scholarly journals Measuring global multi-scale place connectivity using geotagged social media data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhenlong Li ◽  
Xiao Huang ◽  
Xinyue Ye ◽  
Yuqin Jiang ◽  
Yago Martin ◽  
...  
Keyword(s):  
Social Media ◽  
Early Stage ◽  
Boundary Effect ◽  
Positive Association ◽  
Human Movement ◽  
Connectivity Index ◽  
Contextual Knowledge ◽  
Spatial Interactions ◽  
Multi Scale ◽  
The Us

AbstractShaped by human movement, place connectivity is quantified by the strength of spatial interactions among locations. For decades, spatial scientists have researched place connectivity, applications, and metrics. The growing popularity of social media provides a new data stream where spatial social interaction measures are largely devoid of privacy issues, easily assessable, and harmonized. In this study, we introduced a global multi-scale place connectivity index (PCI) based on spatial interactions among places revealed by geotagged tweets as a spatiotemporal-continuous and easy-to-implement measurement. The multi-scale PCI, demonstrated at the US county level, exhibits a strong positive association with SafeGraph population movement records (10% penetration in the US population) and Facebook’s social connectedness index (SCI), a popular connectivity index based on social networks. We found that PCI has a strong boundary effect and that it generally follows the distance decay, although this force is weaker in more urbanized counties with a denser population. Our investigation further suggests that PCI has great potential in addressing real-world problems that require place connectivity knowledge, exemplified with two applications: (1) modeling the spatial spread of COVID-19 during the early stage of the pandemic and (2) modeling hurricane evacuation destination choice. The methodological and contextual knowledge of PCI, together with the open-sourced PCI datasets at various geographic levels, are expected to support research fields requiring knowledge in human spatial interactions.

scholarly journals Prevalence and source analysis of COVID-19 misinformation of 138 countries

2021 ◽  
Author(s):  
Md. Sayeed Al-Zaman
Keyword(s):  
Social Media ◽  
Positive Association ◽  
The Philippines ◽  
Source Analysis ◽  
Mainstream Media ◽  
Content Analysis Method ◽  
The Us ◽  
Political Conservatism ◽  
Social Media Platforms ◽  
Internet Literacy

This study analyzed 9,657 pieces of misinformation that originated in 138 countries and fact-checked by 94 organizations. Collected from Poynter Institute's official website and following a quantitative content analysis method along with descriptive statistical analysis, this research produces some novel insights regarding COVID-19 misinformation. The findings show that India (15.94%), the US (9.74%), Brazil (8.57%), and Spain (8.03%) are the four most misinformation-affected countries. Based on the results, it is presumed that the prevalence of COVID-19 misinformation can have a positive association with the COVID-19 situation. Social media (84.94%) produces the highest amount of misinformation, and the internet (90.5%) as a whole is responsible for most of the COVID-19 misinformation. Moreover, Facebook alone produces 66.87% misinformation among all social media platforms. Of all countries, India (18.07%) produced the highest amount of social media misinformation, perhaps thanks to the country's higher internet penetration rate, increasing social media consumption, and users' lack of internet literacy. On the other hand, countries like Turkey, the US, Brazil, and the Philippines where either political control over media is intense or political conservatism is apparent, experienced a higher amount of misinformation from mainstream media, political figures, and celebrities. Although the prevalence of misinformation was the highest in March 2020, given the present trends, it may likely to increase slightly in 2021.

scholarly journals Early Stage Prediction of US County Vulnerability to the COVID-19 Pandemic

2020 ◽  
Author(s):  
Mihir Mehta ◽  
Juxihong Julaiti ◽  
Paul Griffin ◽  
Soundar Kumara
Keyword(s):  
Early Stage ◽  
Positive Association ◽  
Disease Spread ◽  
County Level ◽  
Effective Response ◽  
Model Study ◽  
Decision Analytical Model ◽  
Rapid Spread ◽  
The Us ◽  
Using Data

