Search Results for author: Stefano Mizzaro

Found 6 papers, 5 papers with code

Managing Bias in Human-Annotated Data: Moving Beyond Bias Removal

no code implementations26 Oct 2021 Gianluca Demartini, Kevin Roitero, Stefano Mizzaro

Due to the widespread use of data-powered systems in our everyday lives, the notions of bias and fairness gained significant attention among researchers and practitioners, in both industry and academia.

Fairness Management

The Many Dimensions of Truthfulness: Crowdsourcing Misinformation Assessments on a Multidimensional Scale

1 code implementation3 Aug 2021 Michael Soprano, Kevin Roitero, David La Barbera, Davide Ceolin, Damiano Spina, Stefano Mizzaro, Gianluca Demartini

We deploy a set of quality control mechanisms to ensure that the thousands of assessments collected on 180 publicly available fact-checked statements distributed over two datasets are of adequate quality, including a custom search engine used by the crowd workers to find web pages supporting their truthfulness assessments.

Informativeness Misinformation

Can the Crowd Judge Truthfulness? A Longitudinal Study on Recent Misinformation about COVID-19

1 code implementation25 Jul 2021 Kevin Roitero, Michael Soprano, Beatrice Portelli, Massimiliano De Luise, Damiano Spina, Vincenzo Della Mea, Giuseppe Serra, Stefano Mizzaro, Gianluca Demartini

Our results show that: workers are able to detect and objectively categorize online (mis)information related to COVID-19; both crowdsourced and expert judgments can be transformed and aggregated to improve quality; worker background and other signals (e. g., source of information, behavior) impact the quality of the data.

Misinformation

The COVID-19 Infodemic: Can the Crowd Judge Recent Misinformation Objectively?

1 code implementation13 Aug 2020 Kevin Roitero, Michael Soprano, Beatrice Portelli, Damiano Spina, Vincenzo Della Mea, Giuseppe Serra, Stefano Mizzaro, Gianluca Demartini

Misinformation is an ever increasing problem that is difficult to solve for the research community and has a negative impact on the society at large.

Misinformation

An Effectiveness Metric for Ordinal Classification: Formal Properties and Experimental Results

1 code implementation ACL 2020 Enrique Amigó, Julio Gonzalo, Stefano Mizzaro, Jorge Carrillo-de-Albornoz

In Ordinal Classification tasks, items have to be assigned to classes that have a relative ordering, such as positive, neutral, negative in sentiment analysis.

Classification General Classification +1

Can The Crowd Identify Misinformation Objectively? The Effects of Judgment Scale and Assessor's Background

1 code implementation14 May 2020 Kevin Roitero, Michael Soprano, Shaoyang Fan, Damiano Spina, Stefano Mizzaro, Gianluca Demartini

Truthfulness judgments are a fundamental step in the process of fighting misinformation, as they are crucial to train and evaluate classifiers that automatically distinguish true and false statements.

Misinformation

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