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Maxime Garambois

dblp:440/7458 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2026
0009-0000-0594-9803ORCID · reported

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

Databases, data management, data science and information retrieval · 1 · 1 first-author · 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.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational social science and digital humanities · 100%
Artificial intelligence
1 paper
Information extraction and text analysis · 100%
Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%

Topics — the 1 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational social science and digital humanities › cultural heritage
cultural heritage analysis
1.012026
A Dataset of Cultural Heritage Manipulation on English Wikipedia in the Russo-Ukrainian Context · SIGIR 2026

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

dataset construction · 3.0annotation · 3.0
YearPublicationVenuePosition
2026 A Dataset of Cultural Heritage Manipulation on English Wikipedia in the Russo-Ukrainian Context
abstract
Digital knowledge platforms increasingly shape how cultural heritage and historical responsibility are represented and remembered, yet they are vulnerable to subtle forms of narrative manipulation that rarely appear as explicit misinformation. We present a new dataset of English Wikipedia revision pairs designed to support the systematic study of cultural heritage manipulation in the context of the Russo--Ukrainian conflict. The resource captures fine-grained textual changes through which cultural attribution, agency, and legitimacy are incrementally reframed within a collaborative encyclopedic infrastructure. By providing paired revisions, editorial context, and consistent annotations, the dataset enables analysis of discursive interventions that remain procedurally legitimate while producing cumulative cultural effects. The dataset is released to support research in information retrieval, natural language processing, and computational social science on detecting and understanding subtle narrative change in large-scale, collaborative knowledge systems. The resource is available at https://zenodo.org/records/19883068.
Maxime Garambois, Hamest Tamrazyan, Emanuela Boros
SIGIR1