The effect of the pandemic on European narratives on smart cities and surveillance.

This article presents an analysis of European smart city narratives and how they evolved under the pressure of the COVID-19 pandemic. We start with Joss et al.'s observation that the smart-city discourse is presently in flux, engaged in intensive boundary-work and struggling to gain wider support. We approach this process from the critical perspective of surveillance capitalism, as proposed by Zuboff, to highlight the growing privacy concerns related to technological development. Our results are based on analysing 184 articles regarding smart-city solutions, published on social media by five European journals between 2017 and 2021. We adopted both human and machine coding processes for qualitative and quantitative analysis of our data. As a result, we identified the main actors and four dominant narratives: regulation of artificial intelligence and facial recognition, technological fight with the climate emergency, contact tracing apps and the potential of 5G technology to boost the digitalisation processes. Our analysis shows the growing number of positive narratives underlining the importance of technology in fighting the pandemic and mitigating the climate emergency, but the latter is often mentioned in a tokenistic fashion. Right to privacy considerations are central for two out of four discovered topics. We found that the main rationale for the development of surveillance technologies relates to the competitiveness of the EU in the global technological rivalry, while ambitions like increasing societal well-being or safeguarding the transparency of new policies are nearly non-existent.

Structural measures of similarity and complementarity in complex networks.

The principle of similarity, or homophily, is often used to explain patterns observed in complex networks such as transitivity and the abundance of triangles (3-cycles). However, many phenomena from division of labor to protein-protein interactions (PPI) are driven by complementarity (differences and synergy). Here we show that the principle of complementarity is linked to the abundance of quadrangles (4-cycles) and dense bipartite-like subgraphs. We link both principles to their characteristic motifs and introduce two families of coefficients of: (1) structural similarity, which generalize local clustering and closure coefficients and capture the full spectrum of similarity-driven structures; (2) structural complementarity, defined analogously but based on quadrangles instead of triangles. Using multiple social and biological networks, we demonstrate that the coefficients capture structural properties related to meaningful domain-specific phenomena. We show that they allow distinguishing between different kinds of social relations as well as measuring an increasing structural diversity of PPI networks across the tree of life. Our results indicate that some types of relations are better explained by complementarity than homophily, and may be useful for improving existing link prediction methods. We also introduce a Python package implementing efficient algorithms for calculating the proposed coefficients.

The Effect of Context and Individual Differences in Human-Generated Randomness.

Many psychological studies have shown that human-generated sequences are hardly ever random in the strict mathematical sense. However, what remains an open question is the degree to which this (in)ability varies between people and is affected by contextual factors. Herein, we investigated this problem. In two studies, we used a modern, robust measure of randomness based on algorithmic information theory to assess human-generated series. In Study 1 ( N=183 ), in a factorial design with task description as a between-subjects variable, we tested the effects of context and mental fatigue on human-generated randomness. In Study 2 ( N=266 ), in online research, in experimental design, we further investigated the effect of mental fatigue on the randomness of human-generated series and the relationship between the need for cognition (NFC) and the ability to produce random-like series. Results of Study 1 show that the activation of the ability to produce random-like series depends on the relevance of the contextual cues ( χ2(2)=7.9828,p=.0192 ), whether they activate known representations of a random series generator and consequently help to avoid the production of trivial sequences. Our findings from both studies on the effect of mental fatigue (Study 1 - t(47,529.5568)=-18.62,p<.001 ; Study 2 - F(edf=3.587,Ref.df=3.587)=11.863,p<.0001 ) and cognitive motivation ( t(180)=2.66,p=.009 ) demonstrate that regardless of the context or task's novelty people quickly lose interest in the random series generation. Therefore, their performance decreases over time. However, people high in the NFC can maintain the cognitive motivation for a longer period and consequently on average generate more random series. In general, our results suggest that when contextual cues and intrinsic constraints are in optimal interaction people can temporarily escape the structured and trivial patterns and produce more random-like sequences.

Revealing semantic and emotional structure of suicide notes with cognitive network science.

Understanding how people who commit suicide perceive their cognitive states and emotions represents an important open scientific challenge. We build upon cognitive network science, psycholinguistics and semantic frame theory to introduce a network representation of suicidal ideation as expressed in multiple suicide notes. By reconstructing the knowledge structure of such notes, we reveal interconnections between the ideas and emotional states of people who committed suicide through an analysis of emotional balance motivated by structural balance theory, semantic prominence and emotional profiling. Our results indicate that connections between positively- and negatively-valenced terms give rise to a degree of balance that is significantly higher than in a null model where the affective structure is randomized and in a linguistic baseline model capturing mind-wandering in absence of suicidal ideation. We show that suicide notes are affectively compartmentalized such that positive concepts tend to cluster together and dominate the overall network structure. Notably, this positive clustering diverges from perceptions of self, which are found to be dominated by negative, sad conceptual associations in analyses based on subject-verb-object relationships and emotional profiling. A key positive concept is "love", which integrates information relating the self to others and is semantically prominent across suicide notes. The emotions constituting the semantic frame of "love" combine joy and trust with anticipation and sadness, which can be linked to psychological theories of meaning-making as well as narrative psychology. Our results open new ways for understanding the structure of genuine suicide notes and may be used to inform future research on suicide prevention.