Chinese Social Media Reaction to Information about 42 Notifiable Infectious Diseases

This study aimed to identify what information triggered social media users’ responses regarding infectious diseases. Chinese microblogs in 2012 regarding 42 infectious diseases were obtained through a keyword search in the Weiboscope database. Qualitative content analysis was performed for the posts pertinent to each keyword of the day of the year with the highest daily count. Similar posts were grouped and coded. We identified five categories of information that increased microblog traffic pertaining to infectious diseases: news of an outbreak or a case; health education / information; alternative health information / Traditional Chinese Medicine; commercial advertisement / entertainment; and social issues. News unrelated to the specified infectious diseases also led to elevated microblog traffic. Our study showcases the diverse contexts from which increased social media traffic occur. Our results will facilitate better health communication as causes underlying increased social media traffic are revealed.     

Surveying Hard-to-Reach Groups Through Sampled Respondents in a Social Network

The sampling frame in most social science surveys misses members of certain groups, such as the homeless or individuals living with HIV. These groups are known as hard-to-reach groups. One strategy for learning about these groups, or subpopulations, involves reaching hard-to-reach group members through their social network. In this paper we compare the efficiency of two common methods for subpopulation size estimation using data from standard surveys. These designs are examples of mental link tracing designs. These designs begin with a randomly sampled set of network members (nodes) and then reach other nodes indirectly through questions asked to the sampled nodes. Mental link tracing designs cost significantly less than traditional link tracing designs, yet introduce additional sources of potential bias. We examine the influence of one such source of bias using simulation studies. We then demonstrate our findings using data from the General Social Survey collected in 2004 and 2006. Additionally, we provide survey design suggestions for future surveys incorporating such designs.     

Epidemic management and control through risk-dependent individual contact interventions

Testing, contact tracing, and isolation (TTI) is an epidemic management and control approach that is difficult to implement at scale because it relies on manual tracing of contacts. Exposure notification apps have been developed to digitally scale up TTI by harnessing contact data obtained from mobile devices; however, exposure notification apps provide users only with limited binary information when they have been directly exposed to a known infection source. Here we demonstrate a scalable improvement to TTI and exposure notification apps that uses data assimilation (DA) on a contact network. Network DA exploits diverse sources of health data together with the proximity data from mobile devices that exposure notification apps rely upon. It provides users with continuously assessed individual risks of exposure and infection, which can form the basis for targeting individual contact interventions. Simulations of the early COVID-19 epidemic in New York City are used to establish proof-of-concept. In the simulations, network DA identifies up to a factor 2 more infections than contact tracing when both harness the same contact data and diagnostic test data. This remains true even when only a relatively small fraction of the population uses network DA. When a sufficiently large fraction of the population (≳ 75%) uses network DA and complies with individual contact interventions, targeting contact interventions with network DA reduces deaths by up to a factor 4 relative to TTI. Network DA can be implemented by expanding the computational backend of existing exposure notification apps, thus greatly enhancing their capabilities. Implemented at scale, it has the potential to precisely and effectively control future epidemics while minimizing economic disruption.     

Catch the wave: prairie dogs assess neighbours’ awareness using contagious displays

The jump–yip display of black-tailed prairie dogs (Cynomys ludovicianus) is contagious, spreading through a prairie dog town as ‘the wave’ through a stadium. Because contagious communication in primates serves to assess conspecific social awareness, we investigated whether instigators of jump–yip bouts adjusted their behaviour relative to the response of conspecifics recruited to display bouts. Increased responsiveness of neighbouring town members resulted in bout initiators devoting a significantly greater proportion of time to active foraging. Contagious jump–yips thus function to assess neighbours’ alertness, soliciting social information to assess effective conspecific group size in real time and reveal active probing of conspecific awareness consistent with theory of mind in these group-living rodents.     

