Extended Implementation Method for Virtual Sensors: Web-Based Real-Time Transportation Data Collection and Analysis for Incident Management

2015 ◽  
Vol 2528 (1) ◽  
pp. 27-37 ◽  
Author(s):  
Abdullah Kurkcu ◽  
Ender Faruk Morgul ◽  
Kaan Ozbay
Keyword(s):  
Social Media ◽  
Data Collection ◽  
Travel Time ◽  
Real Time ◽  
Open Data ◽  
Data Sources ◽  
Incident Management ◽  
Virtual Sensors ◽  
Social Media Data ◽  
Media Data

Open data sources and social media data are gaining increasing attention as important information providers in transportation and incident management. In this paper, practical evidence for the emerging potential of online and open data sources is presented. The authors’ previous research on virtual sensors is combined and extended by integrating real-time incident information and social media network engagement. The fundamental contribution of this paper is the development of an extended virtual sensor framework to provide an automated travel time data collection method as incidents occur. In addition, social media data can be useful for more effective real-time incident response. The proposed framework can easily be modified and used to evaluate travel time effects of incidents on roadways and clearance times and to make use of social media data in obtaining time-critical incident-related information.

scholarly journals Using Social Media Data to Plan for Tourism

2020 ◽  
Vol 39 (3) ◽  
pp. 125-138
Author(s):  
Alina Zajadacz ◽  
Aleksandra Minkwitz
Keyword(s):  
Social Media ◽  
Local Level ◽  
Data Sources ◽  
Communication Tool ◽  
Tourism Planning ◽  
Communication Tools ◽  
Social Media Data ◽  
Planning Methods ◽  
The One ◽  
Media Data

AbstractThe purpose of the article is to present the concept of using social media (SM) as data sources and communication tools, useful at the various stages of planning, implementing and monitoring the effects of tourism development on a local level. The first part discusses the stages of planning, then presents the characteristics of SM, along with a discussion of the issues presented in the literature to this date. The next part presents data sources and methods of research on SM and functions that they can perform in tourism. The concept presented, on the one hand, reviews the perspectives of practical use of SM as a communication tool and source of data and, on the other hand, the challenges related to the need to further deepen research on tourism planning methods that are adequate to the continuously changing environment.

Social Media-Based Data Collection and Analysis in Educational Research

2020 ◽  
pp. 54-77
Author(s):  
Liuli Huang
Keyword(s):  
Social Media ◽  
Data Collection ◽  
Educational Research ◽  
Media In Education ◽  
Social Media Data ◽  
Educational Processes ◽  
Data Collection And Analysis ◽  
Media Data ◽  
Social Media In Education

The past decades have brought many changes to education, including the role of social media in education. Social media data offer educational researchers first-hand insights into educational processes. This is different from most traditional and often obtrusive data collection methods (e.g., interviews and surveys). Many researchers have explored the role of social media in education, such as the value of social media in the classroom, the relationship between academic achievement and social media. However, the role of social media in educational research, including data collection and analysis from social media, has been examined to a far lesser degree. This study seeks to discuss the potential of social media for educational research. The purpose of this chapter is to illustrate the process of collecting and analyzing social media data through a pilot study of current math educational conditions.

scholarly journals A Big Data Platform for Real Time Analysis of Signs of Depression in Social Media

2020 ◽  
Vol 17 (13) ◽  
pp. 4752 ◽  
Author(s):  
Rodrigo Martínez-Castaño ◽  
Juan C. Pichel ◽  
David E. Losada 
Keyword(s):  
Social Media ◽  
Real Time ◽  
Public Health Surveillance ◽  
Time Analysis ◽  
Social Media Data ◽  
Real Time Processing ◽  
Processing Elements ◽  
Real Time Analysis ◽  
Data Platform ◽  
Media Data

In this paper we propose a scalable platform for real-time processing of Social Media data. The platform ingests huge amounts of contents, such as Social Media posts or comments, and can support Public Health surveillance tasks. The processing and analytical needs of multiple screening tasks can easily be handled by incorporating user-defined execution graphs. The design is modular and supports different processing elements, such as crawlers to extract relevant contents or classifiers to categorise Social Media. We describe here an implementation of a use case built on the platform that monitors Social Media users and detects early signs of depression.

