The Use of Social Media for Urban Planning

2014 ◽  
pp. 113-134 ◽  
Author(s):  
Fabian Neuhaus
Keyword(s):  
Social Media ◽  
Big Data ◽  
Urban Areas ◽  
Large Scale ◽  
Urban Environments ◽  
Data Systems ◽  
User Management ◽  
Social Nature ◽  
User Data ◽  
Use Of Social Media

User data created in the digital context has increasingly been of interest to analysis and spatial analysis in particular. Large scale computer user management systems such as digital ticketing and social networking are creating vast amount of data. Such data systems can contain information generated by potentially millions of individuals. This kind of data has been termed big data. The analysis of big data can in its spatial but also in a temporal and social nature be of much interest for analysis in the context of cities and urban areas. This chapter discusses this potential along with a selection of sample work and an in-depth case study. Hereby the focus is mainly on the use and employment of insight gained from social media data, especially the Twitter platform, in regards to cities and urban environments. The first part of the chapter discusses a range of examples that make use of big data and the mapping of digital social network data. The second part discusses the way the data is collected and processed. An important section is dedicated to the aspects of ethical considerations. A summary and an outlook are discussed at the end.

The Use of Social Media for Urban Planning

2019 ◽  
pp. 1049-1070
Author(s):  
Fabian Neuhaus
Keyword(s):  
Social Media ◽  
Big Data ◽  
Urban Areas ◽  
Large Scale ◽  
Urban Environments ◽  
Data Systems ◽  
User Management ◽  
Social Nature ◽  
User Data ◽  
Use Of Social Media

User data created in the digital context has increasingly been of interest to analysis and spatial analysis in particular. Large scale computer user management systems such as digital ticketing and social networking are creating vast amount of data. Such data systems can contain information generated by potentially millions of individuals. This kind of data has been termed big data. The analysis of big data can in its spatial but also in a temporal and social nature be of much interest for analysis in the context of cities and urban areas. This chapter discusses this potential along with a selection of sample work and an in-depth case study. Hereby the focus is mainly on the use and employment of insight gained from social media data, especially the Twitter platform, in regards to cities and urban environments. The first part of the chapter discusses a range of examples that make use of big data and the mapping of digital social network data. The second part discusses the way the data is collected and processed. An important section is dedicated to the aspects of ethical considerations. A summary and an outlook are discussed at the end.

scholarly journals Making Inferences Using Incidentally Collected Data

2016 ◽  
Author(s):  
Jonathan Mellon
Keyword(s):  
Social Media ◽  
Big Data ◽  
The Internet ◽  
User Interactions ◽  
Social Media Data ◽  
Broad Definition ◽  
User Data ◽  
The Cost ◽  
Media Data

This chapter discusses the use of large quantities of incidentallycollected data (ICD) to make inferences about politics. This type of datais sometimes referred to as “big data” but I avoid this term because of itsconflicting definitions (Monroe, 2012; Ward & Barker, 2013). ICD is datathat was created or collected primarily for a purpose other than analysis.Within this broad definition, this chapter focuses particularly on datagenerated through user interactions with websites. While ICD has beenaround for at least half a century, the Internet greatly expanded theavailability and reduced the cost of ICD. Examples of ICD include data onInternet searches, social media data, and user data from civic platforms.This chapter briefly explains some sources and uses of ICD and thendiscusses some of the potential issues of analysis and interpretation thatarise when using ICD, including the different approaches to inference thatresearchers can use.

scholarly journals Fear of Missing Out as a Predictor of Problematic Social Media Use and Phubbing Behavior among Flemish Adolescents

2018 ◽  
Vol 15 (10) ◽  
pp. 2319 ◽  
Author(s):  
Vittoria Franchina ◽  
Mariek Vanden Abeele ◽  
Antonius van Rooij ◽  
Gianluca Lo Coco ◽  
Lieven De Marez
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Media Use ◽  
Survey Study ◽  
Social Media Use ◽  
Fear Of Missing Out ◽  
Social Media Platforms ◽  
Large Scale Survey ◽  
Problematic Social Media Use ◽  
Use Of Social Media

