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Demo Data from EDBT 2026 / reviewers in the wild / expert

Martin Banzer

dblp:434/6318 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none

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

Security and privacy · 1 · 1 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.

Artificial intelligence
1 paper
Trustworthy machine learning · 50% Language models and text generation · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Trustworthy machine learning
content moderation		 1.012026
There is No War in Ba Sing Se: A Global Analysis of Content Moderation in Large Language Models · NDSS 2026
Natural language and speech › Language models and text generation
large language model safety		 1.012026
There is No War in Ba Sing Se: A Global Analysis of Content Moderation in Large Language Models · NDSS 2026
YearPublicationVenuePosition
2026 There is No War in Ba Sing Se: A Global Analysis of Content Moderation in Large Language Models
Friedemann Lipphardt, Moonis Ali, Martin Banzer, Anja Feldmann, Devashish Gosain
 NDSS3