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Challenges and frontiers in abusive content detection - ACL Anthology

BibTeX. Markdown MODS XML Endnote More options… PDF: https://aclanthology.org/W19-3509.pdf; Code: bvidgen/Challenges-and-frontiers-in-abusive-content-detection ...

bvidgen/Challenges-and-frontiers-in-abusive-content-detection

... for the paper, 'Challenges and frontiers in abusive content detection' (Vidgen et al. 2019) - bvidgen/Challenges-and-frontiers-in-abusive-content-detection.

Challenges and frontiers in abusive content detection

Challenges and frontiers in abusive content detection. Bertie Vidgen ... 1 https://github.com/bvidgen/Challenges-and-frontiers- in-abusive-content-detection.

Challenges and frontiers in abusive content detection

Challenges and frontiers in abusive content detection. WS 2019 · Bertie ... Add Remove Mark official. bvidgen/Challenges-and-frontiers-in… official. 2.

Challenges and frontiers in abusive content detection ... - GitHub

... frontiers-in-abusive-content-detection/Challenges and frontiers in abusive content detection ... bvidgen/Challenges-and-frontiers-in-abusive-content-detection.

Challenges and frontiers in abusive content detection - ResearchGate

Request PDF | On Jan 1, 2019, Bertie Vidgen and others published Challenges and frontiers in abusive content detection | Find, read and cite all the ...

Abuse Detection | Papers With Code

Challenges and frontiers in abusive content detection. bvidgen/Challenges-and-frontiers-in-abusive-content-detection • WS 2019. Online abusive content ...

Challenges and frontiers in abusive content detection - ORA

Vidgen, B, A Harris, D Nguyen, R Tromble, S Hale, and H Margetts. 2019. “Challenges and Frontiers in Abusive Content Detection.

Reporting examples - Workshop on Online Abuse and Harms

... online appendix, available at: https://github.com/bvidgen/Challenges-and-frontiers-in-abusive-content-detection. Hosted on GitHub Pages — Theme by orderedlist.

Dynamically Generated Datasets to Improve Online Hate Detection

They also have better performance on HateCheck, a suite of functional tests for online hate detection. ... bvidgen/Dynamically-Generated-Hate-Speech-Dataset + ...

Directions in abusive language training data, a systematic review

* E-mail: [email protected]. Affiliation: The Alan Turing Institute ... Challenges and frontiers in abusive content detection. In ...

Directions in abusive language training data, a systematic review

4 [email protected]. Abstract. Data-driven and machine ... review challenges and frontiers in abusive content research, and describe sev-.

bvidgen/RPackage source: R/cor_sample.R - rdrr.io

In bvidgen/RPackage: Big Data Analytics functions. #' @title cor_sample ... The page or its content looks wrong. I can't find what I'm looking for. I have ...

Bertram Vidgen - OII

He is actively interested in the use of computational methods in social science, the ethical challenges ... Contact. [email protected] · @bertramvidgen.

Hate speech: measures and counter-measures | The Alan Turing ...

Blog post: How much online abuse is there? August 2019. 'Challenges and frontiers in abusive content detection' presented by Bertie Vidgen at ALW3: 3rd Workshop ...

bvidgen/RPackage source: R/cor_pop.R - rdrr.io

In bvidgen/RPackage: Big Data Analytics functions ... bvidgen/RPackage documentation built on May 13, 2019, 9:04 a.m. ... The page or its content looks wrong. I can ...

Detecting weak and strong Islamophobic hate speech on social media

Detecting weak and strong Islamophobic hate speech on social media. Bertie [email protected] ... content. Accuracy is 77.6% ...

Directions in abusive language training data, a systematic review ...

Vidgen, Bertie1 (AUTHOR) [email protected] ... Making effective detection systems for abusive content ... abusive content training datasets. [ABSTRACT ...

The 6th Workshop on Online Abuse and Harms | ACL Member Portal

[email protected] · [email protected] ... challenges in developing technological systems for the detection of abuse and other harms.

Hate Speech Detection with Transformers

Unusually for an abusive content dataset, it comprises 54% hateful and 46% not hateful entries. There is no published state-of-the-art model ...