Social Network Analysis of COVID-19 Sentiments: 10 Metropolitan Cities in Italy.

The pandemic spread rapidly across Italy, putting the region's health system on the brink of collapse, and generating concern regarding the government's capacity to respond to the needs of patients considering isolation measures. This study developed a sentiment analysis using millions of Twitter data during the first wave of the COVID-19 pandemic in 10 metropolitan cities in Italy's (1) north: Milan, Venice, Turin, Bologna; (2) central: Florence, Rome; (3) south: Naples, Bari; and (4) islands: Palermo, Cagliari. Questions addressed are as follows: (1) How did tweet-related sentiments change over the course of the COVID-19 pandemic, and (2) How did sentiments change when lagged with policy shifts and/or specific events? Findings show an assortment of differences and connections across Twitter sentiments (fear, anger, and joy) based on policy measures and geographies during the COVID-19 pandemic. Results can be used by policy makers to quantify the satisfactory level of positive/negative acceptance of decision makers and identify important topics related to COVID-19 policy measures, which can be useful for imposing geographically varying lockdowns and protective measures using historical data.

Spatial and Descriptive Analysis of Smoke and Vape Shop Locations Focusing on A Cancer Center Neighboring Catchment Area.

Tobacco products cause about 1 in 5 deaths premature deaths each year. With increased retailing of both tobacco and electronic nicotine delivery systems (ENDS) products, cancer centers such as City of Hope are prioritizing tobacco and ENDS control. Therefore, we conducted formative geospatial analyses of dedicated smoke and vape shops linked to neighborhood demographic characteristics. The objective of the study was to analyze local data on smoke and vaping shop locations by age, socio-economic status, and racial/ethnic group. Our geospatial analysis used aggregate data from the U.S. Census, Google Maps, and Yelp. Geospatial maps were created using ArcGIS Pro with American Community Survey and U.S. Census 2010. The distributions of exclusive tobacco and vaping shop locations data were overlaid with data from the U.S. Census 2010 to generate maps of the relative geographic distributions of shops across varying area demographic characteristics. Results showed that a higher concentration of exclusive smoke and vaping shops were in areas with a higher concentration of ethnic minorities and lower income and lower status neighborhoods. These findings suggest that laws and licensing should be evaluated to regulate the placement of these shops to reduce and even prevent targeting of minorities and other vulnerable populations.

A Spatio-Demographic Perspective on the Role of Social Determinants of Health and Chronic Disease in Determining a Population's Vulnerability to COVID-19.

INTRODUCTION: During the COVID-19 pandemic, health and social inequities placed racial and ethnic minority groups at increased risk of severe illness. Our objective was to investigate this health disparity by analyzing the relationship between potential social determinants of health (SDOH), COVID-19, and chronic disease in the spatial context of San Diego County, California. METHODS: We identified potential SDOH from a Pearson correlation analysis between socioeconomic variables and COVID-19 case rates during 5 pandemic stages, from March 31, 2020, to April 3, 2021. We used ridge regression to model chronic disease hospitalization and death rates by using the selected socioeconomic variables. Through the lens of COVID-19 and chronic disease, we identified vulnerable communities by using spatial methods, including Global Moran I spatial autocorrelation, local bivariate relationship analysis, and geographically weighted regression. RESULTS: In the Pearson correlation analysis, we identified 26 socioeconomic variables as potential SDOH because of their significance (P ≤ .05) in relation to COVID-19 case rates. Of the analyzed chronic disease rates, ridge regression most accurately modeled rates of diabetes age-adjusted death (R2 = 0.903) and age-adjusted hospitalization for hypertensive disease (hypertension, hypertensive heart disease, hypertensive chronic kidney disease, and hypertensive encephalopathy) (R2 = 0.952). COVID-19 and chronic disease rates exhibited positive spatial autocorrelation (0.304≤I≤0.561, 3.092≤Z≤6.548, 0.001≤P≤ .002), thereby justifying spatial models to highlight communities that are vulnerable to COVID-19. CONCLUSION: Novel spatial analysis methods reveal relationships between SDOH, COVID-19, and chronic disease that are intuitive and easily communicated to public health decision makers and practitioners. Observable disparity patterns between urban and rural areas and between affluent and low-income communities establish the need for spatially differentiated COVID-19 response approaches to achieve health equity.

