--- title: "One-step and Two-step Classification for Abusive — Park 2017" description: "Park and Fung address automatic detection of sexist and racist tweets." authors: ["Ji Ho Park", "Pascale Fung"] year: 2017 venue: "Proceedings of the First Workshop on Abusive Language Online" source: "https://aclanthology.org/W17-3006/" doi: "10.18653/v1/w17-3006" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/park-2017-one-step-two-step-classification/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/park-2017-one-step-two-step-classification/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2017](https://md.research.elamur.ai/free/en/papers/2017/page/3/) / One-step and Two-step Classification for Abusive Language Detection on Twitter # One-step and Two-step Classification for Abusive Language Detection on Twitter **Authors:** [Ji Ho Park](https://md.research.elamur.ai/free/en/authors/ji-ho-park/), [Pascale Fung](https://md.research.elamur.ai/free/en/authors/pascale-fung/) · **Year:** 2017 · **Venue:** Proceedings of the First Workshop on Abusive Language Online · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/W17-3006/) · [PDF](https://aclanthology.org/W17-3006.pdf) · [DOI 10.18653/v1/w17-3006](https://doi.org/10.18653/v1/w17-3006) · [OpenAlex](https://openalex.org/W2622349515) **Citations:** 353 citations (openalex-snapshot · 2026-10-14) · [80](#cited-by) in our library, of the 140884 papers whose bibliographies we read ## Summary Park and Fung address automatic detection of sexist and racist tweets. They compare two strategies. The one-step strategy is a single three-way classifier (none / sexism / racism). The two-step strategy first flags a tweet as abusive or not, then assigns an abusive tweet to sexism or racism. They merged the two expert-annotated English Twitter corpora by Waseem and Hovy (2016) and Waseem (2016), released for ALW1, into roughly 18k labelled tweets. They tested three CNN variants: CharCNN on characters, WordCNN on static word2vec embeddings, and a new HybridCNN that reads both. Baselines were character n-gram logistic regression, SVM and FastText. The best one-step system was HybridCNN, with a weighted F1 of 0.827. Chaining two logistic regression classifiers reached a nearly equal 0.824. The second step (sexism vs. racism) proved easy for every model, at about 0.95 F1. This suggests the hard part is spotting abuse in the first place. The two-step design therefore lets cheap models compete with neural ones and makes it possible to mix classifier types. ## Key points - **Data:** the merged corpus has 12,427 "none", 2,059 racism and 3,864 sexism tweets. For the first step, racism and sexism were pooled into 5,923 "abusive" tweets. - **HybridCNN design:** a character channel with filters of width 3, 4 and 5 and a word channel with widths 1, 2 and 3. Each uses 50 feature maps, and the outputs are 1-max-pooled, concatenated and fed to softmax. - **WordCNN:** follows Kim's CNN-static setup with frozen 300-d Google News word2vec. Hashtags such as #womenagainstfeminism are split into words using the wordsegment library. - **CharCNN:** the deep Zhang et al. (2015) architecture was cut down to 2 convolution layers because the dataset is small. It scored below WordCNN. - **Evaluation:** 10-fold cross-validation with label-weighted precision, recall and F1. On the binary abusive-detection step, HybridCNN led with F1 0.734, ahead of WordCNN (0.726) and LR (0.711). ## Interesting ideas - **Split coarse detection from fine-grained typing.** If the subtype distinction is easy, all the difficulty sits in the binary step. A large dataset labelled only abusive/not could train step one, and a small finely labelled set could train step two. This fits low-resource settings, such as Ukrainian moderation data. - **Mix models per stage.** Each step can use whichever classifier works best for it. For example, HybridCNN followed by LR beat HybridCNN used for both steps. Any cascaded pipeline can choose components stage by stage instead of using one model throughout. - **Combine character and word channels.** Running both in parallel within one CNN helps with misspellings and invented words like #feminazi. This is relevant for noisy social-media text and morphologically rich languages. - **Segment hashtags before embedding lookup.** Splitting hashtags into ordinary words is a cheap way to recover out-of-vocabulary tokens when using pretrained embeddings. - **Use class-balanced mini-batches.** Sampling each batch with equal label proportions is a simple alternative to loss reweighting for skewed datasets. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/) ## Cited by Cited by 80 papers of our library cite it (of the 140884 whose bibliographies we read); 6 of them have a page here, 74 not yet. - [I Feel Offended, Don't Be Abusive! Implicit/Explicit Messages in Offensive and Abusive Language](https://md.research.elamur.ai/free/en/papers/caselli-2020-i-feel-offended-don-t/) - [RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models](https://md.research.elamur.ai/free/en/papers/gehman-2020-realtoxicityprompts-evaluating-neural-toxic-degeneration/) - [Sexism detection: The first corpus in Algerian dialect with a code-switching in Arabic/ French and English](https://md.research.elamur.ai/free/en/papers/guellil-2021-sexism-detection-first-corpus-algerian/) - [Reducing Gender Bias in Abusive Language Detection](https://md.research.elamur.ai/free/en/papers/park-2018-reducing-gender-bias-abusive-language/) - [Toxicity Detection: Does Context Really Matter?](https://md.research.elamur.ai/free/en/papers/pavlopoulos-2020-toxicity-detection-does-context-really/) - [The Risk of Racial Bias in Hate Speech Detection](https://md.research.elamur.ai/free/en/papers/sap-2019-risk-racial-bias-hate-speech/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{park2017one, title = {{One-step and Two-step Classification for Abusive Language Detection on Twitter}}, author = {Park, Ji Ho and Fung, Pascale}, booktitle = {Proceedings of the First Workshop on Abusive Language Online}, year = {2017}, doi = {10.18653/v1/w17-3006}, url = {https://doi.org/10.18653/v1/w17-3006} } ``` - [← previous On the Need of Cross Validation for Discourse Relation Classification](https://md.research.elamur.ai/free/en/papers/shi-2017-need-cross-validation-discourse-relation/) - [next → Ontology-Aware Token Embeddings for Prepositional Phrase Attachment](https://md.research.elamur.ai/free/en/papers/onto-2017-dasigi-ontology-aware-token-embeddings-prepositional/) - [↑ back to papers of 2017](https://md.research.elamur.ai/free/en/papers/2017/page/3/) ## References 14 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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