Related papers: Measuring prominence of scientific work in online news as a proxy for impact

Measuring prominence of scientific work in online news as a proxy for impact

URL: http://arxiv.org/abs/2007.14454v1
Date: Tue, 28 Jul 2020 19:52:21 GMT
Title: Measuring prominence of scientific work in online news as a proxy for impact
Authors: James Ravenscroft and Amanda Clare and Maria Liakata
Abstract summary: We present a new corpus of newspaper articles linked to the scientific papers that they describe. We find that Impact Case studies submitted to the UK Research Excellence Framework (REF) 2014 that refer to scientific papers mentioned in newspaper articles were awarded a higher score. This supports our hypothesis that linguistic prominence in news can be used to suggest the wider non-academic impact of scientific work.
Score: 15.772621977756058
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: The impact made by a scientific paper on the work of other academics has many established metrics, including metrics based on citation counts and social media commenting. However, determination of the impact of a scientific paper on the wider society is less well established. For example, is it important for scientific work to be newsworthy? Here we present a new corpus of newspaper articles linked to the scientific papers that they describe. We find that Impact Case studies submitted to the UK Research Excellence Framework (REF) 2014 that refer to scientific papers mentioned in newspaper articles were awarded a higher score in the REF assessment. The papers associated with these case studies also feature prominently in the newspaper articles. We hypothesise that such prominence can be a useful proxy for societal impact. We therefore provide a novel baseline approach for measuring the prominence of scientific papers mentioned within news articles. Our measurement of prominence is based on semantic similarity through a graph-based ranking algorithm. We find that scientific papers with an associated REF case study are more likely to have a stronger prominence score. This supports our hypothesis that linguistic prominence in news can be used to suggest the wider non-academic impact of scientific work.

Related papers

Mapping the Increasing Use of LLMs in Scientific Papers [99.67983375899719]
We conduct the first systematic, large-scale analysis across 950,965 papers published between January 2020 and February 2024 on the arXiv, bioRxiv, and Nature portfolio journals. Our findings reveal a steady increase in LLM usage, with the largest and fastest growth observed in Computer Science papers.
arXiv Detail & Related papers (2024-04-01T17:45:15Z)
Position: AI/ML Influencers Have a Place in the Academic Process [82.2069685579588]
We investigate the role of social media influencers in enhancing the visibility of machine learning research. We have compiled a comprehensive dataset of over 8,000 papers, spanning tweets from December 2018 to October 2023. Our statistical and causal inference analysis reveals a significant increase in citations for papers endorsed by these influencers.
arXiv Detail & Related papers (2024-01-24T20:05:49Z)
CausalCite: A Causal Formulation of Paper Citations [80.82622421055734]
CausalCite is a new way to measure the significance of a paper by assessing the causal impact of the paper on its follow-up papers. It is based on a novel causal inference method, TextMatch, which adapts the traditional matching framework to high-dimensional text embeddings. We demonstrate the effectiveness of CausalCite on various criteria, such as high correlation with paper impact as reported by scientific experts.
arXiv Detail & Related papers (2023-11-05T23:09:39Z)
Fusion of the Power from Citations: Enhance your Influence by Integrating Information from References [3.607567777043649]
This study aims to formulate the prediction problem to identify whether one paper can increase scholars' influence or not. By applying the framework in this work, scholars can identify whether their papers can improve their influence in the future.
arXiv Detail & Related papers (2023-10-27T19:51:44Z)
Estimating the Causal Effect of Early ArXiving on Paper Acceptance [56.538813945721685]
We estimate the effect of arXiving a paper before the reviewing period (early arXiving) on its acceptance to the conference. Our results suggest that early arXiving may have a small effect on a paper's chances of acceptance.
arXiv Detail & Related papers (2023-06-24T07:45:38Z)
Detecting and analyzing missing citations to published scientific entities [5.811229506383401]
We design a special method Citation Recommendation for Published Scientific Entity (CRPSE) based on the cooccurrences between published scientific entities and in-text citations. We conduct a statistical analysis on missing citations among papers published in prestigious computer science conferences in 2020. On a median basis, the papers proposing these published scientific entities with missing citations were published 8 years ago.
arXiv Detail & Related papers (2022-10-18T18:08:20Z)
Semantic Analysis for Automated Evaluation of the Potential Impact of Research Articles [62.997667081978825]
This paper presents a novel method for vector representation of text meaning based on information theory. We show how this informational semantics is used for text classification on the basis of the Leicester Scientific Corpus. We show that an informational approach to representing the meaning of a text has offered a way to effectively predict the scientific impact of research papers.
arXiv Detail & Related papers (2021-04-26T20:37:13Z)
Enhancing Scientific Papers Summarization with Citation Graph [78.65955304229863]
We redefine the task of scientific papers summarization by utilizing their citation graph. We construct a novel scientific papers summarization dataset Semantic Scholar Network (SSN) which contains 141K research papers in different domains. Our model can achieve competitive performance when compared with the pretrained models.
arXiv Detail & Related papers (2021-04-07T11:13:35Z)
Utilizing Citation Network Structure to Predict Citation Counts: A Deep Learning Approach [0.0]
This paper proposes an end-to-end deep learning network, DeepCCP, which combines the effect of information cascade and looks at the citation counts prediction problem. According to experiments on 6 real data sets, DeepCCP is superior to the state-of-the-art methods in terms of the accuracy of citation count prediction.
arXiv Detail & Related papers (2020-09-06T05:27:50Z)
Attention: to Better Stand on the Shoulders of Giants [34.5017808610466]
This paper develops an attention mechanism for the long-term scientific impact prediction. It validates the method based on a real large-scale citation data set.
arXiv Detail & Related papers (2020-05-27T00:25:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.