# A Study of Slang Representation Methods

arXiv: https://arxiv.org/abs/2212.05613 · HTML: https://mlsanity.com/paper/2212.05613/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 32 references that are in the map.
- First posted on arXiv 11 Dec 2022.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [HateBERT: Retraining BERT for Abusive Language Detection in English](https://mlsanity.com/paper/2010.12472/) (2020)
- [SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)](https://mlsanity.com/paper/2006.07235/) (2020)
- [Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang](https://mlsanity.com/paper/2203.04651/) (2022)
- [A large-scale crowdsourced analysis of abuse against women journalists and politicians on Twitter](https://mlsanity.com/paper/1902.03093/) (2019)
- [Are Anti-Feminist Communities Gateways to the Far Right? Evidence from Reddit and YouTube](https://mlsanity.com/paper/2102.12837/) (2021)
- [Neutral bots probe political bias on social media](https://mlsanity.com/paper/2005.08141/) (2020)
- [Emo, Love, and God: Making Sense of Urban Dictionary, a Crowd-Sourced Online Dictionary](https://mlsanity.com/paper/1712.08647/) (2017)
- [SlangSD: Building and Using a Sentiment Dictionary of Slang Words for Short-Text Sentiment Classification](https://mlsanity.com/paper/1608.05129/) (2016)
- [Reddit and the Fourth Estate: Exploring the magnitude and effects of media influence on community level moderation on Reddit](https://mlsanity.com/paper/2111.00084/) (2021)

## Built on by (most cited first, 1 of 1)

- [Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLM](https://mlsanity.com/paper/2508.05775/) (2025)

## Abstract (arXiv)

Considering the large amount of content created online by the minute, slang-aware automatic tools are critically needed to promote social good, and assist policymakers and moderators in restricting the spread of offensive language, abuse, and hate speech. Despite the success of large language models and the spontaneous emergence of slang dictionaries, it is unclear how far their combination goes in terms of slang understanding for downstream social good tasks. In this paper, we provide a framework to study different combinations of representation learning models and knowledge resources for a variety of downstream tasks that rely on slang understanding. Our experiments show the superiority of models that have been pre-trained on social media data, while the impact of dictionaries is positive only for static word embeddings. Our error analysis identifies core challenges for slang representation learning, including out-of-vocabulary words, polysemy, variance, and annotation disagreements, which can be traced to characteristics of slang as a quickly evolving and highly subjective language.