Navigation Using Sensory Substitution in Real and Virtual Mazes

Under certain specific conditions people who are blind have a perception of space that is equivalent to that of sighted individuals. However, in most cases their spatial perception is impaired. Is this simply due to their current lack of access to visual information or does the lack of visual information throughout development prevent the proper integration of the neural systems underlying spatial cognition? Sensory Substitution devices (SSDs) can transfer visual information via other senses and provide a unique tool to examine this question. We hypothesize that the use of our SSD (The EyeCane: a device that translates distance information into sounds and vibrations) can enable blind people to attain a similar performance level as the sighted in a spatial navigation task. We gave fifty-six participants training with the EyeCane. They navigated in real life-size mazes using the EyeCane SSD and in virtual renditions of the same mazes using a virtual-EyeCane. The participants were divided into four groups according to visual experience: congenitally blind, low vision & late blind, blindfolded sighted and sighted visual controls. We found that with the EyeCane participants made fewer errors in the maze, had fewer collisions, and completed the maze in less time on the last session compared to the first. By the third session, participants improved to the point where individual trials were no longer significantly different from the initial performance of the sighted visual group in terms of errors, time and collision.     

Gustatory imagery reveals functional connectivity from the prefrontal to insular cortices traced with magnetoencephalography

Our experience and prejudice concerning food play an important role in modulating gustatory information processing; gustatory memory stored in the central nervous system influences gustatory information arising from the peripheral nervous system. We have elucidated the mechanism of the “top-down” modulation of taste perception in humans using functional magnetic resonance imaging (fMRI) and demonstrated that gustatory imagery is mediated by the prefrontal (PFC) and insular cortices (IC). However, the temporal order of activation of these brain regions during gustatory imagery is still an open issue. To explore the source of “top-down” signals during gustatory imagery tasks, we analyzed the temporal activation patterns of activated regions in the cerebral cortex using another non-invasive brain imaging technique, magnetoencephalography (MEG). Gustatory imagery tasks were presented by words (Letter G-V) or pictures (Picture G-V) of foods/beverages, and participants were requested to recall their taste. In the Letter G-V session, 7/9 (77.8%) participants showed activation in the IC with a latency of 401.7±34.7 ms (n = 7) from the onset of word exhibition. In 5/7 (71.4%) participants who exhibited IC activation, the PFC was activated prior to the IC at a latency of 315.2±56.5 ms (n = 5), which was significantly shorter than the latency to the IC activation. In the Picture G-V session, the IC was activated in 6/9 (66.7%) participants, and only 1/9 (11.1%) participants showed activation in the PFC. There was no significant dominance between the right and left IC or PFC during gustatory imagery. These results support those from our previous fMRI study in that the Letter G-V session rather than the Picture G-V session effectively activates the PFC and IC and strengthen the hypothesis that the PFC mediates “top-down” control of retrieving gustatory information from the storage of long-term memories and in turn activates the IC.     

Social Influences in Sequential Decision Making

People often make decisions in a social environment. The present work examines social influence on people’s decisions in a sequential decision-making situation. In the first experimental study, we implemented an information cascade paradigm, illustrating that people infer information from decisions of others and use this information to make their own decisions. We followed a cognitive modeling approach to elicit the weight people give to social as compared to private individual information. The proposed social influence model shows that participants overweight their own private information relative to social information, contrary to the normative Bayesian account. In our second study, we embedded the abstract decision problem of Study 1 in a medical decision-making problem. We examined whether in a medical situation people also take others’ authority into account in addition to the information that their decisions convey. The social influence model illustrates that people weight social information differentially according to the authority of other decision makers. The influence of authority was strongest when an authority''s decision contrasted with private information. Both studies illustrate how the social environment provides sources of information that people integrate differently for their decisions.     

