scholarly journals Geolocated Twitter social media data to describe the geographic spread of SARS-CoV-2

2020 ◽  
Vol 27 (5) ◽  
Author(s):  
Donal Bisanzio ◽  
Moritz U G Kraemer ◽  
Thomas Brewer ◽  
John S Brownstein ◽  
Richard Reithinger
Keyword(s):  
Human Mobility ◽  
Mobility Patterns ◽  
Social Media Data ◽  
Country Level ◽  
Twitter Data ◽  
Geographic Spread ◽  
Twitter User ◽  
The Usa ◽  
Twitter Users ◽  
Media Data

Openly available, geotagged Twitter data from 2013 to 2015 was used to estimate the 2019–2020 human mobility patterns in and outside of China to predict the spatiotemporal spread of severe acute respiratory syndrome coronavirus 2. Countries with the highest number of visiting Twitter users outside of China were the USA, Japan, UK, Germany and Turkey. A high correlation was observed when comparing country-level Twitter user visits and reported cases.

scholarly journals Towards Detecting Social Events by Mining Geographical Patterns with VGI Data

2018 ◽  
Vol 7 (12) ◽  
pp. 481
Author(s):  
Zhewei Liu ◽  
Xiaolin Zhou ◽  
Wenzhong Shi ◽  
Anshu Zhang
Keyword(s):  
Social Media ◽  
Event Detection ◽  
Human Mobility ◽  
Ground Truth ◽  
Mobility Patterns ◽  
Social Media Data ◽  
Geographical Patterns ◽  
Outlier Test ◽  
Social Events ◽  
Media Data

Detecting events using social media data is important for timely emergency response and urban monitoring. Current studies primarily use semantic-based methods, in which “bursts” of certain semantic signals are detected to identify emerging events. Nevertheless, our consideration is that a social event will not only affect semantic signals but also cause irregular human mobility patterns. By introducing depictive features, such irregular patterns can be used for event detection. Consequently, in this paper, we develop a novel, comprehensive workflow for event detection by mining the geographical patterns of VGI. This workflow first uses data geographical topic modeling to detect the hashtag communities with VGI semantic data. Both global and local indicators are then constructed by introducing spatial autocorrelation measurements. We then adopt an outlier test and generate indicator maps to spatiotemporally identify the potential social events. This workflow was implemented using a real-world dataset (104,000 geo-tagged photos) and the evaluation was conducted both qualitatively and quantitatively. A set of experiments showed that the discovered semantic communities were internally consistent and externally differentiable, and the plausibility of the detected events was demonstrated by referring to the available ground truth. This study examined the feasibility of detecting events by investigating the geographical patterns of social media data and can be applied to urban knowledge retrieval.

scholarly journals Use of Twitter social media activity as a proxy for human mobility to predict the spatiotemporal spread of COVID-19 at global scale

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Donal Bisanzio ◽  
Moritz U.G. Kraemer ◽  
Isaac I. Bogoch ◽  
Thomas Brewer ◽  
John S Brownstein ◽  
...  
Keyword(s):  
Social Media ◽  
Human Mobility ◽  
Mainland China ◽  
Vulnerability Index ◽  
Global Scale ◽  
Study Cohort ◽  
Mobility Patterns ◽  
Social Media Data ◽  
Current Outbreak ◽  
Media Data

