scholarly journals Using VGI and Social Media Data to Understand Urban Green Space: A Narrative Literature Review

2021 ◽  
Vol 10 (7) ◽  
pp. 425
Author(s):  
Nan Cui ◽  
Nick Malleson ◽  
Victoria Houlden ◽  
Alexis Comber
Keyword(s):  
Social Media ◽  
Data Quality ◽  
Rural Areas ◽  
Green Space ◽  
Urban Green Space ◽  
Geographical Information ◽  
Future Research ◽  
Social Media Data ◽  
Urban Green ◽  
Media Data

Volunteered Geographical Information (VGI) and social media can provide information about real-time perceptions, attitudes and behaviours in urban green space (UGS). This paper reviews the use of VGI and social media data in research examining UGS. The current state of the art is described through the analysis of 177 papers to (1) summarise the characteristics and usage of data from different platforms, (2) provide an overview of the research topics using such data sources, and (3) characterise the research approaches based on data pre-processing, data quality assessment and improvement, data analysis and modelling. A number of important limitations and priorities for future research are identified. The limitations include issues of data acquisition and representativeness, data quality, as well as differences across social media platforms in different study areas such as urban and rural areas. The research priorities include a focus on investigating factors related to physical activities in UGS areas, urban park use and accessibility, the use of data from multiple sources and, where appropriate, making more effective use of personal information. In addition, analysis approaches can be extended to examine the network suggested by social media posts that are shared, re-posted or reacted to and by being combined with textual, image and geographical data to extract more representative information for UGS analysis.

scholarly journals Tweeting back: predicting new cases of back pain with mass social media data

2015 ◽  
Vol 23 (3) ◽  
pp. 644-648 ◽  
Author(s):  
Hopin Lee ◽  
James H McAuley ◽  
Markus Hübscher ◽  
Heidi G Allen ◽  
Steven J Kamper ◽  
...  
Keyword(s):  
Risk Factors ◽  
Social Media ◽  
Back Pain ◽  
Health Interventions ◽  
Future Research ◽  
Preventive Interventions ◽  
Public Health Interventions ◽  
Social Media Data ◽  
Media Data ◽  
Global Health Problem

Background Back pain is a global health problem. Recent research has shown that risk factors that are proximal to the onset of back pain might be important targets for preventive interventions. Rapid communication through social media might be useful for delivering timely interventions that target proximal risk factors. Identifying individuals who are likely to discuss back pain on Twitter could provide useful information to guide online interventions. Methods We used a case-crossover study design for a sample of 742 028 tweets about back pain to quantify the risks associated with a new tweet about back pain. Results The odds of tweeting about back pain just after tweeting about selected physical, psychological, and general health factors were 1.83 (95% confidence interval [CI], 1.80-1.85), 1.85 (95% CI: 1.83-1.88), and 1.29 (95% CI, 1.27-1.30), respectively. Conclusion These findings give directions for future research that could use social media for innovative public health interventions.

scholarly journals Using social media data to map the areas most affected by ISIS in Syria

2020 ◽  
Vol 26 (1) ◽  
pp. 464-470
Author(s):  
Mohamad Hasan
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Geographical Information ◽  
Data Mapping ◽  
Islamic State ◽  
Social Media Data ◽  
The Social ◽  
Mapping Process ◽  
Data Source ◽  
Media Data

This paper presents a model to collect, save, geocode, and analyze social media data. The model is used to collect and process the social media data concerned with the ISIS terrorist group (the Islamic State in Iraq and Syria), and to map the areas in Syria most affected by ISIS accordingly to the social media data. Mapping process is assumed automated compilation of a density map for the geocoded tweets. Data mined from social media (e.g., Twitter and Facebook) is recognized as dynamic and easily accessible resources that can be used as a data source in spatial analysis and geographical information system. Social media data can be represented as a topic data and geocoding data basing on the text of the mined from social media and processed using Natural Language Processing (NLP) methods. NLP is a subdomain of artificial intelligence concerned with the programming computers to analyze natural human language and texts. NLP allows identifying words used as an initial data by developed geocoding algorithm. In this study, identifying the needed words using NLP was done using two corpora. First corpus contained the names of populated places in Syria. The second corpus was composed in result of statistical analysis of the number of tweets and picking the words that have a location meaning (i.e., schools, temples, etc.). After identifying the words, the algorithm used Google Maps geocoding API in order to obtain the coordinates for posts.