Examining Vaccine Sentiment on Twitter and Local Vaccine Deployment during the COVID-19 Pandemic.

Understanding local public attitudes toward receiving vaccines is vital to successful vaccine campaigns. Social media platforms may help uncover vaccine sentiments during infectious disease outbreaks at the local level, and whether offline local events support vaccine-promotion efforts. Communication Infrastructure Theory (CIT) served as a guiding framework for this case study of the San Diego region examining local public sentiment toward vaccines expressed on Twitter during the COVID-19 pandemic. We performed a sentiment analysis (including positivity and subjectivity) of 187,349 tweets gathered from May 2020 to March 2021, and examined how sentiment corresponded with local vaccine deployment. The months of November and December (52.9%) 2020 saw a majority of tweets expressing positive sentiment and coincided with announcements of offline local events signaling San Diego's imminent deployment of COVID-19 vaccines. Across all months, tweets remained mostly objective (never falling below 63%). In terms of CIT, considering multiple levels of the Story Telling Network in online spaces, and examining sentiment about vaccines on Twitter may help scholars to explore the Communication Action Context, as well as cultivate positive community attitudes to improve the Field of Health Action regarding vaccines. Real-time analysis of local tweets during development and deployment of new vaccines may help monitor local public responses and guide promotion of immunizations in communities.

Disentangling Racial, Ethnic, and Socioeconomic Disparities in Treatment for Colorectal Cancer.

BACKGROUND: Colorectal cancer is curable if diagnosed early and treated properly. Black and Hispanic patients with colorectal cancer are more likely to experience treatment delays and/or receive lower standards of care. Socioeconomic deprivation may contribute to these disparities, but this has not been extensively quantified. We studied the interrelationship between patient race/ethnicity and neighborhood socioeconomic status (nSES) on receipt of timely appropriate treatment among patients with colorectal cancer in California. METHODS: White, Black, and Hispanic patients (26,870) diagnosed with stage I-III colorectal cancer (2009-2013) in the California Cancer Registry were included. Logistic regression models were used to examine the association of race/ethnicity and nSES with three outcomes: undertreatment, >60-day treatment delay, and >90-day treatment delay. Joint effect models and mediation analysis were used to explore the interrelationships between race/ethnicity and nSES. RESULTS: Hispanics and Blacks were at increased risk for undertreatment [Black OR = 1.39; 95% confidence interval (CI) = 1.23-1.57; Hispanic OR = 1.17; 95% CI = 1.08-1.27] and treatment delay (Black/60-day OR = 1.78; 95% CI = 1.57-2.02; Hispanic/60-day OR = 1.50; 95% CI = 1.38-1.64) compared with Whites. Of the total effect (OR = 1.15; 95% CI = 1.07-1.24) of non-white race on undertreatment, 45.71% was explained by nSES. CONCLUSIONS: Lower nSES patients of any race were at substantially higher risk for undertreatment and treatment delay, and racial/ethnic disparities are reduced or eliminated among non-white patients living in the highest SES neighborhoods. Racial and ethnic disparities persisted after accounting for neighborhood socioeconomic status, and between the two, race/ethnicity explained a larger portion of the total effects. IMPACT: This research improves our understanding of how socioeconomic deprivation contributes to racial/ethnic disparities in colorectal cancer.

Twitter Communication During an Outbreak of Hepatitis A in San Diego, 2016-2018.

Objectives. To examine how and what information is communicated via social media during an infectious disease outbreak.Methods. In the context of the 2016 through 2018 hepatitis A outbreak in San Diego County, California, we used a grounded theory-based thematic analysis that employed qualitative and quantitative approaches to uncover themes in a sample of public tweets (n = 744) from Twitter, a primary platform used by key stakeholders to communicate to the public during the outbreak.Results. Tweets contained both general and hepatitis A-specific information related to the outbreak, restatements of policy and comments critical of government responses to the outbreak, information with the potential to shape risk perceptions, and expressions of concern regarding individuals experiencing homelessness and their role in spreading hepatitis A. We also identified misinformation and common channels of content driving themes that emerged in our sample.Conclusions. Public health professionals may identify real-time public risk perceptions and concerns via social media during an outbreak and target responses that fulfill the informational needs of those who seek direction and reassurance during times of uncertainty.

