Demo Data from EDBT 2026 / reviewers in the wild / expert
Nida Tanveer
dblp:374/9808
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2026
0009-0007-7079-4634ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 2 · 2 since 2021Applied, interdisciplinary, general and emerging computing · 2 · 2 since 2021
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.
| Network and information security
2 papers |
Web and mobile security · 51% Systems and software security · 40% Privacy and data protection · 9% | |
| Databases, data mining, and information retrieval
1 paper |
Web and social media mining · 100% | |
| Computer networks
1 paper |
Network measurement and analytics · 100% |
Topics — the 3 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Systems and software security
content moderation |
1.0 | 1 | 2026 | Safeguarding Children at Scale: Cost-Effective Multimodal LLM Detection of Inappropriate YouTube Advertising · WWW 2026 |
Web and mobile security › harmful content detection
multimodal harmful content detection |
1.0 | 1 | 2026 | Safeguarding Children at Scale: Cost-Effective Multimodal LLM Detection of Inappropriate YouTube Advertising · WWW 2026 |
Web and mobile security › web security
online advertising security |
0.3 | 1 | 2026 | Safeguarding Children at Scale: Cost-Effective Multimodal LLM Detection of Inappropriate YouTube Advertising · WWW 2026 |
Methods — techniques the papers use, named apart from their topics
cross-regional measurement · 2.3multimodal large language model · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Safeguarding Children at Scale: Cost-Effective Multimodal LLM Detection of Inappropriate YouTube Advertising
Eman Nabeel, Haleema Jamil, Shizza Asher, Zaeem Mohtashim Khan, Nida Tanveer, Ihsan Ayyub Qazi, Zafar Ayyub Qazi |
WWW | 5 |
| 2024 | Analyzing Ad Exposure and Content in Child-Oriented Videos on YouTubeabstractAs a popular choice for video and entertainment streaming, YouTube hosts a large audience, including children, who form a growing proportion of its users. Despite separate "made for kids" labelling and stricter moderation of these videos, inappropriate advertising remains a concern as it threatens the safety of YouTube for young viewers. This paper is the first comparative measurement study that explores how advertisement exposure and content vary across child-oriented videos on YouTube. We do this by conducting a cross-regional advertisement analysis on highly viewed "made for kids" labelled content across a total of ten countries with varying regulation. A second front of comparison is carried out between ad patterns on unlabelled and labelled child-oriented videos. Our analysis reveals that the safety of a child's YouTube experience is shaped significantly by their external environment. There also appears to be lax enforcement of YouTube ad and child protection policies, indicated by the presence of unlabelled child-oriented content with weak ad regulation. We discuss the implications of inappropriate exposure on children and suggest policy and implementation measures to mitigate this threat. Emaan Bilal Khan, Nida Tanveer, Aima Shahid, Mohammad Jaffer Iqbal, Haashim Ali Mirza, Armish Javed, Ihsan Ayyub Qazi, Zafar Ayyub Qazi |
WWW | 2 |