Analyzing Social Media Research: A Data Quality and Research Reproducibility Perspective

2021 ◽  
pp. 227797522110118
Author(s):  
Amit K. Srivastava ◽  
Rajhans Mishra
Keyword(s):  
Social Media ◽  
Data Quality ◽  
Quality Of Data ◽  
Social Media Data ◽  
National Crisis ◽  
Social Media Platforms ◽  
Quality Issues ◽  
The One ◽  
Media Data

Social media platforms have become very popular these days among individuals and organizations. On the one hand, organizations use social media as a potential tool to create awareness of their products among consumers, and on the other hand, social media data is useful to predict the national crisis, election polls, stock prediction, etc. However, nowadays, a debate is going on about the quality of data generated on social media platforms, whether it is relevant for prediction and generalization. The article discusses the relevance and quality of data obtained from social media in the context of research and development. Social media data quality issues may impact the generalizability and reproducibility of the results of the study. The paper explores possible reasons for quality issues in the data generated over social media platforms along with the suggestive measures to minimize them using the proposed social media data quality framework.

scholarly journals Social Media Text Mining Framework for Drug Abuse: Development and Validation Study With an Opioid Crisis Case Analysis

10.2196/18350 ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. e18350 ◽  
Author(s):  
Tareq Nasralah ◽  
Omar El-Gayar ◽  
Yong Wang
Keyword(s):  
Social Media ◽  
Drug Abuse ◽  
Data Quality ◽  
Prescription Drugs ◽  
Case Analysis ◽  
Media Content ◽  
Opioid Epidemic ◽  
Social Media Data ◽  
Social Media Text ◽  
Media Data

Background Social media are considered promising and viable sources of data for gaining insights into various disease conditions and patients’ attitudes, behaviors, and medications. They can be used to recognize communication and behavioral themes of problematic use of prescription drugs. However, mining and analyzing social media data have challenges and limitations related to topic deduction and data quality. As a result, we need a structured approach to analyze social media content related to drug abuse in a manner that can mitigate the challenges and limitations surrounding the use of such data. Objective This study aimed to develop and evaluate a framework for mining and analyzing social media content related to drug abuse. The framework is designed to mitigate challenges and limitations related to topic deduction and data quality in social media data analytics for drug abuse. Methods The proposed framework started with defining different terms related to the keywords, categories, and characteristics of the topic of interest. We then used the Crimson Hexagon platform to collect data based on a search query informed by a drug abuse ontology developed using the identified terms. We subsequently preprocessed the data and examined the quality using an evaluation matrix. Finally, a suitable data analysis approach could be used to analyze the collected data. Results The framework was evaluated using the opioid epidemic as a drug abuse case analysis. We demonstrated the applicability of the proposed framework to identify public concerns toward the opioid epidemic and the most discussed topics on social media related to opioids. The results from the case analysis showed that the framework could improve the discovery and identification of topics in social media domains characterized by a plethora of highly diverse terms and lack of a commonly available dictionary or language by the community, such as in the case of opioid and drug abuse. Conclusions The proposed framework addressed the challenges related to topic detection and data quality. We demonstrated the applicability of the proposed framework to identify the common concerns toward the opioid epidemic and the most discussed topics on social media related to opioids.

Who is Tweeting? A Scoping Review of Methods to Establish Race and Ethnicity from Twitter Datasets (Preprint)

2021 ◽  
Author(s):  
Su Golder ◽  
Robin Stevens ◽  
Karen O'Conor ◽  
Richard James ◽  
Graciela Gonzalez-Hernandez
Keyword(s):  
Social Media ◽  
Scoping Review ◽  
Race And Ethnicity ◽  
Best Practice ◽  
English Language ◽  
Census Data ◽  
Future Research ◽  
Social Media Data ◽  
Lower Accuracy ◽  
Media Data

BACKGROUND Background: A growing amount of health research uses social media data. Those critical of social media research often cite that it may be unrepresentative of the population, but the suitability of social media data in digital epidemiology is more nuanced. Identifying the demographics of social media users can help establish representativeness. OBJECTIVE Objectives: We sought to identify the different approaches or combination of approaches to extract race or ethnicity from social media and report on the challenges of using these methods. METHODS Methods: We present a scoping review to identify the methods used to extract race or ethnicity from Twitter datasets. We searched 17 electronic databases and carried out reference checking and handsearching in order to identify relevant articles. Sifting of each record was undertaken independently by at least two researchers with any disagreement discussed. The included studies could be categorized by the methods the authors applied to extract race or ethnicity. RESULTS Results: From 1249 records we identified 67 that met our inclusion criteria. The majority focus on US based users and English language tweets. A range of types of data were used including Twitter profile -pictures or information from bios (such as names or self-declarations), or location and/or content in the tweets themselves. A range of methodologies were used including using manual inference, linkage to census data, commercial software, language/dialect recognition and machine learning. Not all studies evaluated their methods. Those that did found accuracy to vary from 45% to 93% with significantly lower accuracy identifying non-white race categories. The inference of race/ethnicity raises important ethical questions which can be exacerbated by the data and methods used. The comparative accuracy of different methods is also largely unknown. CONCLUSIONS Conclusion: There is no standard accepted approach or current guidelines for extracting or inferring race or ethnicity of Twitter users. Social media researchers must use careful interpretation of race or ethnicity and not over-promise what can be achieved, as even manual screening is a subjective, imperfect method. Future research should establish the accuracy of methods to inform evidence-based best practice guidelines for social media researchers, and be guided by concerns of equity and social justice.