AbstractImportanceThe rapid spread of COVID-19 means that government and health services providers have little time to plan and design effective response policies. It is therefore important to rapidly provide accurate predictions of how vulnerable geographic regions such as counties are to the spread.ObjectiveDeveloping county level prediction around near future disease movement for COVID-19 occurrences using publicly available data.DesignOriginal Investigation; Decision Analytical Model Study for County Level COVID-19 occurrences using data from March 14-31, 2020.SettingDisease spread prediction for US counties.ParticipantsAll US county level granularity based on data fused from multiple publicly available sources inclusive of health statistics, demographics, and geographical features.Exposure(s) (for observational studies)Daily county level reported COVID-19 occurrences from March 14-31, 2020.Main Outcome(s) and Measure(s)We developed a 3-stage model to quantify, firstly the probability of COVID-19 occurrence for unaffected counties using XGBoost classifier and secondly, the number of potential occurrences of a county via XGBoost regression. Thirdly, these results are combined to compute the county level risk. This risk is then used as an estimated after-five-day-vulnerability of the county.ResultsUsing data from March 14-31, 2020, the model shows a sensitivity over 71.5% and specificity over 94%.Conclusions and RelevanceWe found that population, population density, percentage of people aged 70 or greater and prevalence of comorbidities play an important role in predicting COVID-19 occurrences. We found a positive association between affected and urban counties as well as less vulnerable and rural counties. The developed model can be used for identification of vulnerable counties and potential data discrepancies. Limited testing facilities and delayed results introduces significant variation in reported cases and produces a bias in the model.Trial RegistrationNot ApplicableKey PointsQuestionWhat are key factors that define the vulnerability of counties in the US to cases of the COVID-19 virus?FindingsIn this epidemiological study based on publicly available data, we develop a model that predicts vulnerability to COVID-19 for each US county in terms of likelihood of going from no documented cases to at least one case within five days and in terms of number of occurrences of the virus.MeaningPredicting county vulnerability to COVID-19 can assist health organizations to better plan for resource and workforce needs.

scholarly journals The digital life of the #migrantcaravan: Contextualizing Twitter as a spatial technology

2020 ◽  
Vol 7 (2) ◽  
pp. 205395172097848
Author(s):  
Margath A Walker ◽  
Emmanuel Frimpong Boamah
Keyword(s):  
United States ◽  
Social Media ◽  
The United States ◽  
Central American ◽  
Contextual Knowledge ◽  
Digital World ◽  
The Us ◽  
And Migration ◽  
United States Mexico ◽  
Spatial Technology

The Central American migrant caravans of 2018 are best understood as having been precipitated by entangled multi-scalar geopolitical histories among the United States, Mexico, Guatemala, Honduras, and El Salvador. Unsurprisingly, the migrants traveling north to the United States garnered widespread attention on social media. So much so that the reaction to the caravan accelerated plans to deploy troops to the US southern border and deny Central Americans the opportunity to seek asylum. This example showcases how the digital world can have exponential material effects. While coverage on border security and migration has been extensive, within political geography, such concerns have rarely been paired with social media. In this article, we take as our object of analysis the digitality or “digital life” of the migrant caravan. Mapping the patterns of migrant caravan-related tweeting paired with the exploration of Twitter’s networked dimensions reveals the platform to be a fundamentally spatial technology. Rather than reflect, refract or distort, Twitter produces and (its power) is in turn produced through spatial mechanisms. We present multiple cartographic visualizations in support of this claim and highlight the ways in which a contextual knowledge of the subject under study—the migrant caravan—can further inform analyses of Big Data.

TTIP, CETA, and TiSA Behind Closed Doors

2017 ◽  
Author(s):  
Panagiotis Delimatsis
Keyword(s):  
European Union ◽  
Global Governance ◽  
Early Stage ◽  
European Parliament ◽  
Trade Negotiations ◽  
The European Union ◽  
The Us ◽  
The Eu

Secrecy and informality rather than transparency traditionally reign trade negotiations at the bilateral, regional, and multilateral levels. Yet, transparency ranks among the most basic desiderata in the grammar of global governance and has been regarded as positively related to legitimacy. In the EU’s case, transparent trade diplomacy is quintessential for constitutional—but also for broader political—reasons. First, even if trade matters fall within the EU’s exclusive competence, the EU executive is bound by the Treaty on the Functioning of the European Union (TFEU) to inform the European Parliament, the EU co-legislator, in regular intervals. Second, transparency at an early stage is important to address public reluctance, suspicion, or even opposition regarding a particular trade deal. This chapter chronicles the quest for and turning moments relating to transparency during the EU trade negotiations with Canada (CETA); the US (TTIP), and various WTO members on services (TiSA).

