# Twitter Topic Classification

arXiv: https://arxiv.org/abs/2209.09824 · HTML: https://mlsanity.com/paper/2209.09824/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 72 references that are in the map.
- First posted on arXiv 20 Sep 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)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [Mind the Gap: Assessing Temporal Generalization in Neural Language Models](https://mlsanity.com/paper/2102.01951/) (2021)
- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)
- [TimeLMs: Diachronic Language Models from Twitter](https://mlsanity.com/paper/2202.03829/) (2022)
- [Top2Vec: Distributed Representations of Topics](https://mlsanity.com/paper/2008.09470/) (2020)
- [TweetNLP: Cutting-Edge Natural Language Processing for Social Media](https://mlsanity.com/paper/2206.14774/) (2022)
- [A large-scale COVID-19 Twitter chatter dataset for open scientific research -- an international collaboration](https://mlsanity.com/paper/2004.03688/) (2020)
- [Tweet2Vec: Character-Based Distributed Representations for Social Media](https://mlsanity.com/paper/1605.03481/) (2016)
- [Election campaigning on social media: Politicians, audiences and the mediation of political communication on Facebook and Twitter](https://mlsanity.com/paper/1801.08825/) (2018)

## Built on by (most cited first, 22 of 22)

- [ChatGPT: A Meta-Analysis after 2.5 Months](https://mlsanity.com/paper/2302.13795/) (2023)
- [Controlled Text Generation via Language Model Arithmetic](https://mlsanity.com/paper/2311.14479/) (2023)
- [Named Entity Recognition in Twitter: A Dataset and Analysis on Short-Term Temporal Shifts](https://mlsanity.com/paper/2210.03797/) (2022)
- [Robust Hate Speech Detection in Social Media: A Cross-Dataset Empirical Evaluation](https://mlsanity.com/paper/2307.01680/) (2023)
- [Backpack Language Models](https://mlsanity.com/paper/2305.16765/) (2023)
- [Simple-Sampling and Hard-Mixup with Prototypes to Rebalance Contrastive Learning for Text Classification](https://mlsanity.com/paper/2405.11524/) (2024)
- [Classical Out-of-Distribution Detection Methods Benchmark in Text Classification Tasks](https://mlsanity.com/paper/2307.07002/) (2023)
- [Detection and Analysis of Offensive Online Content in Hausa Language](https://mlsanity.com/paper/2311.10541/) (2023)
- [Do Machine Learning Models Learn Statistical Rules Inferred from Data?](https://mlsanity.com/paper/2303.01433/) (2023)
- [Tweet Insights: A Visualization Platform to Extract Temporal Insights from Twitter](https://mlsanity.com/paper/2308.02142/) (2023)
- [MetRoBERTa: Leveraging Traditional Customer Relationship Management Data to Develop a Transit-Topic-Aware Language Model](https://mlsanity.com/paper/2308.05012/) (2023)
- [FAQ-Gen: An automated system to generate domain-specific FAQs to aid content comprehension](https://mlsanity.com/paper/2402.05812/) (2024)
- [Deep RL with Hierarchical Action Exploration for Dialogue Generation](https://mlsanity.com/paper/2303.13465/) (2023)
- [PETA: Parameter-Efficient Trojan Attacks](https://mlsanity.com/paper/2310.00648/) (2023)
- [Alquist 5.0: Dialogue Trees Meet Generative Models. A Novel Approach for Enhancing SocialBot Conversations](https://mlsanity.com/paper/2310.16119/) (2023)
- [Polarization by Default: Auditing Recommendation Bias in LLM-Based Content Curation](https://mlsanity.com/paper/2604.15937/) (2026)
- [Curating corpora with classifiers: A case study of clean energy sentiment online](https://mlsanity.com/paper/2305.03092/) (2023)
- [Model-Based Simulation for Optimising Smart Reply](https://mlsanity.com/paper/2305.16852/) (2023)
- [Uncertainty-driven Embedding Convolution](https://mlsanity.com/paper/2507.20718/) (2025)
- [TopiCLEAR: Adaptive embedding clustering for interpretable topic discovery from short texts](https://mlsanity.com/paper/2512.06694/) (2025)
- [Recommender system in X inadvertently profiles ideological positions of users](https://mlsanity.com/paper/2602.02624/) (2026)
- [Improving Topic Modeling by Distilling Soft Labels from Language Models](https://mlsanity.com/paper/2602.17907/) (2026)

## Abstract (arXiv)

Social media platforms host discussions about a wide variety of topics that arise everyday. Making sense of all the content and organising it into categories is an arduous task. A common way to deal with this issue is relying on topic modeling, but topics discovered using this technique are difficult to interpret and can differ from corpus to corpus. In this paper, we present a new task based on tweet topic classification and release two associated datasets. Given a wide range of topics covering the most important discussion points in social media, we provide training and testing data from recent time periods that can be used to evaluate tweet classification models. Moreover, we perform a quantitative evaluation and analysis of current general- and domain-specific language models on the task, which provide more insights on the challenges and nature of the task.