Cognitive Network Science for Understanding Online Social Cognitions: A Brief Review

Cognitive modelling of concepts in the mental lexicon 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. Over decades 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? Here we 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, including in next-generation brain/mind models, we discuss key limitations and promising directions for cutting-edge future research.

The Effect of the COVID Pandemic on Clinical Psychology Research: A Bibliometric Analysis

The present bibliometric analysis used traditional measures and network science techniques to examine how the COVID-19 pandemic influenced research in Clinical Psychology. Publication records from the Web of Science (WoS) were obtained for journal articles published prior to (2015 and 2018), during (2020), and at the end of the pandemic (2022) for the search terms "men and mental health" and "women and mental health". Network analyses of author-provided keywords showed that COVID-19 co-occurred with fear, anxiety, depression, and stress for both men and women in 2020. In 2022, COVID-19 co-occurred with topics related to world-wide lockdowns (e.g., alcohol use, substance use, intimate partner violence, loneliness, physical activity), and to more fundamental topics in Clinical Psychology (e.g., eating disorders and post-traumatic stress disorder). Although the COVID pandemic was associated with several changes in the research topics that were examined in Clinical Psychology, pre-existing disparities in the amount of mental health research on men compared to women did not appear to increase (in contrast to increases associated with COVID in pre-existing gender disparities observed in other areas of society).

Emergence of Covid-19 as a Novel Concept Shifts Existing Semantic Spaces

Conceptual knowledge is dynamic, fluid, and flexible, changing as a function of contextual factors at multiple scales. The Covid-19 pandemic can be considered a large-scale, global context that has fundamentally altered most people's experiences with the world. It has also introduced a new concept, COVID (or COVID-19), into our collective knowledgebase. What are the implications of this introduction for how existing conceptual knowledge is structured? Our collective emotional and social experiences with the world have been profoundly impacted by the Covid-19 pandemic, and experience-based perspectives on concept representation suggest that emotional and social experiences are critical components of conceptual knowledge. Such changes in collective experience should, then, have downstream consequences on knowledge of emotion- and social-related concepts. Using a naturally occurring dataset derived from the social media platform Twitter, we show that semantic spaces for concepts related to our emotional experiences with Covid-19 (i.e., emotional concepts like FEAR)-but not for unrelated concepts (i.e., animals like CAT)-show quantifiable shifts as a function of the emergence of COVID-19 as a concept and its associated emotional and social experiences, shifts which persist 6 months after the onset of the pandemic. The findings support a dynamic view of conceptual knowledge wherein shared experiences affect conceptual structure.

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

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

Instagram-Based Benchmark Dataset for Cyberbullying Detection in Arabic Text

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

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

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

The Whole Is Greater than the Sum of the Parts: A Multilayer Approach on Criminal Networks

Traditional social network analysis can be generalized to model some networked systems by multilayer structures where the individual nodes develop relationships in multiple layers. A multilayer network is called multiplex if each layer shares at least one node with some other layer. In this paper, we built a unique criminal multiplex network from the pre-trial detention order by the Preliminary Investigation Judge of the Court of Messina (Sicily) issued at the end of the Montagna anti-mafia operation in 2007. Montagna focused on two families who infiltrated several economic activities through a cartel of entrepreneurs close to the Sicilian Mafia. Our network possesses three layers which share 20 nodes. The first captures meetings between suspected criminals, the second records phone calls and the third detects crimes committed by pairs of individuals. We used measures from multilayer network analysis to characterize the actors in the network based on their local edges and their relevance to each specific layer. Then, we used measures of layer similarity to study the relationships between different layers. By studying the actor connectivity and the layer correlation, we demonstrated that a complete picture of the structure and the activities of a criminal organization can be obtained only considering the three layers as a whole multilayer network and not as single-layer networks. Specifically, we showed the usefulness of the multilayer approach by bringing out the importance of actors that does not emerge by studying the three layers separately.

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

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

Socio-semantic networks as mutualistic networks

Several studies have shown that discourse and social relationships are intertwined and co-evolve. However, we lack theoretical models to explain the phenomenon. Inspired by recent work in ecology, we propose to model socio-semantic networks as an interaction between two intermingled data generating processes: a social community process and a document-based process. We consider the link between semantic and social ties as analogous to the interactions found in pollination networks whereby agents visit hidden topics in a similar way that insects visit specific plants for pollination. We use the ENRON socio-semantic email network to investigate if it exhibits properties that characterize mutualistic networks, namely moderate connectance, heterogeneous degree distribution, moderate modularity and high nestedness. To do so, we build a plant-pollinator matrix where "insect species" are communities detected via block modelling, "plant species" are latent topics detected with topic modelling, and the interaction between the two is the total number of visits a community makes to specific topics. Our results show that the ENRON socio-semantic interaction matrix respects the aforementioned criteria of mutualism paving the way for the development of a relevant framework to better understand the dynamic of human socio-semantic interactions.

Editors' Introduction to Networks of the Mind: How Can Network Science Elucidate Our Understanding of Cognition?

Thinking is complex. Over the years, several types of methods and paradigms have developed across the psychological, cognitive, and neural sciences to study such complexity. A rapidly growing multidisciplinary quantitative field of network science offers quantitative methods to represent complex systems as networks, or graphs, and study the network properties of these systems. While the application of network science to study the brain has greatly advanced our understanding of the brains structure and function, the application of these tools to study cognition has been done to a much lesser account. This topic is a collection of papers that discuss the fruitfulness of applying network science to study cognition across a wide scope of research areas from generalist accounts of memory and encoding, to individual differences, to communities, and finally to cultural and individual change.

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

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