Truth Inference in Crowdsourcing: Is the Problem Solved?

By Yudian Zheng, Guoliang Li, Yuanbing Li, Caihua Shan, Reynold Cheng. Proceedings of the VLDB Endowment, 2017

478 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/b1661ee77758b396a3f3ef41e1b8420dd496b0ab

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Gianluca Demartini
2. Shazia Wasim Sadiq

In Computer Science Applications

1. Gianluca Demartini

In Management Science and Operations Research

1. Solmaz Abdi
2. Hassan Khosravi
3. Gianluca Demartini
4. Shazia Wasim Sadiq

Citations in the corpus

• Evaluating the Quality of Learning Resources: A Learnersourcing Approach (2021, background): “While designing mechanisms for accurate integration of crowdsourced data through machine learning algorithms has been studied extensively within the crowdsourcing community [28], the use of these approaches for aggregating learnersourced decisions has received little attention.”
• DynaSent: A Dynamic Benchmark for Sentiment Analysis (2020, unknown): “Much subsequent work has pursued similar strategies; for a full review, see Zheng et al. 2017.”
• Toward Annotator Group Bias in Crowdsourcing (2021, unknown): “In recent years, a lot of efforts from the machine learning community have been conducted to mitigate the effect of these noisy crowdsourcing labels (Zheng et al., 2017).”