Time Development in the Early History of Social Networks: Link Stabilization,Group Dynamics,and Segregation

Studies of the time development of empirical networks usually investigate late stages where lasting connections have already stabilized. Empirical data on early network history are rare but needed for a better understanding of how social network topology develops in real life. Studying students who are beginning their studies at a university with no or few prior connections to each other offers a unique opportunity to investigate the formation and early development of link patterns and community structure in social networks. During a nine week introductory physics course, first year physics students were asked to identify those with whom they communicated about problem solving in physics during the preceding week. We use these students'' self reports to produce time dependent student interaction networks. We investigate these networks to elucidate possible effects of different student attributes in early network formation. Changes in the weekly number of links show that while roughly half of all links change from week to week, students also reestablish a growing number of links as they progress through their first weeks of study. Using the Infomap community detection algorithm, we show that the networks exhibit community structure, and we use non-network student attributes, such as gender and end-of-course grade to characterize communities during their formation. Specifically, we develop a segregation measure and show that students structure themselves according to gender and pre-organized sections (in which students engage in problem solving and laboratory work), but not according to end-of-coure grade. Alluvial diagrams of consecutive weeks'' communities show that while student movement between groups are erratic in the beginnning of their studies, they stabilize somewhat towards the end of the course. Taken together, the analyses imply that student interaction networks stabilize quickly and that students establish collaborations based on who is immediately available to them and on observable personal characteristics.     

Intensified Neuronal Investment in the Processing of Chemosensory Anxiety Signals in Non-Socially Anxious and Socially Anxious Individuals

Background

The ability to communicate anxiety through chemosensory signals has been documented in humans by behavioral, perceptual and brain imaging studies. Here, we investigate in a time-sensitive manner how chemosensory anxiety signals, donated by humans awaiting an academic examination, are processed by the human brain, by analyzing chemosensory event-related potentials (CSERPs, 64-channel recording with current source density analysis).

Methodology/Principal Findings

In the first study cerebral stimulus processing was recorded from 28 non-socially anxious participants and in the second study from 16 socially anxious individuals. Each individual participated in two sessions, smelling sweat samples donated from either female or male donors (88 sessions; balanced session order). Most of the participants of both studies were unable to detect the stimuli olfactorily. In non-socially anxious females, CSERPs demonstrate an increased magnitude of the P3 component in response to chemosensory anxiety signals. The source of this P3 activity was allocated to medial frontal brain areas. In socially anxious females chemosensory anxiety signals require more neuronal resources during early pre-attentive stimulus processing (N1). The neocortical sources of this activity were located within medial and lateral frontal brain areas. In general, the event-related neuronal brain activity in males was much weaker than in females. However, socially anxious males processed chemosensory anxiety signals earlier (N1 latency) than the control stimuli collected during an ergometer training.

Conclusions/Significance

It is concluded that the processing of chemosensory anxiety signals requires enhanced neuronal energy. Socially anxious individuals show an early processing bias towards social fear signals, resulting in a repression of late attentional stimulus processing.     

Competition between Homophily and Information Entropy Maximization in Social Networks

In social networks, it is conventionally thought that two individuals with more overlapped friends tend to establish a new friendship, which could be stated as homophily breeding new connections. While the recent hypothesis of maximum information entropy is presented as the possible origin of effective navigation in small-world networks. We find there exists a competition between information entropy maximization and homophily in local structure through both theoretical and experimental analysis. This competition suggests that a newly built relationship between two individuals with more common friends would lead to less information entropy gain for them. We demonstrate that in the evolution of the social network, both of the two assumptions coexist. The rule of maximum information entropy produces weak ties in the network, while the law of homophily makes the network highly clustered locally and the individuals would obtain strong and trust ties. A toy model is also presented to demonstrate the competition and evaluate the roles of different rules in the evolution of real networks. Our findings could shed light on the social network modeling from a new perspective.     

