# SciTweets -- A Dataset and Annotation Framework for Detecting Scientific Online Discourse

arXiv: https://arxiv.org/abs/2206.07360 · HTML: https://mlsanity.com/paper/2206.07360/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 15 Jun 2022.

## Builds on (most cited first, 16 of 16)

- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Fact or Fiction: Verifying Scientific Claims](https://mlsanity.com/paper/2004.14974/) (2020)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims](https://mlsanity.com/paper/2012.00614/) (2020)
- [Automated Fact-Checking for Assisting Human Fact-Checkers](https://mlsanity.com/paper/2103.07769/) (2021)
- [COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic](https://mlsanity.com/paper/2106.03794/) (2021)
- [Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society](https://mlsanity.com/paper/2005.00033/) (2020)
- [Generating Scientific Claims for Zero-Shot Scientific Fact Checking](https://mlsanity.com/paper/2203.12990/) (2022)
- [Quantifying Controversy in Social Media](https://mlsanity.com/paper/1507.05224/) (2015)
- [Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims](https://mlsanity.com/paper/2204.12294/) (2022)
- [TweetsCOV19 -- A Knowledge Base of Semantically Annotated Tweets about the COVID-19 Pandemic](https://mlsanity.com/paper/2006.14492/) (2020)
- [SciClops: Detecting and Contextualizing Scientific Claims for Assisting Manual Fact-Checking](https://mlsanity.com/paper/2110.13090/) (2021)
- [Extracting Core Claims from Scientific Articles](https://mlsanity.com/paper/1707.07678/) (2017)
- [SciLens News Platform: A System for Real-Time Evaluation of News Articles](https://mlsanity.com/paper/2008.12039/) (2020)
- [Investigating Dissemination of Scientific Information on Twitter: A Study of Topic Networks in Opioid Publications](https://mlsanity.com/paper/2101.11483/) (2021)

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

- [The CLEF-2025 CheckThat! Lab: Subjectivity, Fact-Checking, Claim Normalization, and Retrieval](https://mlsanity.com/paper/2503.14828/) (2025)
- [Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues](https://mlsanity.com/paper/2211.05289/) (2022)
- [TACO -- Twitter Arguments from COnversations](https://mlsanity.com/paper/2404.00406/) (2024)
- [DS@GT at CheckThat! 2025: Ensemble Methods for Detection of Scientific Discourse on Social Media](https://mlsanity.com/paper/2507.06205/) (2025)
- [DS@GT at CheckThat! 2025: Exploring Retrieval and Reranking Pipelines for Scientific Claim Source Retrieval on Social Media Discourse](https://mlsanity.com/paper/2507.06563/) (2025)
- [TurQUaz at CheckThat! 2025: Debating Large Language Models for Scientific Web Discourse Detection](https://mlsanity.com/paper/2508.08265/) (2025)
- [NFDI4DS Shared Tasks for Scholarly Document Processing](https://mlsanity.com/paper/2509.22141/) (2025)
- [SciClaimSeekers at CheckThat! 2026: Retrieving Scientific Sources for Social Media Claims with LLM Reranking](https://mlsanity.com/paper/2607.24803/) (2026)
- [Identifying Scientists on X](https://mlsanity.com/paper/2609.31264/) (2026)

## Abstract (arXiv)

Scientific topics, claims and resources are increasingly debated as part of online discourse, where prominent examples include discourse related to COVID-19 or climate change. This has led to both significant societal impact and increased interest in scientific online discourse from various disciplines. For instance, communication studies aim at a deeper understanding of biases, quality or spreading pattern of scientific information whereas computational methods have been proposed to extract, classify or verify scientific claims using NLP and IR techniques. However, research across disciplines currently suffers from both a lack of robust definitions of the various forms of science-relatedness as well as appropriate ground truth data for distinguishing them. In this work, we contribute (a) an annotation framework and corresponding definitions for different forms of scientific relatedness of online discourse in Tweets, (b) an expert-annotated dataset of 1261 tweets obtained through our labeling framework reaching an average Fleiss Kappa kappa of 0.63, (c) a multi-label classifier trained on our data able to detect science-relatedness with 89% F1 and also able to detect distinct forms of scientific knowledge (claims, references). With this work we aim to lay the foundation for developing and evaluating robust methods for analysing science as part of large-scale online discourse.