# TweetsKB: A Public and Large-Scale RDF Corpus of Annotated Tweets

arXiv: https://arxiv.org/abs/1810.10308 · HTML: https://mlsanity.com/paper/1810.10308/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2018.

## Built on by (most cited first, 6 of 6)

- [AI-Generated Faces in the Real World: A Large-Scale Case Study of Twitter Profile Images](https://mlsanity.com/paper/2404.14244/) (2024)
- [MM-Claims: A Dataset for Multimodal Claim Detection in Social Media](https://mlsanity.com/paper/2205.01989/) (2022)
- [OEKG: The Open Event Knowledge Graph](https://mlsanity.com/paper/2302.14688/) (2023)
- [Beyond the Explicit: A Bilingual Dataset for Dehumanization Detection in Social Media](https://mlsanity.com/paper/2510.18582/) (2025)
- [MigrationsKB: A Knowledge Base of Public Attitudes towards Migrations and their Driving Factors](https://mlsanity.com/paper/2108.07593/) (2021)
- [DS@GT at CheckThat! 2025: Ensemble Methods for Detection of Scientific Discourse on Social Media](https://mlsanity.com/paper/2507.06205/) (2025)

## Abstract (arXiv)

Publicly available social media archives facilitate research in a variety of fields, such as data science, sociology or the digital humanities, where Twitter has emerged as one of the most prominent sources. However, obtaining, archiving and annotating large amounts of tweets is costly. In this paper, we describe TweetsKB, a publicly available corpus of currently more than 1.5 billion tweets, spanning almost 5 years (Jan'13-Nov'17). Metadata information about the tweets as well as extracted entities, hashtags, user mentions and sentiment information are exposed using established RDF/S vocabularies. Next to a description of the extraction and annotation process, we present use cases to illustrate scenarios for entity-centric information exploration, data integration and knowledge discovery facilitated by TweetsKB.