Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from EDBT 2026 / reviewers in the wild / expert

Mshabab Alrizah

dblp:249/3044 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
0since 2021 · last 2019
0009-0008-0843-7748ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Empirical software engineering · 100%
Network and information security
1 paper
Web and mobile security · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Web and mobile security › content blocking
ad blocking		 0.412019
Errors, Misunderstandings, and Attacks: Analyzing the Crowdsourcing Process of Ad-blocking Systems · Internet Measurement Conference 2019
Empirical software engineering
crowdsourcing		 0.412019
Errors, Misunderstandings, and Attacks: Analyzing the Crowdsourcing Process of Ad-blocking Systems · Internet Measurement Conference 2019
Empirical software engineering
issue report analysis		 0.412019
Errors, Misunderstandings, and Attacks: Analyzing the Crowdsourcing Process of Ad-blocking Systems · Internet Measurement Conference 2019

Methods — techniques the papers use, named apart from their topics

longitudinal dataset analysis · 0.8
YearPublicationVenuePosition
2019 Errors, Misunderstandings, and Attacks: Analyzing the Crowdsourcing Process of Ad-blocking Systems
abstract
Ad-blocking systems such as Adblock Plus rely on crowdsourcing to build and maintain filter lists, which are the basis for determining which ads to block on web pages. In this work, we seek to advance our understanding of the ad-blocking community as well as the errors and pitfalls of the crowdsourcing process. To do so, we collected and analyzed a longitudinal dataset that covered the dynamic changes of popular filter-list EasyList for nine years and the error reports submitted by the crowd in the same period.
Mshabab Alrizah, Sencun Zhu, Xinyu Xing 0001, Gang Wang 0011
 Internet Measurement Conference1