A "fitness" Theme May Mitigate Regional Prevalence of Overweight and Obesity: Evidence from Google Search and Tweets.

Taking ecological perspectives to overweight and obesity, the current study applies data mining approach to examine the association between information and social environments and regional prevalence of overweight and obesity. In particular, we focus on online search and social media data since the increasing popularity of location-based geo-targeting could be an influential source of regional differences in health information and social environment. In Study 1, we calculated the correlation between regional overweight and obesity rates with regional Google searches for a time period of 12 years (2004 to 2016). The findings showed that in regions with high overweight and obesity rates, people were looking for and obtaining information on weight-loss and diet;, but in regions with low overweight and obesity rates, people were looking for and obtaining information on fitness services and facilities. In Study 2, we analyzed and compared 4010 tweets from Houston, a city with a high overweight and obesity rate, and 3281 tweets from San Diego, a city with a low overweight and obesity rate. The tweets were collected from August 2015 to August of 2016. We analyzed the textual content of tweets by word frequency analysis and topic modeling. The findings suggest that San Diego has a social environment that focuses on fitness and combining exercising with dieting. In contrast, Houston's social environment emphasizes dieting. The implication of these findings is that health practitioners should push a paradigm shift to a stronger focus on "healthy life" (combining exercising and dieting) in regions with high overweight and obesity rates.

Twitter-based measures of neighborhood sentiment as predictors of residential population health.

Several studies have recently applied sentiment-based lexicons to Twitter to gauge local sentiment to understand health behaviors and outcomes for local areas. While this research has demonstrated the vast potential of this approach, lingering questions remain regarding the validity of Twitter mining and surveillance in local health research. First, how well does this approach predict health outcomes at very local scales, such as neighborhoods? Second, how robust are the findings garnered from sentiment signals when accounting for spatial effects? To evaluate these questions, we link 2,076,025 tweets from 66,219 distinct users in the city of San Diego over the period of 2014-12-06 to 2017-05-24 to the 500 Cities Project data and 2010-2014 American Community Survey data. We determine how well sentiment predicts self-rated mental health, sleep quality, and heart disease at a census tract level, controlling for neighborhood characteristics and spatial autocorrelation. We find that sentiment is related to some outcomes on its own, but these relationships are not present when controlling for other neighborhood factors. Evaluating our encoding strategy more closely, we discuss the limitations of existing measures of neighborhood sentiment, calling for more attention to how race/ethnicity and socio-economic status play into inferences drawn from such measures.

"Okay, We Get It. You Vape": An Analysis of Geocoded Content, Context, and Sentiment regarding E-Cigarettes on Twitter.

The current study examined conversations on Twitter related to use and perceptions of e-cigarettes in the United States. We employed the Social Media Analytic and Research Testbed (SMART) dashboard, which was used to identify and download (via a public API) e-cigarette-related geocoded tweets. E-cigarette-related tweets were collected continuously using customized geo-targeted Twitter APIs. A total of 193,051 tweets were collected between October 2015 and February 2016. Of these tweets, a random sample of 973 geocoded tweets were selected and manually coded for information regarding source, context, and message characteristics. Our findings reveal that although over half of tweets were positive, a sizeable portion was negative or neutral. We also found that, among those tweets mentioning a stigma of e-cigarettes, most confirmed that a stigma does exist. Conversely, among tweets mentioning the harmfulness of e-cigarettes, most denied that e-cigarettes were a health hazard. These results suggest that current efforts have left the public with ambiguity regarding the potential dangers of e-cigarettes. Consequently, it is critical to communicate the public health stance on this issue to inform the public and provide counterarguments to the positive sentiments presently dominating conversations about e-cigarettes on social media. The lack of awareness and need to voice a public health position on e-cigarettes represents a vital opportunity to continue winning gains for tobacco control and prevention efforts through health communication interventions targeting e-cigarettes.

Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge.

BACKGROUND: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season. METHODS: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). RESULTS: Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones. CONCLUSION: Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts.

Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza.

Traditional methods for monitoring influenza are haphazard and lack fine-grained details regarding the spatial and temporal dynamics of outbreaks. Twitter gives researchers and public health officials an opportunity to examine the spread of influenza in real-time and at multiple geographical scales. In this paper, we introduce an improved framework for monitoring influenza outbreaks using the social media platform Twitter. Relying upon techniques from geographic information science (GIS) and data mining, Twitter messages were collected, filtered, and analyzed for the thirty most populated cities in the United States during the 2013-2014 flu season. The results of this procedure are compared with national, regional, and local flu outbreak reports, revealing a statistically significant correlation between the two data sources. The main contribution of this paper is to introduce a comprehensive data mining process that enhances previous attempts to accurately identify tweets related to influenza. Additionally, geographical information systems allow us to target, filter, and normalize Twitter messages.

Using Social Media to Detect Outdoor Air Pollution and Monitor Air Quality Index (AQI): A Geo-Targeted Spatiotemporal Analysis Framework with Sina Weibo (Chinese Twitter).

Outdoor air pollution is a serious problem in many developing countries today. This study focuses on monitoring the dynamic changes of air quality effectively in large cities by analyzing the spatiotemporal trends in geo-targeted social media messages with comprehensive big data filtering procedures. We introduce a new social media analytic framework to (1) investigate the relationship between air pollution topics posted in Sina Weibo (Chinese Twitter) and the daily Air Quality Index (AQI) published by China's Ministry of Environmental Protection; and (2) monitor the dynamics of air quality index by using social media messages. Correlation analysis was used to compare the connections between discussion trends in social media messages and the temporal changes in the AQI during 2012. We categorized relevant messages into three types, retweets, mobile app messages, and original individual messages finding that original individual messages had the highest correlation to the Air Quality Index. Based on this correlation analysis, individual messages were used to monitor the AQI in 2013. Our study indicates that the filtered social media messages are strongly correlated to the AQI and can be used to monitor the air quality dynamics to some extent.

Do Global Cities Enable Global Views? Using Twitter to Quantify the Level of Geographical Awareness of U.S. Cities.

Dynamic social media content, such as Twitter messages, can be used to examine individuals' beliefs and perceptions. By analyzing Twitter messages, this study examines how Twitter users exchanged and recognized toponyms (city names) for different cities in the United States. The frequency and variety of city names found in their online conversations were used to identify the unique spatiotemporal patterns of "geographical awareness" for Twitter users. A new analytic method, Knowledge Discovery in Cyberspace for Geographical Awareness (KDCGA), is introduced to help identify the dynamic spatiotemporal patterns of geographic awareness among social media conversations. Twitter data were collected across 50 U.S. cities. Thousands of city names around the world were extracted from a large volume of Twitter messages (over 5 million tweets) by using the Twitter Application Programming Interface (APIs) and Python language computer programs. The percentages of distant city names (cities located in distant states or other countries far away from the locations of Twitter users) were used to estimate the level of global geographical awareness for Twitter users in each U.S. city. A Global awareness index (GAI) was developed to quantify the level of geographical awareness of Twitter users from within the same city. Our findings are that: (1) the level of geographical awareness varies depending on when and where Twitter messages are posted, yet Twitter users from big cities are more aware of the names of international cities or distant US cities than users from mid-size cities; (2) Twitter users have an increased awareness of other city names far away from their home city during holiday seasons; and (3) Twitter users are more aware of nearby city names than distant city names, and more aware of big city names rather than small city names.

The reliability of tweets as a supplementary method of seasonal influenza surveillance.

