Automatic Identification and Classification of Misogynistic Language on Twitter

By Maria Anzovino, Elisabetta Fersini, Paolo Rosso. 2018

218 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing, AI Ethics, Fairness & Society

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Citations in the corpus

Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection (2021, methodology): “This category is used in datasets by Hammer (2014), Golbeck et al. (2017) and Anzovino et al. (2018).”
Challenges and frontiers in abusive content detection (2019, background): “For instance, Anzovino et al. develop a system that not only detects misogyny but also whether it consists of stereotypes, discrediting, objectification, harassment, dominance, derailing or threats of violence (Anzovino, Fersini, & Rosso, 2018).”
Unintended Bias in Misogyny Detection (2019, background): “A first contribution to the problem of automatic misogyny identification has been presented in [2], where the role of different”
A Unified Taxonomy of Harmful Content (2020, background): “Anzovino et al. (2018) studied misog-ynistic social media posts, and modeled seven types of abuse, most of which extend beyond abuse directed at women.”
A Survey on Gender Bias in Natural Language Processing (2021, background): “Studies on detecting misogynist or toxic language on social media released Twitter-based datasets [Anzovino et al. 2018; Hewitt et al. 2016].”
Misogyny Detection in Twitter: a Multilingual and Cross-Domain Study (2020, unknown): “The Automatic Misogyny Identification (AMI) campaign started by Anzovino, Fersini, and Rosso (2018) proposed a first benchmark dataset, capturing misogyny phenomena in Twitter.”
Toxic, Hateful, Offensive or Abusive? What Are We Really Classifying? An Empirical Analysis of Hate Speech Datasets (2020, background): “Thus, in (Anzovino et al., 2018) misogyny is defined as “specific case of hate speech whose targets are women”, which is very similar to the definition of sexist hate speech.”
Introducing CAD: the Contextual Abuse Dataset (2021, background): “Anzovino et al. (2018) label content for six subtypes of misogyny: discrediting, using stereotypes, objectifying, sexually harassing, threatening violence, dominating or derailing.”