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Found 17 papers, 1 papers with code
Y Social: an LLM-powered Social Media Digital Twin
In this paper we introduce Y, a new-generation digital twin designed to replicate an online social media platform.
Cognitive network science reveals bias in GPT-3, ChatGPT, and GPT-4 mirroring math anxiety in high-school students
We use data obtained by probing the three LLMs in a language generation task that has previously been applied to humans.
Towards hypergraph cognitive networks as feature-rich models of knowledge
Semantic networks provide a useful tool to understand how related concepts are retrieved from memory.
Cognitive modelling with multilayer networks: Insights, advancements and future challenges
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.
Writing about COVID-19 vaccines: Emotional profiling unravels how mainstream and alternative press framed AstraZeneca, Pfizer and vaccination campaigns
Our findings expose crucial aspects of the emotional narratives around COVID-19 vaccines adopted by the press, highlighting the need to understand how alternative and mainstream media report vaccination news.
Feature-rich multiplex lexical networks reveal mental strategies of early language learning
Modelling words as vectors is key to natural language processing, whereas networks of word associations can map the nature of semantic memory.
Cognitive network science quantifies feelings expressed in suicide letters and Reddit mental health communities
Our quantitative comparisons provide strong evidence that suicide notes encapsulate different ways of expressing feelings compared to online Reddit boards, the latter acting more like personal diaries and relief valve.
DASentimental: Detecting depression, anxiety and stress in texts via emotional recall, cognitive networks and machine learning
We find that semantic distances between recalled emotions and the dyad "sad-happy" are crucial features for estimating depression levels but are less important for anxiety and stress.
Network psychometrics and cognitive network science open new ways for detecting, understanding and tackling the complexity of math anxiety: A review
This review of data-informed studies outlines math anxiety as a complex system that: (i) cripples well-being, self-confidence and information processing on both conscious and subconscious levels, (ii) can be transmitted by social interactions, like a pathogen, and worsened by distorted perceptions, (iii) affects roughly 20% of students in 63 out of 64 worldwide educational systems but correlates weakly with academic performance, and (iv) poses a concrete threat to students' well-being, computational literacy and career prospects in science.
Cognitive networks identify the content of English and Italian popular posts about COVID-19 vaccines: Anticipation, logistics, conspiracy and loss of trust
Semantic associations with "vaccine," "hoax" and conspiratorial jargon indicated the persistence of conspiracy theories and vaccines in massively read English posts (absent in Italian messages).
Cognitive network science for understanding online social cognitions: A brief review
Social media are digitalising massive amounts of users' cognitions in terms of timelines and emotional content.
Revealing semantic and emotional structure of suicide notes with cognitive network science
We show that suicide notes are affectively compartmentalized such that positive concepts tend to cluster together and dominate the overall network structure.
Distance entropy cartography characterises centrality in complex networks
We introduce distance entropy as a measure of homogeneity in the distribution of path lengths between a given node and its neighbours in a complex network.
Multiplex model of mental lexicon reveals explosive learning in humans
Our findings provide quantitative confirmation of existing conjectures about core structure in the mental lexicon and the importance of integrating multi-relational word-word interactions in psycholinguistic frameworks.
Multiplex lexical networks reveal patterns in early word acquisition in children
Network models of language have provided a way of linking cognitive processes to the structure and connectivity of language.
Mental Lexicon Growth Modelling Reveals the Multiplexity of the English Language
In this work we extend previous analyses of linguistic networks by adopting a multi-layer network framework for modelling the human mental lexicon, i. e. an abstract mental repository where words and concepts are stored together with their linguistic patterns.
Patterns in the English Language: Phonological Networks, Percolation and Assembly Models
In this paper we provide a quantitative framework for the study of phonological networks (PNs) for the English language by carrying out principled comparisons to null models, either based on site percolation, randomization techniques, or network growth models.
