Fact-checking is one of the effective solutions in fighting online misinformation.
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.
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.
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 is an ever increasing problem that is difficult to solve for the research community and has a negative impact on the society at large.
The KG-BIAS 2020 workshop touches on biases and how they surface in knowledge graphs (KGs), biases in the source data that is used to create KGs, methods for measuring or remediating bias in KGs, but also identifying other biases such as how and which languages are represented in automatically constructed KGs or how personal KGs might incur inherent biases.
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.
no code implementations • 26 Oct 2017 • Alessandro Checco, Gianluca Demartini, Alexander Loeser, Ines Arous, Mourad Khayati, Matthias Dantone, Richard Koopmanschap, Svetlin Stalinov, Martin Kersten, Ying Zhang
A core business in the fashion industry is the understanding and prediction of customer needs and trends.