scholarly journals Incremental Learning with Social Media Data to Predict Near Real-Time Events

2014 ◽  
pp. 180-191
Author(s):  
Duc Kinh Le Tran ◽  
Cécile Bothorel ◽  
Pascal Cheung Mon Chan ◽  
Yvon Kermarrec
Keyword(s):  
Social Media ◽  
Real Time ◽  
Incremental Learning ◽  
Social Media Data ◽  
Media Data

scholarly journals Linking Twitter and survey data: asymmetry in quantity and its impact

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Tarek Al Baghal ◽  
Alexander Wenz ◽  
Luke Sloan ◽  
Curtis Jessop
Keyword(s):  
Social Media ◽  
Survey Data ◽  
Linked Data ◽  
Social Research ◽  
Data Sources ◽  
Social Media Data ◽  
Twitter Data ◽  
Unique Source ◽  
Methodological Aspects ◽  
Media Data

AbstractLinked social media and survey data have the potential to be a unique source of information for social research. While the potential usefulness of this methodology is widely acknowledged, very few studies have explored methodological aspects of such linkage. Respondents produce planned amounts of survey data, but highly variant amounts of social media data. This study explores this asymmetry by examining the amount of social media data available to link to surveys. The extent of variation in the amount of data collected from social media could affect the ability to derive meaningful linked indicators and could introduce possible biases. Linked Twitter data from respondents to two longitudinal surveys representative of Great Britain, the Innovation Panel and the NatCen Panel, show that there is indeed substantial variation in the number of tweets posted and the number of followers and friends respondents have. Multivariate analyses of both data sources show that only a few respondent characteristics have a statistically significant effect on the number of tweets posted, with the number of followers being the strongest predictor of posting in both panels, women posting less than men, and some evidence that people with higher education post less, but only in the Innovation Panel. We use sentiment analyses of tweets to provide an example of how the amount of Twitter data collected can impact outcomes using these linked data sources. Results show that more negatively coded tweets are related to general happiness, but not the number of positive tweets. Taken together, the findings suggest that the amount of data collected from social media which can be linked to surveys is an important factor to consider and indicate the potential for such linked data sources in social research.

The impact of social media on human interaction in an organisation based on real-time social media data

2019 ◽  
Vol 4 (3) ◽  
pp. 260
Author(s):  
Sharifah Sakinah Syed Ahmad ◽  
Anis Naseerah Binti Shaik Osman ◽  
Halizah Basiron
Keyword(s):  
Social Media ◽  
Real Time ◽  
Human Interaction ◽  
Social Media Data ◽  
The Impact ◽  
Media Data

New Data Sources in Social Science Research: Things to Know Before Working With Reddit Data

2019 ◽  
pp. 089443931989330 ◽  
Author(s):  
Ashley Amaya ◽  
Ruben Bach ◽  
Florian Keusch ◽  
Frauke Kreuter
Keyword(s):  
Social Media ◽  
Social Science ◽  
Social Science Research ◽  
Science Research ◽  
Lessons Learned ◽  
Data Sources ◽  
Front Page ◽  
Social Media Data ◽  
New Research ◽  
Media Data

Social media are becoming more popular as a source of data for social science researchers. These data are plentiful and offer the potential to answer new research questions at smaller geographies and for rarer subpopulations. When deciding whether to use data from social media, it is useful to learn as much as possible about the data and its source. Social media data have properties quite different from those with which many social scientists are used to working, so the assumptions often used to plan and manage a project may no longer hold. For example, social media data are so large that they may not be able to be processed on a single machine; they are in file formats with which many researchers are unfamiliar, and they require a level of data transformation and processing that has rarely been required when using more traditional data sources (e.g., survey data). Unfortunately, this type of information is often not obvious ahead of time as much of this knowledge is gained through word-of-mouth and experience. In this article, we attempt to document several challenges and opportunities encountered when working with Reddit, the self-proclaimed “front page of the Internet” and popular social media site. Specifically, we provide descriptive information about the Reddit site and its users, tips for using organic data from Reddit for social science research, some ideas for conducting a survey on Reddit, and lessons learned in merging survey responses with Reddit posts. While this article is specific to Reddit, researchers may also view it as a list of the type of information one may seek to acquire prior to conducting a project that uses any type of social media data.

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