no code implementations • 8 Feb 2024 • Mike Thelwall
In contrast, the average scores from the 15 iterations produced a statistically significant positive correlation of 0. 509.
no code implementations • 11 Dec 2022 • Mike Thelwall, Kayvan Kousha, Mahshid Abdoli, Emma Stuart, Meiko Makita, Paul Wilson, Jonathan Levitt, Petr Knoth, Matteo Cancellieri
National research evaluation initiatives and incentive schemes have previously chosen between simplistic quantitative indicators and time-consuming peer review, sometimes supported by bibliometrics.
no code implementations • 11 Dec 2022 • Kayvan Kousha, Mike Thelwall
This includes studies that used machine learning techniques to predict citation counts or quality scores for journal articles or conference papers.
no code implementations • 11 Dec 2022 • Mike Thelwall, Kayvan Kousha, Mahshid Abdoli, Emma Stuart, Meiko Makita, Paul Wilson, Jonathan Levitt
This document describes strategies for using Artificial Intelligence (AI) to predict some journal article scores in future research assessment exercises.
no code implementations • 14 Jun 2020 • Mike Thelwall
The expressions found are most common in the social sciences and the humanities.
no code implementations • WS 2018 • Reshmi Gopalakrishna Pillai, Mike Thelwall, Constantin Orasan
Detecting stress from social media gives a non-intrusive and inexpensive alternative to traditional tools such as questionnaires or physiological sensors for monitoring mental state of individuals.
no code implementations • 1 Jul 2016 • Mike Thelwall
In conclusion, TensiStrength and generic machine learning approaches work well enough to be practical choices for intelligent applications that need to take advantage of stress information, and the decision about which to use depends on the nature of the texts analysed and the purpose of the task.