# Tweet2Vec: Character-Based Distributed Representations for Social Media

arXiv: https://arxiv.org/abs/1605.03481 · HTML: https://mlsanity.com/paper/1605.03481/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2016.

## Built on by (most cited first, 9 of 9)

- [Twitter Topic Classification](https://mlsanity.com/paper/2209.09824/) (2022)
- [Benchmarking Multilabel Topic Classification in the Kyrgyz Language](https://mlsanity.com/paper/2308.15952/) (2023)
- [Repo2Vec: A Comprehensive Embedding Approach for Determining Repository Similarity](https://mlsanity.com/paper/2107.05112/) (2021)
- [Cold Start Active Learning Strategies in the Context of Imbalanced Classification](https://mlsanity.com/paper/2201.10227/) (2022)
- [Learning distributed representations with efficient SoftMax normalization](https://mlsanity.com/paper/2303.17475/) (2023)
- [Graph-of-Tweets: A Graph Merging Approach to Sub-event Identification](https://mlsanity.com/paper/2101.03208/) (2021)
- [UnibucKernel: Geolocating Swiss German Jodels Using Ensemble Learning](https://mlsanity.com/paper/2102.09379/) (2021)
- [Modelling the Impact of Scandals: the case of the 2017 French Presidential Election](https://mlsanity.com/paper/2101.11548/) (2021)
- [Tracing Topic Transitions with Temporal Graph Clusters](https://mlsanity.com/paper/2104.07836/) (2021)

## Abstract (arXiv)

Text from social media provides a set of challenges that can cause traditional NLP approaches to fail. Informal language, spelling errors, abbreviations, and special characters are all commonplace in these posts, leading to a prohibitively large vocabulary size for word-level approaches. We propose a character composition model, tweet2vec, which finds vector-space representations of whole tweets by learning complex, non-local dependencies in character sequences. The proposed model outperforms a word-level baseline at predicting user-annotated hashtags associated with the posts, doing significantly better when the input contains many out-of-vocabulary words or unusual character sequences. Our tweet2vec encoder is publicly available.