# Determining the Credibility of Science Communication

arXiv: https://arxiv.org/abs/2105.14473 · HTML: https://mlsanity.com/paper/2105.14473/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 36 references that are in the map.
- First posted on arXiv 30 May 2021.

## Builds on (most cited first, 17 of 17)

- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications](https://mlsanity.com/paper/1804.09635/) (2018)
- [Construction of the Literature Graph in Semantic Scholar](https://mlsanity.com/paper/1805.02262/) (2018)
- [SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications](https://mlsanity.com/paper/1704.02853/) (2017)
- [ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks](https://mlsanity.com/paper/1909.01716/) (2019)
- [ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis](https://mlsanity.com/paper/2010.06119/) (2020)
- [A Supervised Approach to Extractive Summarisation of Scientific Papers](https://mlsanity.com/paper/1706.03946/) (2017)
- [What Can We Do to Improve Peer Review in NLP?](https://mlsanity.com/paper/2010.03863/) (2020)
- [Transformer Based Multi-Source Domain Adaptation](https://mlsanity.com/paper/2009.07806/) (2020)
- [Examining Citations of Natural Language Processing Literature](https://mlsanity.com/paper/2005.00912/) (2020)
- [Gender Gap in Natural Language Processing Research: Disparities in Authorship and Citations](https://mlsanity.com/paper/2005.00962/) (2020)
- [CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding](https://mlsanity.com/paper/2105.10912/) (2021)
- [Two Stage Transformer Model for COVID-19 Fake News Detection and Fact Checking](https://mlsanity.com/paper/2011.13253/) (2020)
- [Multi-Task Learning of Keyphrase Boundary Classification](https://mlsanity.com/paper/1704.00514/) (2017)
- [Longitudinal Citation Prediction using Temporal Graph Neural Networks](https://mlsanity.com/paper/2012.05742/) (2020)
- [University of Copenhagen Participation in TREC Health Misinformation Track 2020](https://mlsanity.com/paper/2103.02462/) (2021)

## Built on by (most cited first, 4 of 4)

- [Missci: Reconstructing Fallacies in Misrepresented Science](https://mlsanity.com/paper/2406.03181/) (2024)
- [Whose Facts Win? LLM Source Preferences under Knowledge Conflicts](https://mlsanity.com/paper/2601.03746/) (2026)
- [SciEv: Finding Scientific Evidence Papers for Scientific News](https://mlsanity.com/paper/2205.00126/) (2022)
- [User Experience Design for Automatic Credibility Assessment of News Content About COVID-19](https://mlsanity.com/paper/2204.13943/) (2022)

## Abstract (arXiv)

Most work on scholarly document processing assumes that the information processed is trustworthy and factually correct. However, this is not always the case. There are two core challenges, which should be addressed: 1) ensuring that scientific publications are credible -- e.g. that claims are not made without supporting evidence, and that all relevant supporting evidence is provided; and 2) that scientific findings are not misrepresented, distorted or outright misreported when communicated by journalists or the general public. I will present some first steps towards addressing these problems and outline remaining challenges.