# TWEETQA: A Social Media Focused Question Answering Dataset

arXiv: https://arxiv.org/abs/1907.06292 · HTML: https://mlsanity.com/paper/1907.06292/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2019.

## Built on by (most cited first, 30 of 55)

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- [Leveraging QA Datasets to Improve Generative Data Augmentation](https://mlsanity.com/paper/2205.12604/) (2022)
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- [Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?](https://mlsanity.com/paper/2302.08143/) (2023)
- [SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research](https://mlsanity.com/paper/2310.14757/) (2023)
- [Amuro and Char: Analyzing the Relationship between Pre-Training and Fine-Tuning of Large Language Models](https://mlsanity.com/paper/2408.06663/) (2024)
- [Brevity is the soul of sustainability: Characterizing LLM response lengths](https://mlsanity.com/paper/2506.08686/) (2025)
- [More Than Reading Comprehension: A Survey on Datasets and Metrics of Textual Question Answering](https://mlsanity.com/paper/2109.12264/) (2021)

## Abstract (arXiv)

With social media becoming increasingly pop-ular on which lots of news and real-time eventsare reported, developing automated questionanswering systems is critical to the effective-ness of many applications that rely on real-time knowledge. While previous datasets haveconcentrated on question answering (QA) forformal text like news and Wikipedia, wepresent the first large-scale dataset for QA oversocial media data. To ensure that the tweetswe collected are useful, we only gather tweetsused by journalists to write news articles. Wethen ask human annotators to write questionsand answers upon these tweets. Unlike otherQA datasets like SQuAD in which the answersare extractive, we allow the answers to be ab-stractive. We show that two recently proposedneural models that perform well on formaltexts are limited in their performance when ap-plied to our dataset. In addition, even the fine-tuned BERT model is still lagging behind hu-man performance with a large margin. Our re-sults thus point to the need of improved QAsystems targeting social media text.