Examining a hate speech corpus for hate speech detection and popularity prediction

By Filip Klubicka, R. Fern'andez. 2018

37 citations recorded by the source corpus.

As research on hate speech becomes more and more relevant every day, most of it is still focused on hate speech detection. By attempting to replicate a hate speech detection experiment performed on an existing Twitter corpus annotated for hate speech, we highlight some issues that arise from doing research in the field of hate speech, which is essentially still in its infancy. We take a critical look at the training corpus in order to understand its biases, while also using it to venture beyond hate speech detection and investigate whether it can be used to shed light on other facets of research, such as popularity of hate tweets.

Subfields: Artificial Intelligence, Communication, Natural Language Processing, Evaluation & Benchmarks, Safety, Robustness & Interpretability

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Citations in the corpus

The Risk of Racial Bias in Hate Speech Detection (2019, methodology): “However, because of severe limitations of that dataset (see Schmidt and Wiegand, 2017; Klubika and Fernandez, 2018), we relegate those analyses to supplementary (§A.”
Generating Counter Narratives against Online Hate Speech: Data and Strategies (2020, background): “Additionally, HS-CN datasets should not be ephemeral, which is a structural problem with crawled data since, due to copyright limitations, datasets are usually distributed as a list of tweet IDs (Klubiˇcka and Fern´andez, 2018).”
CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech (2019, methodology): “We want to provide a dataset that is not ephemeral, by releasing only copy-free textual data that can be directly exploited by re-searches without data loss across time, as originally pointed out in (Klubiˇcka and Fern´andez, 2018).”