Fear-of-missing-out (FOMO) refers to feelings of anxiety that arise from the realization that you may be missing out on rewarding experiences that others are having. FOMO can be identified as an intra-personal trait that drives people to stay up to date of what other people are doing, among others on social media platforms. Drawing from the findings of a large-scale survey study among 2663 Flemish teenagers, this study explores the relationships between FOMO, social media use, problematic social media use (PSMU) and phubbing behavior. In line with our expectations, FOMO was a positive predictor of both how frequently teenagers use several social media platforms and of how many platforms they actively use. FOMO was a stronger predictor of the use of social media platforms that are more private (e.g., Facebook, Snapchat) than platforms that are more public in nature (e.g., Twitter, Youtube). FOMO predicted phubbing behavior both directly and indirectly via its relationship with PSMU. These findings support extant research that points towards FOMO as a factor explaining teenagers’ social media use.

TWEET DATA EXTRACTOR FOR CREATING A TWITTER TRAFFIC MAP MASHUP

2015 ◽  
Vol 75 (10) ◽  
Author(s):  
Amirul Afif Jasmi ◽  
Mohamad Hafis Izran Ishak ◽  
Nurul Hawani Idris
Keyword(s):  
Social Media ◽  
Big Data ◽  
Traffic Control ◽  
Transportation Planning ◽  
General Public ◽  
New Technologies ◽  
The Road ◽  
Current Information ◽  
Road Users ◽  
Use Of Social Media

Over recent years, there has been a growth of interest in the use of social media including Facebook and Twitter by the authorities to share and updates current information to the general public. The technology has been used for a variety of purposes including traffic control and transportation planning. There is a concern that the use of new technologies, including social media will lead to data abundance that requires effective operational resources to interpret the big data. This paper proposes a tweet data extractor to extract the traffic tweet by the authority and visualise the reports and mash up on top of online map, namely Twitter map. Visualisation of traffic tweet on a map could assist a user to effectively interpret the text based Twitter report by a location based map viewer. Hence, it could ease the process of planning itinerary by the road users. 

The use of social media by higher education institutions

2020 ◽  
Vol 65 (2) ◽  
pp. 30-42
Author(s):  
Jacek Maślankowski ◽  
Łukasz Brzezicki
Keyword(s):  
Higher Education ◽  
Social Media ◽  
Big Data ◽  
Competitive Advantage ◽  
Full Advantage ◽  
Higher Education Institutions ◽  
Marketing Activities ◽  
Use Of Social Media ◽  
Marketing Methods

Higher education institutions have been using, to an increasing extent, various marketing methods and tools, which are becoming a decisive factor in building their competitive advantage and achieving success. In order to initiate and maintain long-term relationships with their communities and to conduct other marketing activities, higher education institutions have been increasingly often using social media, which has enabled them to actively create their image. The aim of this study is to utilize big data methods and tools to measure the scale of the use of social media by the higher education sector. The research carried out in the first quarter of 2019 demonstrates that large higher education institutions, i.e. those with over 1696 students (according to the adopted classification), use social media to communicate current news to a larger extent than the smaller ones. A significantly smaller percentage of mediumsized higher education institutions (223-1695 students) and small ones (up to 222 students) have accounts in social media, thus failing to take full advantage of the potential of these media. Higher education institutions use social media mainly to promote events they organise.

scholarly journals Social Media and Journalism Ethics in Nigeria: A Study of Journalists in Kwara State of Nigeria

2021 ◽  
Vol 04 (03) ◽  
Author(s):  
Bernice Titilola Gbadeyan ◽  
Keyword(s):  
Social Media ◽  
New Media ◽  
Large Scale ◽  
Code Of Ethics ◽  
Survey Method ◽  
Journalism Ethics ◽  
Broadcast Media ◽  
The North ◽  
Use Of Social Media ◽  
The Impact

