[Paper Review] DOBBS: Towards a Comprehensive Dataset to Study the Browsing Behavior of Online Users

Christian von der Weth, Manfred Hauswirth|arXiv (Cornell University)|Jul 5, 2013

Spam and Phishing Detection3 citations

TL;DR

DOBBS proposes a privacy-preserving browser add-on that collects comprehensive, fine-grained browsing behavior data from users, including window/tab activity, session durations, and page-view times, enabling deeper analysis of modern browsing patterns such as passive consumption and parallel browsing. The key contribution is a publicly available, anonymized dataset offering insights beyond traditional server logs.

ABSTRACT

The investigation of the browsing behavior of users provides useful information to optimize web site design, web browser design, search engines offerings, and online advertisement. This has been a topic of active research since the Web started and a large body of work exists. However, new online services as well as advances in Web and mobile technologies clearly changed the meaning behind "browsing the Web" and require a fresh look at the problem and research, specifically in respect to whether the used models are still appropriate. Platforms such as YouTube, Netflix or last.fm have started to replace the traditional media channels (cinema, television, radio) and media distribution formats (CD, DVD, Blu-ray). Social networks (e.g., Facebook) and platforms for browser games attracted whole new, particularly less tech-savvy audiences. Furthermore, advances in mobile technologies and devices made browsing "on-the-move" the norm and changed the user behavior as in the mobile case browsing is often being influenced by the user's location and context in the physical world. Commonly used datasets, such as web server access logs or search engines transaction logs, are inherently not capable of capturing the browsing behavior of users in all these facets. DOBBS (DERI Online Behavior Study) is an effort to create such a dataset in a non-intrusive, completely anonymous and privacy-preserving way. To this end, DOBBS provides a browser add-on that users can install, which keeps track of their browsing behavior (e.g., how much time they spent on the Web, how long they stay on a website, how often they visit a website, how they use their browser, etc.). In this paper, we outline the motivation behind DOBBS, describe the add-on and captured data in detail, and present some first results to highlight the strengths of DOBBS.

Motivation & Objective

• To address the limitations of traditional data sources like server logs and search engine transaction logs, which fail to capture modern browsing behaviors such as passive media consumption and tabbed multitasking.
• To study evolving browsing behaviors driven by new technologies like streaming platforms (e.g., YouTube, Netflix), Ajax, WebSockets, and mobile browsing.
• To create a comprehensive, privacy-preserving dataset that captures user activity at a fine-grained level without compromising user anonymity.
• To enable new research on user behavior metrics beyond simple page visits, such as actual viewing time and session patterns across multiple windows and tabs.
• To encourage broad participation through open-source availability and transparent privacy practices, including anonymous feedback mechanisms.

Proposed method

• Deploying a Firefox-specific browser add-on that silently logs user browsing events in the background without affecting performance.
• Capturing three main event types: window events (open/close of windows and tabs), session events (start/end of browsing sessions and activity changes), and browsing events (page load and view durations).
• Using a randomly generated, non-retraceable user ID to ensure full anonymization and prevent re-identification.
• Encrypting all sensitive data locally before transmission or storage to preserve privacy.
• Storing data in a structured format that enables reconstruction of user browsing history as a graph, allowing analysis of navigation patterns.
• Releasing the dataset publicly via the DOBBS project website, with open-source code under the BSD license to encourage transparency and reuse.

Experimental results

Research questions

• RQ1 How do modern browsing behaviors—such as passive media consumption on platforms like YouTube or last.fm—differ from traditional active, link-based navigation?
• RQ2 To what extent do users engage in parallel browsing using multiple browser windows and tabs, and how do they manage these activities?
• RQ3 How can the distinction between page load time, display time, and actual user viewing time improve the measurement of web page popularity?
• RQ4 What are the patterns of user inactivity and focus state during browsing sessions, and how do they affect the interpretation of logged data?
• RQ5 Can user browsing histories be accurately reconstructed from logged events, and what insights does this enable for behavioral modeling?

Key findings

• Parallel browsing is common, with users frequently managing multiple browser windows and tabs simultaneously, though the strategies for doing so vary significantly across individuals.
• Passive browsing—where users leave a page open without interaction, such as listening to online radio while doing other tasks—occurs very frequently and is a dominant browsing pattern.
• The distinction between page load time, display time, and actual user viewing time enables the formulation of new, more accurate metrics for measuring web page popularity beyond simple visit frequency.
• The dataset allows for highly accurate reconstruction of user browsing histories, which can be visualized as browsing graphs to model complex navigation behaviors.
• The collected data reveal that traditional metrics like server access logs fail to capture critical aspects of modern browsing, such as background media consumption and session fragmentation across multiple tabs.
• Despite technical limitations and interpretive ambiguities (e.g., whether a non-focused window counts as viewed), the dataset provides a richer and more nuanced view of user behavior than existing alternatives.