Answering Mobile Surveys With Images: An Exploration Using a Computer Vision API. (October 2019)

• Record Type: Journal Article
• Authors: Bosch, Oriol J.; Revilla, Melanie; Paura, Ezequiel
• Is Part Of: Social science computer review. Volume 37:Number 5(2019)
• Page Start: 669
• Page End: 683
• Publication Date: 2019-10
• Subjects: mobile web survey -- image recognition -- computer vision -- API -- smartphone -- new data types; Social sciences -- Data processing -- Periodicals; Computers -- Social aspects -- Periodicals; Microcomputers -- Periodicals; Sciences sociales -- Informatique -- Périodiques; Micro-ordinateurs -- Périodiques; 300.285
• DOI: 10.1177/0894439318791515
• Languages: English
• ISSNs: 0894-4393

Abstract

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.