Social media engagement, service complexity, and experiential quality in US hospitals

2020 ◽  
Vol 34 (6) ◽  
pp. 833-845 ◽  
Author(s):  
Youngsu Lee ◽  
Joonhwan In ◽  
Seung Jun Lee
Keyword(s):  
Social Media ◽  
Demographic Data ◽  
Positive Association ◽  
Customer Engagement ◽  
Service Firms ◽  
Multiple Sources ◽  
Cross Sectional ◽  
Content Type ◽  
Social Media Engagement ◽  
Experiential Quality

Purpose As social media platforms become increasingly popular among service firms, many US hospitals have been using social media as a means to improve their patients’ experiences. However, little research has explored the implications of social media use within a hospital context. The purpose of this paper is to investigate a hospital’s customer engagement through social media and its association with customers’ experiential quality. Also, this study examines the role of a hospital’s service characteristics, which could shape the nature of the interactions between patients and the hospital. Design/methodology/approach Data from 669 hospitals with complete experiential quality and demographic data were collected from multiple sources of secondary data, including the rankings of social media friendly hospitals, the Hospital Compare database, the Center for Medicare and Medicaid (CMS) cost report, the CMS impact file, the Healthcare Information and Management Systems Society Analytics database and the Dartmouth Atlas of Health Care. Specifically, the authors designed the instrumental variable estimate to address the endogeneity issue. Findings The empirical results suggest a positive association between a hospital’s social media engagement and experiential quality. For hospitals with a high level of service sophistication, the association between online engagement and experiential quality becomes more salient. For hospitals offering various services, offline engagement is a critical predictor of experiential quality. Research limitations/implications A hospital with more complex services should make efforts to engage customers through social media for better patient experiences. The sample is selected from databases in the US, and the databases are cross-sectional in nature. Practical implications Not all hospitals may be better off improving the patient experience by engaging customers through social media. Therefore, practitioners should exercise caution in applying the study’s results to other contexts and in making causal inferences. Originality/value The current study delineates customer engagement through social media into online and offline customer engagement. This study is based on the theory of customer engagement and reflects the development of mobile technology. Moreover, this research may be considered as pioneering in that it considers the key characteristics of a hospital’s service operations (i.e., service complexity) when discovering the link between customers’ engagement through a hospital’s social media and experiential quality.

scholarly journals Public Awareness: What Climate Change Scientists Should Consider

2020 ◽  
Vol 12 (20) ◽  
pp. 8369
Author(s):  
Mohammad Rahimi
Keyword(s):  
Climate Change ◽  
Social Media ◽  
Large Scale ◽  
Public Awareness ◽  
Connected Network ◽  
Multi Scale ◽  
Valuable Addition ◽  
Scientific Approach ◽  
Design Solutions ◽  
Mitigate Climate Change

In this Opinion, the importance of public awareness to design solutions to mitigate climate change issues is highlighted. A large-scale acknowledgment of the climate change consequences has great potential to build social momentum. Momentum, in turn, builds motivation and demand, which can be leveraged to develop a multi-scale strategy to tackle the issue. The pursuit of public awareness is a valuable addition to the scientific approach to addressing climate change issues. The Opinion is concluded by providing strategies on how to effectively raise public awareness on climate change-related topics through an integrated, well-connected network of mavens (e.g., scientists) and connectors (e.g., social media influencers).

scholarly journals Faster than warp speed: early attention to COVD-19 by anti-vaccine groups on Facebook

2021 ◽  
Author(s):  
Seth C Kalichman ◽  
Lisa A Eaton ◽  
Valerie A Earnshaw ◽  
Natalie Brousseau
Keyword(s):  
Social Media ◽  
Vaccine Development ◽  
Rapid Development ◽  
Development Program ◽  
Vaccine Safety ◽  
Vaccine Hesitancy ◽  
Conspiracy Theories ◽  
The Us ◽  
Us Government ◽  
Vaccine Information

Abstract Background The unprecedented rapid development of COVID-19 vaccines has faced SARS-CoV- (COVID-19) vaccine hesitancy, which is partially fueled by the misinformation and conspiracy theories propagated by anti-vaccine groups on social media. Research is needed to better understand the early COVID-19 anti-vaccine activities on social media. Methods This study chronicles the social media posts concerning COVID-19 and COVID-19 vaccines by leading anti-vaccine groups (Dr Tenpenny on Vaccines, the National Vaccine Information Center [NVIC] the Vaccination Information Network [VINE]) and Vaccine Machine in the early months of the COVID-19 pandemic (February–May 2020). Results Analysis of 2060 Facebook posts showed that anti-vaccine groups were discussing COVID-19 in the first week of February 2020 and were specifically discussing COVID-19 vaccines by mid-February 2020. COVID-19 posts by NVIC were more widely disseminated and showed greater influence than non-COVID-19 posts. Early COVID-19 posts concerned mistrust of vaccine safety and conspiracy theories. Conclusion Major anti-vaccine groups were sowing seeds of doubt on Facebook weeks before the US government launched its vaccine development program ‘Operation Warp Speed’. Early anti-vaccine misinformation campaigns outpaced public health messaging and hampered the rollout of COVID-19 vaccines.

