The massive spread of false information on social media has become a global risk, implicitly
influencing public opinion and threatening social/political development. False information …

Quality improvement methods are essential to gathering high-quality crowdsourced data,
both for research and industry applications. A popular and broadly applicable method is task …

Misinformation such as fake news is one of the big challenges of our society. Research on
automated fact-checking has proposed methods based on supervised learning, but these …

Crowdsourcing has emerged as a novel problem-solving paradigm, which facilitates
addressing problems that are hard for computers, eg, entity resolution and sentiment …

To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant
biomedical knowledge in scientific literature to understand the disease mechanism and …

Thanks to information explosion, data for the objects of interest can be collected from
increasingly more sources. However, for the same object, there usually exist conflicts among …

In this paper we explore the scenario of learning to count multiple instances of objects from
images that have been dot-annotated through crowdsourcing. Specifically, we work with a …

The web is a huge source of valuable information. However, in recent times, there is an
increasing trend towards false claims in social media, other web-sources, and even in news …

Crowdsourcing provides an efficient way to obtain multiple noisy labels from different crowd
workers for each unlabeled instance. Label integration methods are designed to infer the …

This survey provides a comprehensive overview of the landscape of crowdsourcing
research, targeted at the machine learning community. We begin with an overview of the …