Journalism is a term that has been used to describe the act of gathering and reporting news, either through the print media which includes newspaper, magazine or through the broadcast media to mention television, radio broadcasting system and recently journalism has been extended throughout the world through unrestricted use of social media, whereby the act of gathering and disseminating of news is done without restraint. Conversely, one important thing to note about journalism is the ethics that enhance the profession, its notes worthy to know that any information that is disseminated via any media should be ethically standard. The new media has on a large scale given the opportunity to a whole large number of people to practice journalism without them knowing the ethics that guide the profession, which is affecting the dynamics of the profession. Therefore this study is based on assessing the impact of a new communication system on journalism; whether social media promote the ethics of journalism profession and to know if social media journalists are in compliance with the journalism code of ethics in their dissemination of news and information. In this research, the survey method was adopted and the north-central geo-political zone, Kwara state to be précised was selected for the study.

scholarly journals A Mixed Methods Systematic Scoping Review of the Use of Big Data in Early Intervention Research: Ethical and Practical Implications

2018 ◽  
Author(s):  
Sondra M Stegenga ◽  
Kelley Munger ◽  
Jane Squires ◽  
Daniel Anderson
Keyword(s):  
Big Data ◽  
Mixed Methods ◽  
Early Intervention ◽  
Scoping Review ◽  
Large Scale ◽  
Research Evaluation ◽  
Developmentally Appropriate ◽  
Data Systems ◽  
Ethical Practices ◽  
Department Of Health

Big data holds immense potential for innovation and new understanding in research, evaluation, practice, and policy related to young children and their families. Although big data is a relatively new concept, particularly in early intervention systems (EI), recent pushes for data systems alignment in EI and education have propelled the use of large-scale integrated data systems in recent years (U.S. Department of Health and Human Services & the U.S. Department of Education, 2016). This combined with a plethora of new and rapidly increasing data sources has created a new data world. In response, research methodology, ethics, and tools need to be examined to ensure developmentally appropriate and ethical practices in research. A mixed methods systematic scoping review was conducted to gain a foundational understanding of the literature on big data use in EI settings. Strengths, challenges, systems-level needs, and implications for researchers, administrators, and policy makers are included.

scholarly journals Social Media-based User Embedding: A Literature Review

2019 ◽  
Author(s):  
Shimei Pan ◽  
Tao Ding
Keyword(s):  
Social Media ◽  
High Performance ◽  
Large Scale ◽  
Ground Truth ◽  
Representation Learning ◽  
Success Stories ◽  
Recent Success ◽  
User Data ◽  
Low Dimensional ◽  
And Behavior

Automated representation learning is behind many recent success stories in machine learning. It is often used to transfer knowledge learned from a large dataset (e.g., raw text) to tasks for which only a small number of training examples are available. In this paper, we review recent advance in learning to represent social media users in low-dimensional embeddings. The technology is critical for creating high performance social media-based human traits and behavior models since the ground truth for assessing latent human traits and behavior is often expensive to acquire at a large scale. In this survey, we review typical methods for learning a unified user embeddings from heterogeneous user data (e.g., combines social media texts with images to learn a unified user representation). Finally we point out some current issues and future directions.

scholarly journals Sentimen Analisis Terhadap Komentar Konsumen Terhadap Produk Penjualan Toko Online Menggunakan Metode K-Means

2020 ◽  
Vol 19 (2) ◽  
pp. 207-213
Author(s):  
Andris Faesal ◽  
Aziz Muslim ◽  
Aditya Hastami Ruger ◽  
Kusrini Kusrini
Keyword(s):  
Social Media ◽  
Big Data ◽  
Programming Language ◽  
Raw Data ◽  
R Programming Language ◽  
R Programming ◽  
Use Of Social Media ◽  
Media Sentiment ◽  
Existing Data

In this big data era, the use of social media often makes posts in his social media accounts in the form of opinions on events and things around him. One of them is making a post that gives an opinion on the events and items around it. One of them is making a post that gives an opinion on an item that has just been purchased, so that the effect is on other users who are connected to it. The more people who know it, then indirectly people will get to know the item. For that from the description of the problem above, this study raises an idea to make an analysis of social media sentiment which aims to provide a decision of consumer opinion on social media on sales products. As for the several stages of the method for the research, namely from the collection of data carried out by collecting existing data in tweets from social media Twitter using the R programming language. The data produces raw or raw data associated with sales items. With the K-means method as inputting, after each group is known from the K-Means output

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