As of February 27, 2020, 82,294 confirmed cases of coronavirus disease (COVID-19) have been reported since December 2019, including 2,804 deaths, with cases reported throughout China, as well as in 45 international locations outside of mainland China. We predict the spatiotemporal spread of reported COVID- 19 cases at the global level during the first few weeks of the current outbreak by analyzing openly available geolocated Twitter social media data. Human mobility patterns were estimated by analyzing geolocated 2013–2015 Twitter data from users who had: i) tweeted at least twice on consecutive days from Wuhan, China, between November 1, 2013, and January 28, 2014, and November 1, 2014, and January 28, 2015; and ii) left Wuhan following their second tweet during the time period under investigation. Publicly available COVID-19 case data were used to investigate the correlation among cases reported during the current outbreak, locations visited by the study cohort of Twitter users, and airports with scheduled flights from Wuhan. Infectious Disease Vulnerability Index (IDVI) data were obtained to identify the capacity of countries receiving travellers from Wuhan to respond to COVID-19. Our study cohort comprised 161 users. Of these users, 133 (82.6%) posted tweets from 157 Chinese cities (1,344 tweets) during the 30 days after leaving Wuhan following their second tweet, with a median of 2 (IQR= 1–3) locations visited and a mean distance of 601 km (IQR= 295.2–834.7 km) traveled. Of our user cohort, 60 (37.2%) traveled abroad to 119 locations in 28 countries. Of the 82 COVID-19 cases reported outside China as of January 30, 2020, 54 cases had known geolocation coordinates and 74.1% (40 cases) were reported less than 15 km (median = 7.4 km, IQR= 2.9–285.5 km) from a location visited by at least one of our study cohort’s users. Countries visited by the cohort’s users and which have cases reported by January 30, 2020, had a median IDVI equal to 0.74. We show that social media data can be used to predict the spatiotemporal spread of infectious diseases such as COVID-19. Based on our analyses, we anticipate cases to be reported in Saudi Arabia and Indonesia; additionally, countries with a moderate to low IDVI (i.e. ≤0.7) such as Indonesia, Pakistan, and Turkey should be on high alert and develop COVID- 19 response plans as soon as permitting.

scholarly journals Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data

2020 ◽  
Vol 9 (2) ◽  
pp. 125 ◽  
Author(s):  
Zeinab Ebrahimpour ◽  
Wanggen Wan ◽  
José Luis Velázquez García ◽  
Ofelia Cervantes ◽  
Li Hou
Keyword(s):  
Social Media ◽  
Large Scale ◽  
City Planning ◽  
Smart Cities ◽  
Human Mobility ◽  
Location Based Services ◽  
Mobility Patterns ◽  
Social Media Data ◽  
Planning Decisions ◽  
Media Data

Social media data analytics is the art of extracting valuable hidden insights from vast amounts of semi-structured and unstructured social media data to enable informed and insightful decision-making. Analysis of social media data has been applied for discovering patterns that may support urban planning decisions in smart cities. In this paper, Weibo social media data are used to analyze social-geographic human mobility in the CBD area of Shanghai to track citizen’s behavior. Our main motivation is to test the validity of geo-located Weibo data as a source for discovering human mobility and activity patterns. In addition, our goal is to identify important locations in people’s lives with the support of location-based services. The algorithms used are described and the results produced are presented using adequate visualization techniques to illustrate the detected human mobility patterns obtained by the large-scale social media data in order to support smart city planning decisions. The outcome of this research is helpful not only for city planners, but also for business developers who hope to extend their services to citizens.

Exploring human mobility patterns using geo-tagged social media data at the group level

2018 ◽  
Vol 64 (2) ◽  
pp. 221-238 ◽  
Author(s):  
Chao Yang ◽  
Meng Xiao ◽  
Xuan Ding ◽  
Wenwen Tian ◽  
Yong Zhai ◽  
...  
Keyword(s):  
Social Media ◽  
Human Mobility ◽  
Group Level ◽  
Mobility Patterns ◽  
Social Media Data ◽  
Media Data

Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

2020 ◽  
Vol 14 (2) ◽  
pp. 140-159
Author(s):  
Anthony-Paul Cooper ◽  
Emmanuel Awuni Kolog ◽  
Erkki Sutinen
Keyword(s):  
Machine Learning ◽  
Online Community ◽  
High Volume ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Social Media Data ◽  
Twitter Data ◽  
Resource Intensity ◽  
Media Data ◽  
Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

scholarly journals What Your Tweets Tell Us About You: Identity, Ownership and Privacy of Twitter Data

2012 ◽  
Vol 7 (1) ◽  
pp. 174-197 ◽  
Author(s):  
Heather Small ◽  
Kristine Kasianovitz ◽  
Ronald Blanford ◽  
Ina Celaya
Keyword(s):  
Social Media ◽  
Social Networking Sites ◽  
Data Sets ◽  
Data Set ◽  
Social Media Data ◽  
Twitter Data ◽  
Other Information ◽  
Rich Data ◽  
Additional Value ◽  
Media Data

