Demo Data from EDBT 2026 / reviewers in the wild / expert
Magdalena Wilczynska
dblp:351/3587
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 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 |
Information extraction and text analysis · 87% Question answering and dialogue systems · 13% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% |
Topics — the 2 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Information extraction and text analysis › misinformation detection
disinformation detection |
0.8 | 1 | 2024 | MIPD: Exploring Manipulation and Intention In a Novel Corpus of Polish Disinformation · EMNLP 2024 |
Natural language and speech › Question answering and dialogue systems
intention detection |
0.2 | 1 | 2024 | MIPD: Exploring Manipulation and Intention In a Novel Corpus of Polish Disinformation · EMNLP 2024 |
Methods — techniques the papers use, named apart from their topics
multi-label classification · 1.5annotation · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | MIPD: Exploring Manipulation and Intention In a Novel Corpus of Polish DisinformationabstractThis study presents a novel corpus of 15,356 Polish web articles, including articles identified as containing disinformation.Our dataset enables a multifaceted understanding of disinformation.We present a distinctive multilayered methodology for annotating disinformation in texts.What sets our corpus apart is its focus on uncovering hidden intent and manipulation in disinformative content.A team of experts annotated each article with multiple labels indicating both disinformation creators' intents and the manipulation techniques employed.Additionally, we set new baselines for binary disinformation detection and two multiclass multilabel classification tasks: manipulation techniques and intention types classification. Arkadiusz Modzelewski, Giovanni Da San Martino, Pavel Savov, Magdalena Wilczynska, Adam Wierzbicki |
EMNLP | 4 |