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Magdalena Wilczynska

dblp:351/3587 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Natural language and speech › Information extraction and text analysis › misinformation detection
disinformation detection
0.812024
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
0.212024
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
YearPublicationVenuePosition
2024 MIPD: Exploring Manipulation and Intention In a Novel Corpus of Polish Disinformation
abstract
This 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
EMNLP4