Dynamic Social Community Detection and Its Applications

Community structure is one of the most commonly observed features of Online Social Networks (OSNs) in reality. The knowledge of this feature is of great advantage: it not only provides helpful insights into developing more efficient social-aware solutions but also promises a wide range of applications enabled by social and mobile networking, such as routing strategies in Mobile Ad Hoc Networks (MANETs) and worm containment in OSNs. Unfortunately, understanding this structure is very challenging, especially in dynamic social networks where social interactions are evolving rapidly. Our work focuses on the following questions: How can we efficiently identify communities in dynamic social networks? How can we adaptively update the network community structure based on its history instead of recomputing from scratch? To this end, we present Quick Community Adaptation (QCA), an adaptive modularity-based framework for not only discovering but also tracing the evolution of network communities in dynamic OSNs. QCA is very fast and efficient in the sense that it adaptively updates and discovers the new community structure based on its history together with the network changes only. This flexible approach makes QCA an ideal framework applicable for analyzing large-scale dynamic social networks due to its lightweight computing-resource requirement. To illustrate the effectiveness of our framework, we extensively test QCA on both synthesized and real-world social networks including Enron, arXiv e-print citation, and Facebook networks. Finally, we demonstrate the applicability of QCA in real applications: (1) A social-aware message forwarding strategy in MANETs, and (2) worm propagation containment in OSNs. Competitive results in comparison with other methods reveal that social-based techniques employing QCA as a community detection core outperform current available methods.     

The social fabric of citizen science—drivers for long-term engagement in the German butterfly monitoring scheme

Insect conservation needs sound information on species distribution trends. Developing this evidence relies—in practice—on long-term engagement of volunteers who observe and record species over large spatial and temporal scales. Many biodiversity monitoring schemes, including those for insects, are highly dependent on conservation-based citizen science programs with a long-term continuity. As these schemes are built entirely on good will, the nature of social relations and networks is pivotal to success. We assess the working mechanism of a monitoring scheme that is citizen-based as a case study. The German Butterfly Monitoring Scheme (hereafter TMD for “Tagfalter-Monitoring Deutschland”) operates, as many other citizen science monitoring schemes, through an overarching national network of regional subnetworks of volunteers and a central scientific coordination. Using a questionnaire survey paired with a visual social network assessment, we investigate how participants interact within these networks and assess their motivations to engage. We characterise the functionality of this social network based on mechanism of coordination and participation, flows of information and knowledge exchange among recorders, regional and central coordinators, academic scientists and institutions. By analyzing the interactions, we show how the social network facilitates and ensures various communication modes and thereby fosters long-term engagement, stability and growth of the scheme. We identify the central role of project coordination and the importance of social relations within citizen-based monitoring programs for engagement and personal satisfaction. Based on our empirical study, we derive a set of recommendations for establishing and maintaining successful volunteer networks in insect citizen-based monitoring programs.     

In-vivo visualization of key molecular processes involved in Alzheimer's disease pathogenesis: Insights from neuroimaging research in humans and rodent models

Diverse age-associated neurodegenerative disorders are featured at a molecular level by depositions of self-aggregating molecules, as represented by amyloid β peptides (Aβ) and tau proteins in Alzheimer's disease, and cascade-type chain reactions are supposedly commenced with biochemical aberrancies of these amyloidogenic components. Mutagenesis and multiplication of the genes encoding Aβ, tau and other pathogenic initiators may accelerate the incipient process at the cascade top, rationalizing generations of transgenic and knock-in animal models of these illnesses. Meanwhile, these genetic manipulations do not necessarily compress the timelines of crucial intermediate events linking amyloidogenesis and neuronal lethality, resulting in an incomplete recapitulation of the diseases. Requirements for modeling the entire cascade can be illustrated by a side-by-side comparison of humans and animal models with the aid of imaging-based biomarkers commonly applicable to different species. Notably, key components in a highly reactive state are assayable by probe-assisted neuroimaging techniques exemplified by positron emission tomography (PET), providing critical information on the in-vivo accessibility of these target molecules. In fact, multispecies PET studies in conjunction with biochemical, electrophysiological and neuropathological tests have revealed putative neurotoxic subspecies of Aβ assemblies, translocator proteins accumulating in aggressive but not neuroprotective microglia, and functionally active neuroreceptors available to endogenous neurotransmitters and exogenous agonistic ligands. Bidirectional translational studies between human cases and model strains based on this experimental paradigm are presently aimed at clarifying the tau pathogenesis, and would be expanded to analyses of disrupted calcium homeostasis and mitochondrial impairments. Since reciprocal causalities among the key processes have indicated an architectural interchangeability between cascade and network connections as an etiological representation, longitudinal imaging assays with manifold probes covering the cascade from top to bottom virtually delineate the network dynamics continuously altering in the course of the disease and its treatment, and therefore expedite the evaluation and optimization of therapeutic strategies intended for suppressing the neurodegenerative pathway over its full length.     