BACKGROUND: Existing influenza surveillance in the United States is focused on the collection of data from sentinel physicians and hospitals; however, the compilation and distribution of reports are usually delayed by up to 2 weeks. With the popularity of social media growing, the Internet is a source for syndromic surveillance due to the availability of large amounts of data. In this study, tweets, or posts of 140 characters or less, from the website Twitter were collected and analyzed for their potential as surveillance for seasonal influenza. OBJECTIVE: There were three aims: (1) to improve the correlation of tweets to sentinel-provided influenza-like illness (ILI) rates by city through filtering and a machine-learning classifier, (2) to observe correlations of tweets for emergency department ILI rates by city, and (3) to explore correlations for tweets to laboratory-confirmed influenza cases in San Diego. METHODS: Tweets containing the keyword "flu" were collected within a 17-mile radius from 11 US cities selected for population and availability of ILI data. At the end of the collection period, 159,802 tweets were used for correlation analyses with sentinel-provided ILI and emergency department ILI rates as reported by the corresponding city or county health department. Two separate methods were used to observe correlations between tweets and ILI rates: filtering the tweets by type (non-retweets, retweets, tweets with a URL, tweets without a URL), and the use of a machine-learning classifier that determined whether a tweet was "valid", or from a user who was likely ill with the flu. RESULTS: Correlations varied by city but general trends were observed. Non-retweets and tweets without a URL had higher and more significant (P<.05) correlations than retweets and tweets with a URL. Correlations of tweets to emergency department ILI rates were higher than the correlations observed for sentinel-provided ILI for most of the cities. The machine-learning classifier yielded the highest correlations for many of the cities when using the sentinel-provided or emergency department ILI as well as the number of laboratory-confirmed influenza cases in San Diego. High correlation values (r=.93) with significance at P<.001 were observed for laboratory-confirmed influenza cases for most categories and tweets determined to be valid by the classifier. CONCLUSIONS: Compared to tweet analyses in the previous influenza season, this study demonstrated increased accuracy in using Twitter as a supplementary surveillance tool for influenza as better filtering and classification methods yielded higher correlations for the 2013-2014 influenza season than those found for tweets in the previous influenza season, where emergency department ILI rates were better correlated to tweets than sentinel-provided ILI rates. Further investigations in the field would require expansion with regard to the location that the tweets are collected from, as well as the availability of more ILI data.

The complex relationship of realspace events and messages in cyberspace: case study of influenza and pertussis using tweets.

BACKGROUND: Surveillance plays a vital role in disease detection, but traditional methods of collecting patient data, reporting to health officials, and compiling reports are costly and time consuming. In recent years, syndromic surveillance tools have expanded and researchers are able to exploit the vast amount of data available in real time on the Internet at minimal cost. Many data sources for infoveillance exist, but this study focuses on status updates (tweets) from the Twitter microblogging website. OBJECTIVE: The aim of this study was to explore the interaction between cyberspace message activity, measured by keyword-specific tweets, and real world occurrences of influenza and pertussis. Tweets were aggregated by week and compared to weekly influenza-like illness (ILI) and weekly pertussis incidence. The potential effect of tweet type was analyzed by categorizing tweets into 4 categories: nonretweets, retweets, tweets with a URL Web address, and tweets without a URL Web address. METHODS: Tweets were collected within a 17-mile radius of 11 US cities chosen on the basis of population size and the availability of disease data. Influenza analysis involved all 11 cities. Pertussis analysis was based on the 2 cities nearest to the Washington State pertussis outbreak (Seattle, WA and Portland, OR). Tweet collection resulted in 161,821 flu, 6174 influenza, 160 pertussis, and 1167 whooping cough tweets. The correlation coefficients between tweets or subgroups of tweets and disease occurrence were calculated and trends were presented graphically. RESULTS: Correlations between weekly aggregated tweets and disease occurrence varied greatly, but were relatively strong in some areas. In general, correlation coefficients were stronger in the flu analysis compared to the pertussis analysis. Within each analysis, flu tweets were more strongly correlated with ILI rates than influenza tweets, and whooping cough tweets correlated more strongly with pertussis incidence than pertussis tweets. Nonretweets correlated more with disease occurrence than retweets, and tweets without a URL Web address correlated better with actual incidence than those with a URL Web address primarily for the flu tweets. CONCLUSIONS: This study demonstrates that not only does keyword choice play an important role in how well tweets correlate with disease occurrence, but that the subgroup of tweets used for analysis is also important. This exploratory work shows potential in the use of tweets for infoveillance, but continued efforts are needed to further refine research methods in this field.