scholarly journals DeepRePath: Identifying the Prognostic Features of Early-Stage Lung Adenocarcinoma Using Multi-Scale Pathology Images and Deep Convolutional Neural Networks

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3308
Author(s):  
Won Sang Shim ◽  
Kwangil Yim ◽  
Tae-Jung Kim ◽  
Yeoun Eun Sung ◽  
Gyeongyun Lee ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Early Stage ◽  
External Validation ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Deep Convolutional Neural Networks ◽  
Prognostic Features ◽  
Multi Scale ◽  
Model Based ◽  
Number Of Patients

The prognosis of patients with lung adenocarcinoma (LUAD), especially early-stage LUAD, is dependent on clinicopathological features. However, its predictive utility is limited. In this study, we developed and trained a DeepRePath model based on a deep convolutional neural network (CNN) using multi-scale pathology images to predict the prognosis of patients with early-stage LUAD. DeepRePath was pre-trained with 1067 hematoxylin and eosin-stained whole-slide images of LUAD from the Cancer Genome Atlas. DeepRePath was further trained and validated using two separate CNNs and multi-scale pathology images of 393 resected lung cancer specimens from patients with stage I and II LUAD. Of the 393 patients, 95 patients developed recurrence after surgical resection. The DeepRePath model showed average area under the curve (AUC) scores of 0.77 and 0.76 in cohort I and cohort II (external validation set), respectively. Owing to low performance, DeepRePath cannot be used as an automated tool in a clinical setting. When gradient-weighted class activation mapping was used, DeepRePath indicated the association between atypical nuclei, discohesive tumor cells, and tumor necrosis in pathology images showing recurrence. Despite the limitations associated with a relatively small number of patients, the DeepRePath model based on CNNs with transfer learning could predict recurrence after the curative resection of early-stage LUAD using multi-scale pathology images.

Avenues to News and Diverse News Exposure Online: Comparing Direct Navigation, Social Media, News Aggregators, Search Queries, and Article Hyperlinks

2021 ◽  
pp. 194016122110091
Author(s):  
Magdalena Wojcieszak ◽  
Ericka Menchen-Trevino ◽  
Joao F. F. Goncalves ◽  
Brian Weeks
Keyword(s):  
Social Media ◽  
Search Engines ◽  
Direct Access ◽  
Data Set ◽  
Individual Level ◽  
News Sources ◽  
Internet Users ◽  
News Websites ◽  
The Us ◽  
Social Media Platforms

The online environment dramatically expands the number of ways people can encounter news but there remain questions of whether these abundant opportunities facilitate news exposure diversity. This project examines key questions regarding how internet users arrive at news and what kinds of news they encounter. We account for a multiplicity of avenues to news online, some of which have never been analyzed: (1) direct access to news websites, (2) social networks, (3) news aggregators, (4) search engines, (5) webmail, and (6) hyperlinks in news. We examine the extent to which each avenue promotes news exposure and also exposes users to news sources that are left leaning, right leaning, and centrist. When combined with information on individual political leanings, we show the extent of dissimilar, centrist, or congenial exposure resulting from each avenue. We rely on web browsing history records from 636 social media users in the US paired with survey self-reports, a unique data set that allows us to examine both aggregate and individual-level exposure. Visits to news websites account for about 2 percent of the total number of visits to URLs and are unevenly distributed among users. The most widespread ways of accessing news are search engines and social media platforms (and hyperlinks within news sites once people arrive at news). The two former avenues also increase dissimilar news exposure, compared to accessing news directly, yet direct news access drives the highest proportion of centrist exposure.

From Bench to Bedside: New Technologies for Cancer Diagnosis and Treatment

2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Michal-Ruth Schweiger ◽  
Hans Lehrach
Keyword(s):  
Cancer Diagnosis ◽  
New Technologies ◽  
Early Stage ◽  
Predictive Biomarkers ◽  
Patient Treatment ◽  
Malignant Neoplasms ◽  
Advanced Stage ◽  
Common Cause ◽  
The Us ◽  
Risk Patients

According to the centre for disease control (CDC) malignant neoplasms are the second most common cause of death in the US in 2004 (1). One of the major problems is that most of the cancers are diagnosed in an advanced stage, which prohibits curative treatment. In order to circumvent these problems, we need to develop strategies that allow identification of risk patients and tumors at an early stage. In addition, it is necessary to identify prognostic and predictive biomarkers that guide patient treatment at different stages of the disease.

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