Mouse Tracking Measures and Movement Patterns with Application for Online Surveys

Cepeda, Cátia; Rodrigues, João; Dias, Maria Camila; Oliveira, Diogo; Rindlisbacher, Dina; Cheetham, Marcus; Gamboa, Hugo

doi:10.1007/978-3-319-99740-7_3

Cited by 17 publications

(6 citation statements)

References 21 publications

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“…A priori, the collection and processing of those data may be regarded as an innocuous practice. However, the pieces of evidence currently available [34,113] seem to suggest that mouse tracking (when deployed presumably for viewability purposes) cannot but increase users' privacy risk. In recent years, there has been a tremendous improvement in mousebased biometric systems and currently, it is considered a key fingerprint of browsing behavior to track users throughout the web [125].…”

Section: A Role Of Viewability Within the Challenges Of The Online Advertising Ecosystemmentioning

confidence: 99%

A Survey of the Role of Viewability Within the Online Advertising Ecosystem

et al. 2021

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We also want to thank ExoClick for their support in conducting this research. The project that gave rise to these results received the support of a fellowship from "la Caixa" Foundation (ID 100010434) and from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 847648. The fellowship code is LCF/BQ/PR20/11770009. We also acknowledge support from the Spanish Ministry of Economy and Competitiveness through the Juan de la Cierva Formación program (FJCI-2017-34926) ABSTRACT Within the online advertising ecosystem, viewability is defined as the metric that measures if an ad impression had the chance of being viewable by a potential consumer. Although this metric has been presented as a potential game-changer within the ad industry, it has not been fully adopted by the stakeholders, mainly due to disagreement between the different parties on the standards to implement and measure it, and its potential benefits and drawbacks. In this study, we present a survey of the role that viewability can have on the main challenges of the online advertising ecosystem depicting the main applications, benefits and issues. With this objective, we provide an overall picture of how viewability can fit within the ecosystem, which can help the different stakeholders to work on its adoption, integration and establishing a research agenda.

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Section: A Role Of Viewability Within the Challenges Of The Online Advertising Ecosystemmentioning

confidence: 99%

A Survey of the Role of Viewability Within the Online Advertising Ecosystem

et al. 2021

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“…• Survey analysis: In cases where it is important to understand how people fill surveys [12]. The collected data may be used to infer personality traits, or to analyse how the structure of the survey affects the way that the survey respondent fills out the survey.…”

Section: B Application Areasmentioning

confidence: 99%

“…Some researchers take a more complex approach to evaluate users' browsing behaviour, such as the application of text classification [6] or an algorithm to calculate the weight of a particular web page based on related actions [7]. Several scientific studies have focussed on the extraction of personality traits from keyboard and mouse behaviour [8]- [11], while others have detected and described repeated mouse movement patterns [12]. The necessary raw data for these kinds of analyses are not normally available on most tracking tools and services, which depending on the goals, are more dedicated to analysis of e-metric values or specific behaviours.…”

Section: Introductionmentioning

confidence: 99%

Latent: A Flexible Data Collection Tool to Research Human Behavior in the Context of Web Navigation

et al. 2019

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Internet usage has grown dramatically since the early years of its inception. The rich field of data provided by internet users in interaction with digital media content can provide insight into web-based navigation behavior and underlying psychological dimensions. Human-computer interaction in the web is an underutilized source of data for understanding human online behavior. While researchers and usability testing services do use these sources to analyze human behavior and user experience, access to the diverse range of other potentially useful data available during web-based interaction for research is limited. In this paper, we propose a novel tool in the form of a web browser extension, referred to as Latent, which can be used to simultaneously capture information from different sources while users interact with digital content. The data acquisition capabilities of Latent makes it suitable for various research purposes, ranging from studies of usability to decision-making and personality. A particular advantage of Latent is that the method and control of data acquisition is completely transparent to the user. We present the architecture of the web browser extension, describe the data that can be acquired, and report on the residual impact of the tool on the user's computer processing resources.INDEX TERMS Browser extension, data acquisition, Human-computer interaction, web search.

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“…Additionally, most of the available research focuses on qualitative prediction instead of quantitative predicting a user’s interest in various items (Kaur and Singh, 2016; Souza et al , 2019; Arapakis et al , 2014; Katerina and Nicolaos, 2018; Cepeda et al , 2018; Wang et al , 2016; Gu et al , 2016; Peska and Vojtas, 2016).…”

Section: Introductionmentioning

confidence: 99%

Modeling user preferences in online stores based on user mouse behavior on page elements

SadighZadeh

Kaedi

2022

JSIT

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Purpose Online businesses require a deep understanding of their customers’ interests to innovate and develop new products and services. Users, on the other hand, rarely express their interests explicitly. The purpose of this study is to predict users’ implicit interest in products of an online store based on their mouse behavior through various product page elements. Design/methodology/approach First, user mouse behavior data is collected throughout an online store website. Next, several mouse behavioral features on the product pages elements are extracted and finally, several models are extracted using machine learning techniques to predict a user’s interest in a product. Findings The results indicate that focusing on mouse behavior on various page elements improves user preference prediction accuracy compared to other available methods. Research limitations/implications User mouse behavior was used to predict consumer preferences in this study, therefore gathering additional data on user demography, personality dimensions and emotions may significantly aid in accurate prediction. Originality/value Mouse behavior is the most repeated behavior during Web page browsing through personal computers and laptops. It has been referred to as implicit feedback in some studies and an effective way to ascertain user preference. In these studies, mouse behavior is only assessed throughout the entire Web page, lacking a focus on different page elements. It is assumed that in online stores, user interaction with key elements of a product page, such as an image gallery, user reviews, a description and features and specifications, can be highly informative and aid in determining the user’s interest in that product.

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Mouse Tracking Measures and Movement Patterns with Application for Online Surveys

Cited by 17 publications

References 21 publications

A Survey of the Role of Viewability Within the Online Advertising Ecosystem

A Survey of the Role of Viewability Within the Online Advertising Ecosystem

Latent: A Flexible Data Collection Tool to Research Human Behavior in the Context of Web Navigation

Modeling user preferences in online stores based on user mouse behavior on page elements

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