Cognitive modelling of concepts in the mental lexicon with multilayer networks: Insights, advancements, and future challenges

Psychonomic Bulletin & Review, Journal Year: 2024, Volume and Issue: 31(5), P. 1981 - 2004

Published: March 4, 2024

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

Language: Английский

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EmoAtlas: An emotional network analyzer of texts that merges psychological lexicons, artificial intelligence, and network science

Behavior Research Methods, Journal Year: 2025, Volume and Issue: 57(2)

Published: Jan. 27, 2025

Language: Английский

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Introducing CounseLLMe: A dataset of simulated mental health dialogues for comparing LLMs like Haiku, LLaMAntino and ChatGPT against humans

Emerging Trends in Drugs Addictions and Health, Journal Year: 2025, Volume and Issue: unknown, P. 100170 - 100170

Published: Jan. 1, 2025

Language: Английский

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Brexit and bots: characterizing the behaviour of automated accounts on Twitter during the UK election

EPJ Data Science, Journal Year: 2022, Volume and Issue: 11(1)

Published: March 22, 2022

Abstract Online Social Networks (OSNs) offer new means for political communications that have quickly begun to play crucial roles in campaigns, due their pervasiveness and communication speed. However, the OSN environment is quite slippery hides potential risks: many studies presented evidence about presence of d/misinformation campaigns malicious activities by genuine or automated users, putting at severe risk efficiency online offline campaigns. This phenomenon particularly evident during events, as elections. In present paper, we provide a comprehensive description networks interactions among users bots UK elections 2019. particular, focus on polarised discussion Brexit Twitter, analysing data set made more than 10 millions tweets posted over month. We found accounts infected debate days before national elections, which find steep increase discussion; after election day, incidence returned values similar ones observed few weeks On other hand, number suspended (i.e. were removed platform some violation Twitter policy) remained constant until it reached significantly higher values. Remarkably, TV between Boris Johnson Jeremy Corbyn, injection large novel whose behaviour markedly different from pre-existing ones. Finally, explored bots’ orientation, finding activity spread across whole spectrum, although proportions, studied usage hashtags URLs targeting formation common narratives sides debate.

Language: Английский

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Emotional profiling and cognitive networks unravel how mainstream and alternative press framed AstraZeneca, Pfizer and COVID-19 vaccination campaigns

Scientific Reports, Journal Year: 2022, Volume and Issue: 12(1)

Published: Aug. 24, 2022

Abstract COVID-19 vaccines have been largely debated by the press. To understand how mainstream and alternative media vaccines, we introduce a paradigm reconstructing time-evolving narrative frames via cognitive networks natural language processing. We study Italian news articles massively re-shared on Facebook/Twitter (up to 5 million times), covering 5745 vaccine-related from 17 outlets over 8 months. find consistently high trust/anticipation low disgust in way sources framed “vaccine/vaccino”. These emotions were crucially missing outlets. News titles “AstraZeneca” with sadness, absent titles. Initially, linked mostly “Pfizer” side effects (e.g. “allergy”, “reaction”, “fever”). With temporary suspension of “AstraZeneca”, negative associations shifted: Mainstream prominently effects, while underwent positive valence shift, its higher efficacy. Simultaneously, thrombosis fearful conceptual entered frame death changed context, i.e. rather than hopefully preventing deaths, could be reported as potential causes death, increasing fear. Our findings expose crucial aspects emotional narratives around adopted press, highlighting need report vaccination news.

Language: Английский

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Cognitive Networks Extract Insights on COVID-19 Vaccines from English and Italian Popular Tweets: Anticipation, Logistics, Conspiracy and Loss of Trust

Big Data and Cognitive Computing, Journal Year: 2022, Volume and Issue: 6(2), P. 52 - 52