Ambiguity in Social Network Data for Presence,Sensitive-Attribute,Degree and Relationship Privacy Protection

Maintaining privacy in network data publishing is a major challenge. This is because known characteristics of individuals can be used to extract new information about them. Recently, researchers have developed privacy methods based on k-anonymity and l-diversity to prevent re-identification or sensitive label disclosure through certain structural information. However, most of these studies have considered only structural information and have been developed for undirected networks. Furthermore, most existing approaches rely on generalization and node clustering so may entail significant information loss as all properties of all members of each group are generalized to the same value. In this paper, we introduce a framework for protecting sensitive attribute, degree (the number of connected entities), and relationships, as well as the presence of individuals in directed social network data whose nodes contain attributes. First, we define a privacy model that specifies privacy requirements for the above private information. Then, we introduce the technique of Ambiguity in Social Network data (ASN) based on anatomy, which specifies how to publish social network data. To employ ASN, individuals are partitioned into groups. Then, ASN publishes exact values of properties of individuals of each group with common group ID in several tables. The lossy join of those tables based on group ID injects uncertainty to reconstruct the original network. We also show how to measure different privacy requirements in ASN. Simulation results on real and synthetic datasets demonstrate that our framework, which protects from four types of private information disclosure, preserves data utility in tabular, topological and spectrum aspects of networks at a satisfactory level.     

Social Network Analysis and Mining to Monitor and Identify Problems with Large-Scale Information and Communication Technology Interventions

The published literature reveals several arguments concerning the strategic importance of information and communication technology (ICT) interventions for developing countries where the digital divide is a challenge. Large-scale ICT interventions can be an option for countries whose regions, both urban and rural, present a high number of digitally excluded people. Our goal was to monitor and identify problems in interventions aimed at certification for a large number of participants in different geographical regions. Our case study is the training at the Telecentros.BR, a program created in Brazil to install telecenters and certify individuals to use ICT resources. We propose an approach that applies social network analysis and mining techniques to data collected from Telecentros.BR dataset and from the socioeconomics and telecommunications infrastructure indicators of the participants’ municipalities. We found that (i) the analysis of interactions in different time periods reflects the objectives of each phase of training, highlighting the increased density in the phase in which participants develop and disseminate their projects; (ii) analysis according to the roles of participants (i.e., tutors or community members) reveals that the interactions were influenced by the center (or region) to which the participant belongs (that is, a community contained mainly members of the same region and always with the presence of tutors, contradicting expectations of the training project, which aimed for intense collaboration of the participants, regardless of the geographic region); (iii) the social network of participants influences the success of the training: that is, given evidence that the degree of the community member is in the highest range, the probability of this individual concluding the training is 0.689; (iv) the North region presented the lowest probability of participant certification, whereas the Northeast, which served municipalities with similar characteristics, presented high probability of certification, associated with the highest degree in social networking platform.     

