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Gianluca Demartini
Researcher at University of Queensland
Publications - 188
Citations - 3883
Gianluca Demartini is an academic researcher from University of Queensland. The author has contributed to research in topics: Crowdsourcing & Computer science. The author has an hindex of 27, co-authored 156 publications receiving 3169 citations. Previous affiliations of Gianluca Demartini include University of California, Berkeley & Leibniz University of Hanover.
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Journal ArticleDOI
CrowdCO-OP: Sharing Risks and Rewards in Crowdsourcing
TL;DR: A novel crowdsourcing reward mechanism that allows workers to share these risks and achieve a standardized hourly wage equal for all participating workers is proposed and can thereby take up challenging and complex HITs without bearing the financial risk of not being rewarded for completed work.
Proceedings ArticleDOI
Can The Crowd Identify Misinformation Objectively? The Effects of Judgment Scale and Assessor's Background
TL;DR: In this paper, the authors present the results of an extensive study based on crowdsourcing: they collect thousands of truthfulness assessments over two datasets, and compare expert judgments with crowd judgments, expressed on scales with various granularity levels.
Proceedings ArticleDOI
Entity summarization of news articles
TL;DR: A novel entity-labeled corpus with temporal information out of the TREC 2004 Novelty collection is constructed, and it is shown that an article's history can be exploited to improve its summarization.
Proceedings ArticleDOI
The COVID-19 Infodemic: Can the Crowd Judge Recent Misinformation Objectively?
Kevin Roitero,Michael Soprano,Beatrice Portelli,Damiano Spina,Vincenzo Della Mea,Giuseppe Serra,Stefano Mizzaro,Gianluca Demartini +7 more
TL;DR: In this paper, the authors used a crowdsourcing-based approach to assess the truthfulness of a statement, instead of relying on a few experts, a crowd of (non-expert) judges is exploited.
Journal ArticleDOI
The Evolution of Power and Standard Wikidata Editors: Comparing Editing Behavior over Time to Predict Lifespan and Volume of Edits
Cristina Sarasua,Alessandro Checco,Gianluca Demartini,Djellel Eddine Difallah,Michael Feldman,Lydia Pintscher +5 more
TL;DR: This paper investigates the evolution that editors with different levels of engagement exhibit in their editing behaviour over time, and defines and implements prediction models that use the multiple evolution indicators.