Social Media Data Analysis in Urban e-Planning

Computational social media data analysis (SMDA) is opening up new possibilities for participatory urban planning. The aim of this study is to analyse what kind of computational methods can be used to analyse social media data to inform urban planning. A descriptive literature review of recent case study articles reveal that in this context SMDA has been applied mainly to location based social media data, such as geo-tagged Tweets, photographs and check-in data. There were only a few studies concerning the use of non-place-based data. Based on this review SMDA can provide planners with local knowledge about people's opinions, experiences, feelings, behaviour, and about the city structure. However, integration of this knowledge in planning and decision-making has not been completely successful in any of the cases. By way of a conclusion, a planning-led categorization of the SMDA method's tools and analysis results is suggested.

Download Full-text

From the board to the streets: a case study of Local Property Trader

Transactions of the Digital Games Research Association ◽

10.26503/todigra.v1i3.22 ◽

2014 ◽

Vol 1 (3) ◽

Cited By ~ 1

Author(s):

Alison Gazzard ◽

Mark Lochrie ◽

Adrian Gradinar ◽

Paul Coulton ◽

Daniel Burnett ◽

...

Keyword(s):

Social Media ◽

Local Property ◽

Qr Code ◽

Mobile Media ◽

Social Media Data ◽

Key Points ◽

The City ◽

Traditional Game ◽

Media Data

The boardgame of Monopoly has undergone various iterations since it was first published in 1934. Versions have included location-based varieties of the game, involving mobile media devices that have taken the boardgame to the city streets as a way of engaging players with location in new ways. This article examines a new version of Monopoly, titled Local Property Trader that works with NFC/QR code technologies in order to encourage players to move around the city and interact with local businesses. In doing so, the project hopes to highlight how location-based games can use social media data to update a traditional game into more contemporary contexts. Correspondingly, the differences and similarities of taking a boardgame and reworking it for the city streets are explored through ideas surrounding location, player and map as key points of intersection between the two media forms.

Download Full-text

Streaming Social Media Data Analysis for Events Extraction and Warehousing using Hadoop and Storm: Drug Abuse Case Study

Procedia Computer Science ◽

10.1016/j.procs.2019.09.316 ◽

2019 ◽

Vol 159 ◽

pp. 1459-1467

Author(s):

Ferdaous Jenhani ◽

Mohamed Salah Gouider ◽

Lamjed Ben Said

Keyword(s):

Social Media ◽

Data Analysis ◽

Drug Abuse ◽

Social Media Data ◽

Abuse Case ◽

Media Data

Download Full-text

A comprehensive social media data processing and analytics architecture by using big data platforms: a case study of twitter flood-risk messages

Earth Science Informatics ◽

10.1007/s12145-021-00601-w ◽

2021 ◽

Author(s):

Michal Podhoranyi

Keyword(s):

Social Media ◽

Big Data ◽

Data Processing ◽

Flood Risk ◽

Social Media Data ◽

Media Data

Download Full-text

Research on the Emotion Regulation Effect of Home Sports Based on Social Media Data Analysis and Online Experimental Investigation

10.1109/tcs52929.2021.00079 ◽

2021 ◽

Author(s):

Fuwei Jiao ◽

Tianxiao Guo ◽

Jianwei Li ◽

Pengjie Li ◽

Run Cao ◽

...

Keyword(s):

Social Media ◽

Experimental Investigation ◽

Emotion Regulation ◽

Data Analysis ◽

Social Media Data ◽

Regulation Effect ◽

Media Data

Download Full-text

A Pipeline to Understand Emerging Illness Via Social Media Data Analysis: Case Study on Breast Implant Illness (Preprint)

10.2196/preprints.29768 ◽

2021 ◽

Author(s):

Vishal Dey ◽

Peter Krasniak ◽

Minh Nguyen ◽

Clara Lee ◽

Xia Ning

Keyword(s):

Mental Health ◽

Social Media ◽

Natural Language Processing ◽

Data Analysis ◽

Natural Language ◽

Language Processing ◽

Breast Implant ◽

Public Attention ◽

Social Media Data ◽

Media Data

BACKGROUND A new illness can come to public attention through social media before it is medically defined, formally documented, or systematically studied. One example is a condition known as breast implant illness (BII), which has been extensively discussed on social media, although it is vaguely defined in the medical literature. OBJECTIVE The objective of this study is to construct a data analysis pipeline to understand emerging illnesses using social media data and to apply the pipeline to understand the key attributes of BII. METHODS We constructed a pipeline of social media data analysis using natural language processing and topic modeling. Mentions related to signs, symptoms, diseases, disorders, and medical procedures were extracted from social media data using the clinical Text Analysis and Knowledge Extraction System. We mapped the mentions to standard medical concepts and then summarized these mapped concepts as topics using latent Dirichlet allocation. Finally, we applied this pipeline to understand BII from several BII-dedicated social media sites. RESULTS Our pipeline identified topics related to toxicity, cancer, and mental health issues that were highly associated with BII. Our pipeline also showed that cancers, autoimmune disorders, and mental health problems were emerging concerns associated with breast implants, based on social media discussions. Furthermore, the pipeline identified mentions such as rupture, infection, pain, and fatigue as common self-reported issues among the public, as well as concerns about toxicity from silicone implants. CONCLUSIONS Our study could inspire future studies on the suggested symptoms and factors of BII. Our study provides the first analysis and derived knowledge of BII from social media using natural language processing techniques and demonstrates the potential of using social media information to better understand similar emerging illnesses. CLINICALTRIAL

Download Full-text