How Does Dog-Walking Influence Perceptions of Health and Wellbeing in Healthy Adults? A Qualitative Dog-Walk-Along Study

The aim of this study was to explore perceptions of health and wellbeing related to dog-walking in healthy adults. Ten self-reported healthy adult dog-walkers took part in one dog-walk-along interview, and nine of the 10 participants also attended one follow-up participatory analysis session. All interviews took place within the metropolitan area of Dunedin, Aotearoa/New Zealand. Dog-walk-along interviews were participant-led, audio and video recorded, and transcribed. Participants’ input into the inductive generation of themes was sought during the follow-up participatory analysis session. Three themes were identified: participants had an “Emotional Connection” with their dog(s); dog-walking led to “Healthy Interactions” (social and environmental interactions); and participants had come to value dog-walking for its aggregate benefits for Psychological Wellbeing. Our dog-walk-along interviews have revealed novel insights about how the emotional connection between human and dog strengthens intrinsic motivation to exercise (through dog-walking) whilst concurrently enhancing human psychological wellbeing through the act of giving something that gives pleasure to the dog. Through the combined methods of dog-walk-along interviews and participatory analysis sessions, we were able to identify negative influences on human psychological well-being that included anxiety about both dog behaviors and social encounters with other humans. Positive and negative aspects of dog-walking that we identified can be used by health promotion advocates to refine dog-walking related advice, programs, and initiatives.     

Network analysis as a tool to understand social development in spider monkeys

The emerging field of network science has demonstrated that an individual's connectedness within their social network has cascading effects to other dimensions of life. Like humans, spider monkeys live in societies with high fission–fusion dynamics, and are remarkably social. Social network analysis (SNA) is a powerful tool for quantifying connections that may vary as a function of initiating or receiving social behaviors, which has been described as shifting social roles. In primatology, the SNA literature is dominated by work in catarrhines, and has yet to be applied to the study of development in a platyrrhine model. Here, SNA was utilized in combination with R-Index social role calculation to characterize social interaction patterns in juvenile and adult Colombian spider monkeys (Ateles fusciceps rufiventris). Connections were examined across five behaviors: embrace, face-embrace, grooming, agonism, and tail-wrapping from 186 hr of observation and four network metrics. Mann–Whitney U tests were utilized to determine differences between adult and juvenile social network patterns for each behavior. Face-embrace emerged as the behavior with different network patterns for adults and juveniles for every network metric. With regard to social role, juveniles were receivers, not initiators, for embrace, face-embrace, and grooming (ps < .05). Network and social role differences are discussed in light of social development and aspects of the different behaviors.     

Influence of Reciprocal Links in Social Networks

How does reciprocal links affect the function of real social network? Does reciprocal link and non-reciprocal link play the same role? Previous researches haven''t displayed a clear picture to us until now according to the best of our knowledge. Motivated by this, in this paper, we empirically study the influence of reciprocal links in two representative real datasets, Sina Weibo and Douban. Our results demonstrate that the reciprocal links play a more important role than non-reciprocal ones in information diffusion process. In particular, not only coverage but also the speed of the information diffusion can be significantly enhanced by considering the reciprocal effect. We give some possible explanations from the perspectives of network connectivity and efficiency. This work may shed some light on the in-depth understanding and application of the reciprocal effect in directed online social networks.     

The prevalence of overweight and obesity among Croatian hospitalized coronary heart disease patients

The aim of this article was to investigate the prevalence of overweight and obesity using selected anthropometric variables in a sample of hospitalized coronary heart disease (CHD) patients in Croatia (N = 1,298). Prevalence of overweight and obesity in surveyed patient population was high: 48.2% of participants were overweight and 28.6% were obese according to their body mass index; measured through waist-to-hip ratio 54.5% of participants were centrally obese. These data on prevalences of overweight, obesity and central obesity show that although there are some reassuring trends, there is still considerable amount of work to be done if the prevalence of this cardiovascular risk factor is to be reduced further among Croatian CHD patients. While the prevalence of obesity seems to be on the decline, the prevalence of overweight is rising, which may be just an early warning sign of an incoming wave of obesity epidemic in future years.