A mobile visualization platform for exploring social media data

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Yonghong Tong ◽  
Muhammet Bakan
Keyword(s):  
Social Media ◽  
Data Visualization ◽  
Mobile Device ◽  
Large Scale ◽  
Future Research ◽  
Social Media Data ◽  
Human Behaviors ◽  
Big Picture ◽  
Analysis Platform ◽  
Media Data

With the increasing application of using mobile device and social media, large amount of continuous information about human behaviors is available. Data visualization provides an insightful presentation for the large-scale social media datasets. The focus of this paper is on the development of a mobile-device based visualization and analysis platform for social media data for the purpose of retrieving and visualizing visitors’ information for a specific region. This developed platform allows users to view the “big picture” of the visitors’ locations information. The result shows that the developed platform 1) performs a satisfied data collection and data visualization on a mobile device, 2) assists users to understand the varieties of human behaviors while visiting a place, and 3) offers a feasible role in imaging immediate information from social media and leading to further policy-making in related sectors and areas. Future research opportunities and challenges for social media data visualization are discussed.Keywords: Social media, data visualization, mobile device

scholarly journals From FAIR data to fair data use: Methodological data fairness in health-related social media research

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172110103
Author(s):  
Sabina Leonelli ◽  
Rebecca Lovell ◽  
Benedict W Wheeler ◽  
Lora Fleming ◽  
Hywel Williams
Keyword(s):  
Social Media ◽  
Well Being ◽  
Research Process ◽  
Future Research ◽  
Social Media Data ◽  
Data Practices ◽  
Health Related Research ◽  
Health Related ◽  
Use Of Social Media ◽  
Media Data

The paper problematises the reliability and ethics of using social media data, such as sourced from Twitter or Instagram, to carry out health-related research. As in many other domains, the opportunity to mine social media for information has been hailed as transformative for research on well-being and disease. Considerations around the fairness, responsibilities and accountabilities relating to using such data have often been set aside, on the understanding that as long as data were anonymised, no real ethical or scientific issue would arise. We first counter this perception by emphasising that the use of social media data in health research can yield problematic and unethical results. We then provide a conceptualisation of methodological data fairness that can complement data management principles such as FAIR by enhancing the actionability of social media data for future research. We highlight the forms that methodological data fairness can take at different stages of the research process and identify practical steps through which researchers can ensure that their practices and outcomes are scientifically sound as well as fair to society at large. We conclude that making research data fair as well as FAIR is inextricably linked to concerns around the adequacy of data practices. The failure to act on those concerns raises serious ethical, methodological and epistemic issues with the knowledge and evidence that are being produced.

scholarly journals The Urban Nexus Approach for Analyzing Mobility in the Smart City: Towards the Identification of City Users Networking

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Federica Burini ◽  
Nicola Cortesi ◽  
Kevin Gotti ◽  
Giuseppe Psaila
Keyword(s):  
Social Media ◽  
Spatial Analysis ◽  
Mobile Apps ◽  
Interdisciplinary Approach ◽  
Geographical Information ◽  
Digital Environment ◽  
Social Media Data ◽  
Analysis Task ◽  
The City ◽  
Media Data

We present an interdisciplinary approach that makes possible to learn how citizens live in the city by the means of mobile social media data, that is, volunteered geographical information provided by the inhabitants through social media and mobile apps, by adopting a new reticular approach to spatial analysis. In particular, we present the general notions as background of our work, an investigation methodology to apply whenever such an analysis task must be performed, and a digital environment of tools and frameworks to support the methodology.

Using Twitter to explore consumers' sentiments and their social representations towards new food trends

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Emilio Pindado ◽  
Ramo Barrena
Keyword(s):  
Social Media ◽  
Clustering Algorithm ◽  
Social Representations ◽  
Local Level ◽  
Geographical Information ◽  
Social Media Data ◽  
Content Type ◽  
The Social ◽  
Food Trends ◽  
Media Data

PurposeThis paper investigates the use of Twitter for studying the social representations of different regions across the world towards new food trends.Design/methodology/approachA density-based clustering algorithm was applied to 7,014 tweets to identify regions of consumers sharing content about food trends. The attitude of their social representations was addressed with the sentiment analysis, and grid maps were used to explore subregional differences.FindingsTwitter users have a weak, positive attitude towards food trends, and significant differences were found across regions identified, which suggests that factors at the regional level such as cultural context determine users' attitude towards food innovations. The subregional analysis showed differences at the local level, which reinforces the evidence that context matters in consumers' attitude expressed in social media.Research limitations/implicationsThe social media content is sensitive to spatio-temporal events. Therefore, research should take into account content, location and contextual information to understand consumers' perceptions. The methodology proposed here serves to identify consumers' regions and to characterize their attitude towards specific topics. It considers not only administrative but also cognitive boundaries in order to analyse subsequent contextual influences on consumers' social representations.Practical implicationsThe approach presented allows marketers to identify regions of interest and localize consumers' attitudes towards their products using social media data, providing real-time information to contrast with their strategies in different areas and adapt them to consumers' feelings.Originality/valueThis study presents a research methodology to analyse food consumers' understanding and perceptions using not only content but also geographical information of social media data, which provides a means to extract more information than the content analysis applied in the literature.

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