening”; while in promotional tweets, the most popular topic is to increase awareness of “HPV causes cancer.” A total of 87 of the 122 topics are correlated between promotional information and consumers’ discussions. Guided by IBM, we examined the alignment between our Twitter findings and the results obtained from HINTS. Thirty-five topics can be mapped to HINTS questions by keywords, 112 topics can be mapped to IBM constructs, and 45 topics have statistically significant correlations with HINTS responses in terms of geographic distributions.

   Mining Twitter to assess consumers’ health behaviors can not only obtain results comparable to surveys, but also yield additional insights via a theory-driven approach. Limitations exist; nevertheless, these encouraging results impel us to develop innovative ways of leveraging social media in the changing health communication landscape.

   « less
3. 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
4. Regional Influenza Prediction with Sampling Twitter Data and PDE Model

   https://doi.org/10.3390/ijerph17030678  

   Wang, Yufang ; Xu, Kuai ; Kang, Yun ; Wang, Haiyan ; Wang, Feng ; Avram, Adrian ( February 2020 , International Journal of Environmental Research and Public Health)

   The large volume of geotagged Twitter streaming data on flu epidemics provides chances for researchers to explore, model, and predict the trends of flu cases in a timely manner. However, the explosive growth of data from social media makes data sampling a natural choice. In this paper, we develop a method for influenza prediction based on the real-time tweet data from social media, and this method ensures real-time prediction and is applicable to sampling data. Specifically, we first simulate the sampling process of flu tweets, and then develop a specific partial differential equation (PDE) model to characterize and predict themore »aggregated flu tweet volumes. Our PDE model incorporates the effects of flu spreading, flu recovery, and active human interventions for reducing flu. Our extensive simulation results show that this PDE model can almost eliminate the data reduction effects from the sampling process: It requires lesser historical data but achieves stronger prediction results with a relative accuracy of over 90% on the 1% sampling data. Even for the more aggressive data sampling ratios such as 0.1% and 0.01% sampling, our model is still able to achieve relative accuracies of 85% and 83%, respectively. These promising results highlight the ability of our mechanistic PDE model in predicting temporal–spatial patterns of flu trends even in the scenario of small sampling Twitter data.« less
5. #EngineersWeek: Broadening our Understanding of Community Engagement through Analysis of Twitter Use during the National Engineers Week.

   Malik, A ; Johri, A ; Handa, R ; Karbasian, H ; Purohit, H. ( July 2018 , Proceedings of 125th ASEE Annual Conference, Salt Lake City, USA.)

   Community engagement efforts have become an important avenue for raising public interest and know-how related to engineering. These efforts draw the young and the diverse into seeing engineering as a worthwhile profession. One such effort at the national level in the U.S. is the “National Engineers Week”. This is a week-long celebration held every February that consists of numerous events and activities organized for the general public with a focus towards students, women, and under-represented groups. In this paper, we examined this effort through the lens of social media and analyzed Twitter data collected for two hashtags used during themore »National Engineers Week 2017: “#eweek2017” and “#engineersweek”. Our dataset consisted of 6,583 original tweets and 10,885 retweets. To study the impact of the outreach we used three analytical approaches: descriptive analysis, content analysis, and network analysis. We found that the Twitter campaign participation was dominated by engineering companies and individual users followed by a limited participation of educational institutions, professional engineering associations, and non-profits. As opposed to other popular hashtag campaigns, not a single news media organization was identified as a participating user signaling a lower new media-driven propagation of the campaign among the public. From a content perspective, the tweets can be categorized as event promotion, showcasing employees of engineering companies, or encouraging and inspiring public (especially women and children) towards engineering. With the growing popularity of social media, community engagement efforts need to strategically leverage hashtags and other media elements for a broader impact.« less