Published: May 12, 2022

Monitoring social discourse about COVID-19 vaccines is key to understanding how large populations perceive vaccination campaigns. This work reconstructs popular and trending posts framed semantically emotionally on Twitter. We achieve this by merging natural language processing, cognitive network science AI-based image analysis. focus 4765 unique tweets in English or Italian between December 2020 March 2021. One tweet contained our data set was liked around 495,000 times, highlighting could cognitively affect parts of the population. investigate both text multimedia content build a syntactic/semantic associations messages, including emotional cues pictures. representation indicates online users linked ideas along specific semantic/emotional content. The semantic frame “vaccine” highly polarised trust/anticipation (towards vaccine as scientific asset saving lives) anger/sadness (mentioning critical issues with dose administering). Semantic “vaccine,” “hoax” conspiratorial jargon indicated persistence conspiracy theories extremely posts. Interestingly, these were absent messages. Popular images people wearing face masks used that lacked trust joy found showing no masks. difference negative effect attributed face-covering discourse. Behavioural analysis revealed tendency for share eliciting joy, sadness disgust like sad messages less. Both patterns indicate an interplay emotions diffusion beyond sentiment. After its suspension mid-March 2021, “AstraZeneca” associated trustful driven experts. deaths small number vaccinated mid-March, crucially replacing earlier levels deep sadness. Our results stress networks innovative processing open new ways reconstructing perceptions trust.

Language: Английский

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Instagram-Based Benchmark Dataset for Cyberbullying Detection in Arabic Text

Data, Journal Year: 2022, Volume and Issue: 7(7), P. 83 - 83

Published: June 22, 2022

(1) Background: the ability to use social media communicate without revealing one’s real identity has created an attractive setting for cyberbullying. Several studies targeted collect their datasets with aim of automatically detecting offensive language. However, majority were in English, not Arabic. Even few Arabic that collected, none focused on Instagram despite being a major platform Arab world. (2) Methods: we official APIs our dataset. To consider dataset as benchmark, SPSS (Kappa statistic) evaluate inter-annotator agreement (IAA), well examine and performance various learning models (LR, SVM, RFC, MNB). (3) Results: this research, present first corpus (sub-class categorization (multi-class)) focusing The is primarily designed purpose language texts. We end up 200,000 comments, which 46,898 comments annotated by three human annotators. results show SVM classifier outperforms other classifiers, F1 score 69% bullying 85 percent positive comments.

Language: Английский

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Burstiness in Emotions: A Case Study on Collective Affective Responses in Italian Soccer Fandoms

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 56 - 69

Published: Jan. 1, 2025

Language: Английский

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DASentimental: Detecting Depression, Anxiety, and Stress in Texts via Emotional Recall, Cognitive Networks, and Machine Learning

Big Data and Cognitive Computing, Journal Year: 2021, Volume and Issue: 5(4), P. 77 - 77

Published: Dec. 13, 2021

Most current affect scales and sentiment analysis on written text focus quantifying valence/sentiment, the primary dimension of emotion. Distinguishing broader, more complex negative emotions similar valence is key to evaluating mental health. We propose a semi-supervised machine learning model, DASentimental, extract depression, anxiety, stress from text. trained DASentimental identify how N = 200 sequences recalled emotional words correlate with recallers’ Depression Anxiety Stress Scale (DASS-21). Using cognitive network science, we modeled every recall list as bag-of-words (BOW) vector walk over representation semantic memory—in this case, free associations. This weights BOW entries according their centrality (degree) in memory informs recalls using distances, thus embedding representation. translated into state-of-the-art, cross-validated predictions for depression (R 0.7), anxiety 0.44), 0.52), equivalent previous results employing additional human data. Powered by multilayer perceptron neural network, opens door probing organizations distress. found that distances between (i.e., coverage), was estimating levels but redundant levels. Semantic “fear” boosted were when “sad–happy” dyad considered. applied clinical dataset 142 suicide notes predicted (high/low) corresponded differences arousal expected circumplex model affect. discuss directions future research enabled artificial intelligence detecting stress, texts.

Language: Английский

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Voices of rape: Cognitive networks link passive voice usage to psychological distress in online narratives

Computers in Human Behavior, Journal Year: 2024, Volume and Issue: 158, P. 108266 - 108266

Published: April 17, 2024

Past studies of sexual assault have found that passive voice descriptions rape elicit an increased perception victim responsibility compared to active narratives (Bohner, 2001), contributing blaming and the perpetuation myths. Building on this, we investigate relationship between passive/active usage perception, but from perspective survivors as disclosed in their online narratives. We collect Reddit's r/sexualassault board group them into a group. detect differences two groups text using cognitive network science approach creates representations such nodes represent words/concepts while links syntactic semantic relationships them. systematically identify are significantly more central one other, thus identifying characteristic concepts semantically differentiate then contexts these applying frame analysis. find related psychological distress (e.g. PTSD, flashback) narratives, providing quantitative evidence link focus distress. also family members parent, brother) suggesting connection others' roles survivors' experiences. Our results reveal important language mental health has valuable implications for therapeutic interventions.

Language: Английский

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