scholarly journals Assessing the Reliability of Relevant Tweets and Validation Using Manual and Automatic Approaches for Flood Risk Communication

2020 ◽  
Vol 9 (9) ◽  
pp. 532 ◽  
Author(s):  
Xiaohui Liu ◽  
Bandana Kar ◽  
Francisco Alejandro Montiel Ishino ◽  
Chaoyang Zhang ◽  
Faustine Williams
Keyword(s):  
Risk Communication ◽  
Emergency Response ◽  
Application Programming Interface ◽  
Accurate Information ◽  
Text And Image ◽  
Application Programming ◽  
Text Information ◽  
Efficient Processing ◽  
Information Verification ◽  
Vision Application

While Twitter has been touted as a preeminent source of up-to-date information on hazard events, the reliability of tweets is still a concern. Our previous publication extracted relevant tweets containing information about the 2013 Colorado flood event and its impacts. Using the relevant tweets, this research further examined the reliability (accuracy and trueness) of the tweets by examining the text and image content and comparing them to other publicly available data sources. Both manual identification of text information and automated (Google Cloud Vision, application programming interface (API)) extraction of images were implemented to balance accurate information verification and efficient processing time. The results showed that both the text and images contained useful information about damaged/flooded roads/streets. This information will help emergency response coordination efforts and informed allocation of resources when enough tweets contain geocoordinates or location/venue names. This research will identify reliable crowdsourced risk information to facilitate near real-time emergency response through better use of crowdsourced risk communication platforms.

scholarly journals Extracting Reliable Twitter Data for Flood Risk Communication using Manual Assessment and Google Vision API from Text and Images

2020 ◽  
Author(s):  
Xiaohui Liu ◽  
Bandana Kar ◽  
Francisco Alejandro Montiel Ishino ◽  
Chaoyang Zhang ◽  
Faustine Williams
Keyword(s):  
Risk Communication ◽  
Emergency Response ◽  
Accurate Information ◽  
Allocation Of Resources ◽  
Text And Image ◽  
Street Networks ◽  
Twitter Data ◽  
Text Information ◽  
Efficient Processing ◽  
Information Verification

While Twitter has been touted to provide up-to-date information about hazard events, the reliability of tweets is still a concern. Our previous publication extracted relevant tweets containing information about the 2013 Colorado flood event and its impacts. Using the relevant tweets, this research further examined the reliability (accuracy and trueness) of the tweets by examining the text and image content and comparing them to other publicly available data sources. Both manual identification of text information and automated (Google Cloud Vision API) extraction of images were implemented to balance accurate information verification and efficient processing time. The results showed that both the text and images contained useful information about damaged/flooded roads/street networks. This information will help emergency response coordination efforts and informed allocation of resources when enough tweets contain geocoordinates or locations/venue names. This research will help identify reliable crowdsourced risk information to enable near-real time emergency response through better use of crowdsourced risk communication platforms.

Answering Mobile Surveys With Images: An Exploration Using a Computer Vision API

2018 ◽  
Vol 37 (5) ◽  
pp. 669-683 ◽  
Author(s):  
Oriol J. Bosch ◽  
Melanie Revilla ◽  
Ezequiel Paura
Keyword(s):  
Computer Vision ◽  
Application Programming Interface ◽  
Web Survey ◽  
Online Surveys ◽  
Mobile Web ◽  
Application Programming ◽  
The Moment ◽  
Human Coder ◽  
Programming Interface ◽  
Vision Application

Most mobile devices nowadays have a camera. Besides, posting and sharing images have been found as one of the most frequent and engaging Internet activities. However, to our knowledge, no research has explored the feasibility of asking respondents of online surveys to upload images to answer survey questions. The main goal of this article is to investigate the viability of asking respondents of an online opt-in panel to upload during a mobile web survey: First, a photo taken in the moment, and second, an image already saved on their smartphone. In addition, we want to test to what extent the Google Vision application programming interface (API), which can label images into categories, produces similar tags than a human coder. Overall, results from a survey conducted among millennials in Spain and Mexico ( N = 1,614) show that more than half of the respondents uploaded an image. Of those, 77.3% and 83.4%, respectively, complied with what the question asked. Moreover, respectively, 52.4% and 65.0% of the images were similarly codified by the Google Vision API and the human coder. In addition, the API codified 1,818 images in less than 5 min, whereas the human coder spent nearly 35 hours to complete the same task.

