# Predicting the future success of scientific publications through social network and semantic analysis

arXiv: https://arxiv.org/abs/2103.14556 · HTML: https://mlsanity.com/paper/2103.14556/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2021.

## Built on by (most cited first, 1 of 1)

- [Comprehensive Manuscript Assessment with Text Summarization Using 69707 articles](https://mlsanity.com/paper/2503.20835/) (2025)

## Abstract (arXiv)

Citations acknowledge the impact a scientific publication has on subsequent work. At the same time, deciding how and when to cite a paper, is also heavily influenced by social factors. In this work, we conduct an empirical analysis based on a dataset of 2010-2012 global publications in chemical engineering. We use social network analysis and text mining to measure publication attributes and understand which variables can better help predicting their future success. Controlling for intrinsic quality of a publication and for the number of authors in the byline, we are able to predict scholarly impact of a paper in terms of citations received six years after publication with almost 80 percent accuracy. Results suggest that, all other things being equal, it is better to co-publish with rotating co-authors and write the papers' abstract using more positive words, and a more complex, thus more informative, language. Publications that result from the collaboration of different social groups also attract more citations.