Automatic Vandalism Detection in Wikipedia

By Martin Potthast, Benno Stein, R. Gerling. 2008

204 citations recorded by the source corpus.

Subfields: Communication, Information Systems, Molecular Biology, 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 Communication

1. Patrick Lewis
2. Edouard Grave
3. Fabio Petroni
4. Sebastian Riedel

In Information Systems

1. Gautier Izacard
2. Patrick Lewis
3. Zhengbao Jiang
4. Edouard Grave
5. Timo Schick
6. Fabio Petroni
7. Sebastian Riedel

In Molecular Biology

1. Patrick Lewis
2. Edouard Grave
3. Sebastian Riedel

In Natural Language Processing

1. Sebastian Riedel
2. Patrick Lewis
3. Fabio Petroni
4. Timo Schick
5. Gautier Izacard
6. Edouard Grave
7. Hannaneh Hajishirzi
8. Zhengbao Jiang

In Safety, Robustness & Interpretability

1. Zhengbao Jiang
2. Edouard Grave
3. Timo Schick
4. Fabio Petroni
5. Sebastian Riedel

Citations in the corpus

• PEER: A Collaborative Language Model (2022, methodology): “Filtering As Wikipedia’s edit history contains several low quality edits and numerous instances of vandalism (Potthast et al., 2008), we use some simple heuristics to improve data quality.”
• Antisocial Behavior in Online Discussion Communities (2015, unknown): “Several papers have focused on detecting vandalism on Wikipedia by using features such as user language and reputation, as well as article meta-data (Adler et al. 2011; Potthast, Stein, and Gerling 2008).”