# Tweet2Vec: Character-Based Distributed Representations for Social Media Source: [Tweet2Vec: Character-Based Distributed Representations for Social Media]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:19:32.265Z Authors: Bhuwan Dhingra, Zhong Zhou, Dylan Fitzpatrick, Michael Muehl, William W. Cohen Published: 2016-05-11T15:30:09.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Tag Prediction on Twitter dataset](): Tag prediction; Twitter dataset; Precision@1: 28.4. ## Original sources and implementation [Original paper]() [PDF]()