Sistem Pengawasan Aktifitas Penggunaan Smartphone Android

2018 ◽  
Vol 9 (1) ◽  
pp. 24-31
Author(s):  
Rudianto Rudianto ◽  
Eko Budi Setiawan
Keyword(s):  
Application Programming Interface ◽  
Third Party ◽  
Image Content ◽  
Image File ◽  
Application Programming ◽  
Index Terms ◽  
Programming Interface

Availability the Application Programming Interface (API) for third-party applications on Android devices provides an opportunity to monitor Android devices with each other. This is used to create an application that can facilitate parents in child supervision through Android devices owned. In this study, some features added to the classification of image content on Android devices related to negative content. In this case, researchers using Clarifai API. The result of this research is to produce a system which has feature, give a report of image file contained in target smartphone and can do deletion on the image file, receive browser history report and can directly visit in the application, receive a report of child location and can be directly contacted via this application. This application works well on the Android Lollipop (API Level 22). Index Terms— Application Programming Interface(API), Monitoring, Negative Content, Children, Parent.

Is JUUL marketing to kids on social media? Analysis of JUUL Twitter Followers� Age (Preprint)

2018 ◽  
Author(s):  
Annice Kim ◽  
Robert Chew ◽  
Michael Wenger ◽  
Margaret Cress ◽  
Thomas Bukowski ◽  
...  
Keyword(s):  
Social Media ◽  
Application Programming Interface ◽  
Media Analysis ◽  
Prediction Algorithm ◽  
Social Media Networks ◽  
Verification Methods ◽  
Twitter Account ◽  
Application Programming ◽  
Age Verification ◽  
Age Category

BACKGROUND JUUL is an electronic nicotine delivery system (ENDS) resembling a USB device that has become rapidly popular among youth. Recent studies suggest that social media may be contributing to its popularity. JUUL company claims their products are targeted for adult current smokers but recent surveillance suggests youth may be exposed to JUUL products online. To date, there has been little attention on restricting youth exposure to age restricted products on social media. OBJECTIVE The objective of this study was to utilize a computational age prediction algorithm to determine the extent to which underage youth are being exposed to JUUL’s marketing practices on Twitter. METHODS We examined all of @JUULvapor’s Twitter followers in April 2018. For followers with a public account, we obtained their metadata and last 200 tweets using the Twitter application programming interface. We ran a series of classification models to predict whether the account following @JUULvapor was an underage youth or an adult. RESULTS Out of 9,077 individuals following @JUULvapor Twitter account, a three-age category model predicted that 44.9% are 13 to 17 years old (N=4,078), 43.6% are 18 to 24 years old (N=3,957), and 11.5% are 25 years old or older (N=1,042); and a two-age category model predicted that 80.6% (N=7,313) are under 21 years old. CONCLUSIONS Despite a disclaimer that followers must be of legal age to purchase tobacco products, the majority of JUUL followers on Twitter are under age. This suggests that ENDS brands and social media networks need to implement more stringent age-verification methods to protect youth from age-restricted content.

As-RaD System as a Design Model of the Network Automation Configuration System Based on the REST-API and Django Framework

2020 ◽  
pp. 291-298
Author(s):  
Adian Fatchur Rochim ◽  
Abda Rafi ◽  
Adnan Fauzi ◽  
Kurniawan Teguh Martono
Keyword(s):  
Information Technology ◽  
Application Programming Interface ◽  
Design Model ◽  
Automation System ◽  
Network Configuration ◽  
Web Based ◽  
Representational State Transfer ◽  
Application Programming ◽  
Rest Api ◽  
Python Programming

The use of information technology these days are very high. From business through education activities tend to use this technology most of the time. Information technology uses computer networks for integration and management data. To avoid business problems, the number of network devices installed requires a manageable network configuration for easier maintenance. Traditionally, each of network devices has to be manually configured by network administrators. This process takes time and inefficient. Network automation methods exist to overcome the repetitive process. Design model uses a web-based application for maintenance and automates networking tasks. In this research, the network automation system implemented and built a controller application that used REST API (Representational State Transfer Application Programming Interface) architecture and built by Django framework with Python programming language. The design modeled namely As-RaD System. The network devices used in this research are Cisco CSR1000V because it supports REST API communication to manage its network configuration and could be placed on the server either. The As-RaD System provides 75% faster performance than Paramiko and 92% than NAPALM.

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