Web Browsing Behavior Analysis and Interactive Hypervideo

© ACM, 2013. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in, ACM Transactions on the Web, Vol. 7, No. 4, Article 20, Publication date: October 2013.http://doi.acm.org/ 10.1145/2529995.2529996[EN] Processing data on any sort of user interaction is well known to be cumbersome and mostly time consuming. In order to assist researchers in easily inspecting fine-grained browsing data, current tools usually display user interactions as mouse cursor tracks, a video-like visualization scheme. However, to date, traditional online video inspection has not explored the full capabilities of hypermedia and interactive techniques. In response to this need, we have developed SMT 2ǫ, a Web-based tracking system for analyzing browsing behavior using feature-rich hypervideo visualizations. We compare our system to related work in academia and the industry, showing that ours features unprecedented visualization capabilities. We also show that SMT 2ǫ efficiently captures browsing data and is perceived by users to be both helpful and usable. A series of prediction experiments illustrate that raw cursor data are accessible and can be easily handled, providing evidence that the data can be used to construct and verify research hypotheses. Considering its limitations, it is our hope that SMT 2ǫ will assist researchers, usability practitioners, and other professionals interested in understanding how users browse the Web.This work was partially supported by the MIPRCV Consolider Ingenio 2010 program (CSD2007-00018) and the TIN2009-14103-C03-03 project. It is also supported by the 7th Framework Program of the European Commision (FP7/2007-13) under grant agreement No. 287576 (CasMaCat).Leiva Torres, LA.; Vivó Hernando, RA. (2013). Web Browsing Behavior Analysis and Interactive Hypervideo. ACM Transactions on the Web. 7(4):20:1-20:28. https://doi.org/10.1145/2529995.2529996S20:120:287

Analyzing user feedback of on-line communities

The economic success of the World Wide Web makes it a highly competitive environment for web businesses. For this reason, it is crucial for web business owners to learn what their customers want. This thesis provides a conceptual framework and an implementation of a system that helps to better understand the behavior and potential interests of web site visitors by accounting for both explicit and implicit feedback. This thesis is divided into two parts. The first part is rooted in computer science and information systems and uses graph theory and an extended click-stream analysis to define a framework and a system tool that is useful for analyzing web user behavior by calculating the interests of the users. The second part is rooted in behavioral economics, mathematics, and psychology and is investigating influencing factors on different types of web user choices. In detail, a model for the cognitive process of rating products on the Web is defined and an importance hierarchy of the influencing factors is discovered. Both parts make use of techniques from a variety of research fields and, therefore, contribute to the area of Web Science.Welche Interessen verfolgen meine Webseiten-Nutzer? Diese Frage beschäftigt viele Betreiber von Online-Unternehmen. Um in einem solch hart umkämpften Markt wie dem des Internetbusiness erfolgreich bestehen zu können, ist es für die Entscheidungsträger dieser Unternehmen ausschlaggebend zu verstehen, welche Ziele ihre Kunden verfolgen. Hauptziel der vorliegenden Arbeit ist es, diese Frage mit Hilfe eines konzeptionellen Bezugssystems und der Implementierung eines Systems zu beantworten. Beide Elemente berücksichtigen sowohl das Verhalten, als auch das explizite und das implizite Feedback der Webseiten-Nutzer. Der vorgeschlagene Lösungsansatz unterstützt Betreiber von Online-Unternehmen dabei ihre Kunden besser zu verstehen. Dies geschieht durch das Beobachten und Auswerten des Kundenverhaltens, um daraus die vermuteten Kundeninteressen zu berechnen. Außerdem werden, um den Prozess des Feedbackgebens besser zu verstehen, diejenigen Faktoren untersucht, die die Auswahl des Webseiten-Nutzers beim Feedbackgeben beeinflussen. Folgende Forschungsfragen werden in dieser Arbeit im Hinblick auf unterschiedliche Aspekte des Feedbacks von Webseiten-Nutzern untersucht: * Was lernen wir aus der Analyse des explizit und des implizit durch die Webseiten-Nutzer ausgeführten Feedbacks? * Was sind die wichtigsten Faktoren, die das Feedback von Webseiten-Nutzern beeinflussen

Detecting Abnormal Behavior in Web Applications

The rapid advance of web technologies has made the Web an essential part of our daily lives. However, network attacks have exploited vulnerabilities of web applications, and caused substantial damages to Internet users. Detecting network attacks is the first and important step in network security. A major branch in this area is anomaly detection. This dissertation concentrates on detecting abnormal behaviors in web applications by employing the following methodology. For a web application, we conduct a set of measurements to reveal the existence of abnormal behaviors in it. We observe the differences between normal and abnormal behaviors. By applying a variety of methods in information extraction, such as heuristics algorithms, machine learning, and information theory, we extract features useful for building a classification system to detect abnormal behaviors.;In particular, we have studied four detection problems in web security. The first is detecting unauthorized hotlinking behavior that plagues hosting servers on the Internet. We analyze a group of common hotlinking attacks and web resources targeted by them. Then we present an anti-hotlinking framework for protecting materials on hosting servers. The second problem is detecting aggressive behavior of automation on Twitter. Our work determines whether a Twitter user is human, bot or cyborg based on the degree of automation. We observe the differences among the three categories in terms of tweeting behavior, tweet content, and account properties. We propose a classification system that uses the combination of features extracted from an unknown user to determine the likelihood of being a human, bot or cyborg. Furthermore, we shift the detection perspective from automation to spam, and introduce the third problem, namely detecting social spam campaigns on Twitter. Evolved from individual spammers, spam campaigns manipulate and coordinate multiple accounts to spread spam on Twitter, and display some collective characteristics. We design an automatic classification system based on machine learning, and apply multiple features to classifying spam campaigns. Complementary to conventional spam detection methods, our work brings efficiency and robustness. Finally, we extend our detection research into the blogosphere to capture blog bots. In this problem, detecting the human presence is an effective defense against the automatic posting ability of blog bots. We introduce behavioral biometrics, mainly mouse and keyboard dynamics, to distinguish between human and bot. By passively monitoring user browsing activities, this detection method does not require any direct user participation, and improves the user experience

