arXiv subjects

Yoed N. Kenett

Publications and source records attributed to Yoed N. Kenett.

7 recordsLinked to original sources

arXiv2025

Investigating the originality of scientific papers across time and domain: A quantitative analysis

The study of creativity in science has long sought quantitative metrics capable of capturing the originality of the scientific insights contained within articles and other scientific works. In recent years, the field has witnessed a substantial expansion of research activity, enabled by advances in natural language processing and network analysis, and has utilised both macro- and micro-scale approaches with success. However, they often do not examine the text itself for evidence of originality. In this paper, we apply a computational measure correlating with originality from creativity science, Divergent Semantic Integration (DSI), to a set of 51,200 scientific abstracts and titles sourced from the Web of Science. To adapt DSI for application to scientific texts, we advance the original BERT method by incorporating SciBERT (a model trained on scientific corpora) into the computation of DSI. In our study, we observe that DSI plays a more pronounced role in the accrual of early citations for papers with fewer authors, varies substantially across subjects and research fields, and exhibits a declining correlation with citation counts over time. Furthermore, by modelling SciBERT- and BERT-DSI as predictors of the logarithm of 5-year citation counts alongside field, publication year, and the logarithm of author count, we find statistically significant relationships, with adjusted R-squared of 0.103 and 0.101 for BERT-DSI and SciBERT-DSI. Because existing scientometric measures rarely assess the originality expressed in textual content, DSI provides a valuable means of directly quantifying the conceptual originality embedded in scientific writing.

cs.DL↗

arXiv2025

Stable Emotional Co-occurrence Patterns Revealed by Network Analysis of Social Media

Examining emotion interactions as an emotion network in social media offers key insights into human psychology, yet few studies have explored how fluctuations in such emotion network evolve during crises and normal times. This study proposes a novel computational approach grounded in network theory, leveraging large-scale Japanese social media data spanning varied crisis events (earthquakes and COVID-19 vaccination) and non-crisis periods over the past decade. Our analysis identifies and evaluates links between emotions through the co-occurrence of emotion-related concepts (words), revealing a stable structure of emotion network across situations and over time at the population level. We find that some emotion links (represented as link strength) such as emotion links associated with Tension are significantly strengthened during earthquake and pre-vaccination periods. However, the rank of emotion links remains highly intact. These findings challenge the assumption that emotion co-occurrence is context-based and offer a deeper understanding of emotions' intrinsic structure. Moreover, our network-based framework offers a systematic, scalable method for analyzing emotion co-occurrence dynamics, opening new avenues for psychological research using large-scale textual data.

cs.SI↗

arXiv2025

Cognitive networks highlight differences and similarities in the STEM mindsets of human and LLM-simulated trainees, experts and academics

Understanding attitudes towards STEM means quantifying the cognitive and emotional ways in which individuals, and potentially large language models too, conceptualise such subjects. This study uses behavioural forma mentis networks (BFMNs) to investigate the STEM-focused mindset, i.e. ways of associating and perceiving ideas, of 177 human participants and 177 artificial humans simulated by GPT-3.5. Participants were split in 3 groups - trainees, experts and academics - to compare the influence of expertise level on their mindset. The results revealed that human forma mentis networks exhibited significantly higher clustering coefficients compared to GPT-3.5, indicating that human mindsets displayed a tendency to form and close triads of conceptual associations while recollecting STEM ideas. Human experts, in particular, demonstrated robust clustering coefficients, reflecting better integration of STEM concepts into their cognitive networks. In contrast, GPT-3.5 produced sparser mindsets. Furthermore, both human and GPT mindsets framed mathematics in neutral or positive terms, differently from STEM high schoolers, researchers and other large language models sampled in other works. This research contributes to understanding how mindset structure can provide cognitive insights about memory structure and machine limitations.

cs.CL↗

arXiv2025

Originality in scientific titles and abstracts can predict citation count

In this research-in-progress paper, we apply a computational measure correlating with originality from creativity science: Divergent Semantic Integration (DSI), to a selection of 99,557 scientific abstracts and titles selected from the Web of Science. We observe statistically significant differences in DSI between subject and field of research, and a slight rise in DSI over time. We model the base 10 logarithm of the citation count after 5 years with DSI and find a statistically significant positive correlation in all fields of research with an adjusted $R^2$ of 0.13.

cs.DL↗

arXiv2025

Applying Text Mining to Analyze Human Question Asking in Creativity Research

Creativity relates to the ability to generate novel and effective ideas in the areas of interest. How are such creative ideas generated? One possible mechanism that supports creative ideation and is gaining increased empirical attention is by asking questions. Question asking is a likely cognitive mechanism that allows defining problems, facilitating creative problem solving. However, much is unknown about the exact role of questions in creativity. This work presents an attempt to apply text mining methods to measure the cognitive potential of questions, taking into account, among others, (a) question type, (b) question complexity, and (c) the content of the answer. This contribution summarizes the history of question mining as a part of creativity research, along with the natural language processing methods deemed useful or helpful in the study. In addition, a novel approach is proposed, implemented, and applied to five datasets. The experimental results obtained are comprehensively analyzed, suggesting that natural language processing has a role to play in creative research.

cs.CL↗

arXiv2022

Cognitive modelling with multilayer networks: Insights, advancements and future challenges

The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Decades of psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic, phonological, syntactic, and other types of conceptual associations be mapped within a coherent mathematical framework to study how the mental lexicon works? We here review cognitive multilayer networks as a promising quantitative and interpretative framework for investigating the mental lexicon. Cognitive multilayer networks can map multiple types of information at once, thus capturing how different layers of associations might co-exist within the mental lexicon and influence cognitive processing. This review starts with a gentle introduction to the structure and formalism of multilayer networks. We then discuss quantitative mechanisms of psychological phenomena that could not be observed in single-layer networks and were only unveiled by combining multiple layers of the lexicon: (i) multiplex viability highlights language kernels and facilitative effects of knowledge processing in healthy and clinical populations; (ii) multilayer community detection enables contextual meaning reconstruction depending on psycholinguistic features; (iii) layer analysis can mediate latent interactions of mediation, suppression and facilitation for lexical access. By outlining novel quantitative perspectives where multilayer networks can shed light on cognitive knowledge representations, also in next-generation brain/mind models, we discuss key limitations and promising directions for cutting-edge future research.

cs.CL↗

arXiv2014

Resilience of modular complex networks

Complex networks often have a modular structure, where a number of tightly- connected groups of nodes (modules) have relatively few interconnections. Modularity had been shown to have an important effect on the evolution and stability of biological networks, on the scalability and efficiency of large-scale infrastructure, and the development of economic and social systems. An analytical framework for understanding modularity and its effects on network vulnerability is still missing. Through recent advances in the understanding of multilayer networks, however, it is now possible to develop a theoretical framework to systematically study this critical issue. Here we study, analytically and numerically, the resilience of modular networks under attacks on interconnected nodes, which exhibit high betweenness values and are often more exposed to failure. Our model provides new understandings into the feedback between structure and function in real world systems, and consequently has important implications as diverse as developing efficient immunization strategies, designing robust large-scale infrastructure, and understanding brain function.

physics.soc-ph↗