Moderation Techniques for Social Media Content

By A. Veglis. 2014

40 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Communication, Information Systems, Natural Language Processing, Safety, Robustness & Interpretability

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. Vinodkumar Prabhakaran
2. Ben Hutchinson
3. Emily L. Denton
4. Thilo Hagendorff
5. Kellie Webster
6. Luke Munn
7. Ming-Hsuan Yang
8. Adina Williams

In Communication

1. Ben Hutchinson
2. Thilo Hagendorff
3. Kellie Webster
4. Vinodkumar Prabhakaran
5. Emily L. Denton

In Information Systems

1. Ben Hutchinson
2. Thilo Hagendorff
3. Kellie Webster
4. Vinodkumar Prabhakaran
5. Emily L. Denton

In Natural Language Processing

1. Ben Hutchinson
2. Vinodkumar Prabhakaran
3. Emily L. Denton
4. Thilo Hagendorff
5. Kellie Webster
6. Adina Williams

In Safety, Robustness & Interpretability

1. Vinodkumar Prabhakaran
2. Emily L. Denton
3. Ben Hutchinson
4. Thilo Hagendorff
5. Kellie Webster
6. Paula Czarnowska
7. Yogarshi Vyas
8. Kashif Shah

Citations in the corpus

• Social Biases in NLP Models as Barriers for Persons with Disabilities (2020, background): “In a deployment context that involves human moderation, model scores may sometimes be used to select and prioritize messages for review by moderators (Veglis, 2014; Chandrasekharan et al., 2019).”
• The Ethics of AI Ethics: An Evaluation of Guidelines (2019, background): “Issues such as lithium mining, e-waste, the one-way use of rare earth minerals, energy consumption, low-wage “clickworkers” creating labels for data sets or doing content moderation are of relevance here (Crawford and Joler 2018; Irani 2016; Veglis 2014; Fang 2019; Casilli 2017).”