20 Web Browsing Behavior Analysis and Interactive Hypervideo

Processing data on any sort of user interaction is well known to be cumbersome and mostly time consuming. In order to assist researchers in easily inspecting fine-grained browsing data, current tools usually display user interactions as mouse cursor tracks, a video-like visualization scheme. However, to date, traditional online video inspection has not explored the full capabilities of hypermedia and interactive techniques. In response to this need, we have developed SMT2ǫ, a Web-based tracking system for analyzing browsing behavior using feature-rich hypervideo visualizations. We compare our system to related work in academia and the industry, showing that ours features unprecedented visualization capabilities. We also show that SMT2ǫ efficiently captures browsing data and is perceived by users to be both helpful and usable. A series of prediction experiments illustrate that raw cursor data are accessible and can be easily handled, providing evidence that the data can be used to construct and verify research hypotheses. Considering its limitations, it is our hope that SMT2ǫ will assist researchers, usability practitioners, and other professionals interested in understanding how users browse the Web

Are cookie banners indeed compliant with the law? Deciphering EU legal requirements on consent and technical means to verify compliance of cookie banners

In this work, we analyze the legal requirements on how cookie banners are supposed to be implemented to be fully compliant with the e-Privacy Directive and the General Data Protection Regulation. Our contribution resides in the definition of seventeen operational and fine-grained requirements on cookie banner design that are legally compliant, and moreover, we define whether and when the verification of compliance of each requirement is technically feasible. The definition of requirements emerges from a joint interdisciplinary analysis composed of lawyers and computer scientists in the domain of web tracking technologies. As such, while some requirements are provided by explicitly codified legal sources, others result from the domain-expertise of computer scientists. In our work, we match each requirement against existing cookie banners design of websites. For each requirement, we exemplify with compliant and non-compliant cookie banners. As an outcome of a technical assessment, we verify per requirement if technical (with computer science tools) or manual (with any human operator) verification is needed to assess compliance of consent and we also show which requirements are impossible to verify with certainty in the current architecture of the Web. For example, we explain how the requirement for revocable consent could be implemented in practice: when consent is revoked, the publisher should delete the consent cookie and communicate the withdrawal to all third parties who have previously received consent. With this approach we aim to support practically-minded parties (compliance officers, regulators, researchers, and computer scientists) to assess compliance and detect violations in cookie banner design and implementation, specially under the current revision of the European Union e-Privacy framework.Comment: 75 page

A Taxonomy of Web Personalization

Web personalization has become an important way to provide individualized user experiences. As a fragmented use of the term “Web personalization” and a lack of a common framework potentially hinder the establishment of a cumulative body of research, we develop a taxonomy of Web personalization. Bringing together research from information systems, computer science, and marketing, we develop a taxonomy focusing on the meta-characteristics user modeling (with the dimensions type of data, acquisition method, and life span of data) and system adaptation (with the dimensions object, volatility, scope, and control of adaptation). We demonstrate an application of our taxonomy by analyzing a sample of articles published in premier information systems journals and present some exemplary use cases to demonstrate how the taxonomy could be applied in practical contexts

Diverse Contributions to Implicit Human-Computer Interaction

Cuando las personas interactúan con los ordenadores, hay mucha información que no se proporciona a propósito. Mediante el estudio de estas interacciones implícitas es posible entender qué características de la interfaz de usuario son beneficiosas (o no), derivando así en implicaciones para el diseño de futuros sistemas interactivos. La principal ventaja de aprovechar datos implícitos del usuario en aplicaciones informáticas es que cualquier interacción con el sistema puede contribuir a mejorar su utilidad. Además, dichos datos eliminan el coste de tener que interrumpir al usuario para que envíe información explícitamente sobre un tema que en principio no tiene por qué guardar relación con la intención de utilizar el sistema. Por el contrario, en ocasiones las interacciones implícitas no proporcionan datos claros y concretos. Por ello, hay que prestar especial atención a la manera de gestionar esta fuente de información. El propósito de esta investigación es doble: 1) aplicar una nueva visión tanto al diseño como al desarrollo de aplicaciones que puedan reaccionar consecuentemente a las interacciones implícitas del usuario, y 2) proporcionar una serie de metodologías para la evaluación de dichos sistemas interactivos. Cinco escenarios sirven para ilustrar la viabilidad y la adecuación del marco de trabajo de la tesis. Resultados empíricos con usuarios reales demuestran que aprovechar la interacción implícita es un medio tanto adecuado como conveniente para mejorar de múltiples maneras los sistemas interactivos.Leiva Torres, LA. (2012). Diverse Contributions to Implicit Human-Computer Interaction [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17803Palanci