Social networking sites and other social media have enabled new forms of collaborative communication and participation for users, and created additional value as rich data sets for research. Research based on accessing, mining, and analyzing social media data has risen steadily over the last several years and is increasingly multidisciplinary; researchers from the social sciences, humanities, computer science and other domains have used social media data as the basis of their studies. The broad use of this form of data has implications for how curators address preservation, access and reuse for an audience with divergent disciplinary norms related to privacy, ownership, authenticity and reliability.In this paper, we explore how the characteristics of the Twitter platform, coupled with an ambiguous and evolving understanding of privacy in networked communication, and divergent disciplinary understandings of the resulting data, combine to create complex issues for curators trying to ensure broad-based and ethical reuse of Twitter data. We provide a case study of a specific data set to illustrate how data curators can engage with the topics and questions raised in the paper. While some initial suggestions are offered to librarians and other information professionals who are beginning to receive social media data from researchers, our larger goal is to stimulate discussion and prompt additional research on the curation and preservation of social media data.

Changing Trends in Long-Term Sentiments and Neighborhood Determinants in a Shrinking City

2021 ◽  
pp. 0739456X2110442
Author(s):  
Yunmi Park ◽  
Minju Kim ◽  
Jiyeon Shin ◽  
Megan E. Heim LaFrombois
Keyword(s):  
Social Media ◽  
Built Environment ◽  
Urban Regeneration ◽  
Neighborhood Conditions ◽  
Social Media Data ◽  
Twitter Data ◽  
Changing Trends ◽  
Shrinking City ◽  
Media Data

This research examined social media’s role in understanding perceptions about the spaces in which individuals interact, what planners can learn from social media data, and how to use social media to inform urban regeneration efforts. Using Twitter data from 2010 to 2018 recorded in one U.S. shrinking city, Detroit, Michigan, this paper longitudinally investigated topics that people discuss, their emotions, and neighborhood conditions associated with these topics and sentiments. Findings demonstrate that neighborhood demographics, socioeconomic, and built environment conditions impact people’s sentiments.

scholarly journals Insulin pricing and other major diabetes-related concerns in the USA: a study of 46 407 tweets between 2017 and 2019

2020 ◽  
Vol 8 (1) ◽  
pp. e001190
Author(s):  
Adrian Ahne ◽  
Francisco Orchard ◽  
Xavier Tannier ◽  
Camille Perchoux ◽  
Beverley Balkau ◽  
...  
Keyword(s):  
Social Media ◽  
Positive Emotions ◽  
Negative Impact ◽  
Diabetes Distress ◽  
Social Media Data ◽  
Health Related ◽  
The Usa ◽  
Personal Content ◽  
Design And Methods ◽  
Media Data

IntroductionLittle research has been done to systematically evaluate concerns of people living with diabetes through social media, which has been a powerful tool for social change and to better understand perceptions around health-related issues. This study aims to identify key diabetes-related concerns in the USA and primary emotions associated with those concerns using information shared on Twitter.Research design and methodsA total of 11.7 million diabetes-related tweets in English were collected between April 2017 and July 2019. Machine learning methods were used to filter tweets with personal content, to geolocate (to the USA) and to identify clusters of tweets with emotional elements. A sentiment analysis was then applied to each cluster.ResultsWe identified 46 407 tweets with emotional elements in the USA from which 30 clusters were identified; 5 clusters (18% of tweets) were related to insulin pricing with both positive emotions (joy, love) referring to advocacy for affordable insulin and sadness emotions related to the frustration of insulin prices, 5 clusters (12% of tweets) to solidarity and support with a majority of joy and love emotions expressed. The most negative topics (10% of tweets) were related to diabetes distress (24% sadness, 27% anger, 21% fear elements), to diabetic and insulin shock (45% anger, 46% fear) and comorbidities (40% sadness).ConclusionsUsing social media data, we have been able to describe key diabetes-related concerns and their associated emotions. More specifically, we were able to highlight the real-world concerns of insulin pricing and its negative impact on mood. Using such data can be a useful addition to current measures that inform public decision making around topics of concern and burden among people with diabetes.

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