Showing papers on "Social network published in 2020"

Social disconnectedness, perceived isolation, and symptoms of depression and anxiety among older Americans (NSHAP): a longitudinal mediation analysis

Abstract: Summary Background Research indicates that social isolation and loneliness increase the risk of mental disorders, but less is known about the distinct contributions of different aspects of isolation. We aimed to distinguish the pathways through which social disconnectedness (eg, small social network, infrequent social interaction) and perceptions of social isolation (eg, loneliness, perceived lack of support) contribute to anxiety and depression symptom severity in community-residing older adults aged 57–85 years at baseline. Methods We did a longitudinal mediation analysis with data from the National Social Life, Health, and Aging Project (NSHAP). The study included individuals from the USA born between 1920 and 1947. Validated measures on social disconnectedness, perceived isolation, and depression and anxiety symptoms were used. Structural equation modelling was used to construct complete longitudinal path models. Findings Using data from 3005 adults aged 57–85 years, we identified two significant longitudinal mediation patterns with symptoms of depression, and two with anxiety symptoms. Overall, social disconnectedness predicted higher subsequent perceived isolation (β=0·09; p Interpretation Social network structure and function are strongly intertwined with anxiety and depression symptoms in the general population of older adults. Public health initiatives could reduce perceived isolation by facilitating social network integration and participation in community activities, thereby protecting against the development of affective disorders. Funding Nordea-fonden.

Mental health during the COVID-19 pandemic: Effects of stay-at-home policies, social distancing behavior, and social resources.

Abstract: Social distancing is the most visible public health response to the COVID-19 pandemic, but its implications for mental health are unknown. In a nationwide online sample of 435 U.S. adults, conducted in March 2020 as the pandemic accelerated and states implemented stay-at-home orders, we examined whether stay-at-home orders and individuals' personal distancing behavior were associated with symptoms of depression, generalized anxiety disorder (GAD), intrusive thoughts, insomnia, and acute stress. Stay-at-home order status and personal distancing were independently associated with higher symptoms, beyond protective effects of available social resources (social support and social network size). A subsample of 118 participants who had completed symptom measures earlier in the outbreak (February 2020) showed increases in depression and GAD between February and March, and personal distancing behavior was associated with these increases. Findings suggest that there are negative mental health correlates of social distancing, which should be addressed in research, policy, and clinical approaches to the COVID-19 pandemic.

Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world.

Abstract: Social distancing and isolation have been widely introduced to counter the COVID-19 pandemic. Adverse social, psychological and economic consequences of a complete or near-complete lockdown demand the development of more moderate contact-reduction policies. Adopting a social network approach, we evaluate the effectiveness of three distancing strategies designed to keep the curve flat and aid compliance in a post-lockdown world. These are: limiting interaction to a few repeated contacts akin to forming social bubbles; seeking similarity across contacts; and strengthening communities via triadic strategies. We simulate stochastic infection curves incorporating core elements from infection models, ideal-type social network models and statistical relational event models. We demonstrate that a strategic social network-based reduction of contact strongly enhances the effectiveness of social distancing measures while keeping risks lower. We provide scientific evidence for effective social distancing that can be applied in public health messaging and that can mitigate negative consequences of social isolation.

Consensus reaching for social network group decision making by considering leadership and bounded confidence

Abstract: With the rapid development of information, communication and techniques, social network group decision making problems which allow information exchange and communication among experts are more and more common in recent years. How to use social relationships generated by social networks to promote consensus among experts has been becoming a hot topic in the field of group decision making. In this paper, we consider a new type of group decision making problems in which experts will provide his/her interval fuzzy preference relations over alternatives under social network environment and propose a new model to help experts reach consensus. In the proposed model, we first define the individual consensus measure and the group consensus measure, and then use a network partition algorithm to detect sub-networks of experts, based on which the leadership of experts can be identified. Afterwards, by considering the leadership and the bounded confidence levels of experts, a new feedback mechanism which can provide acceptable advice to experts who need to modify their opinions is devised and a consensus reaching algorithm is further developed. To demonstrate the performance of the proposed consensus model and algorithm, a hypothetical application and some simulation analysis are provided eventually.

FakeDetector: Effective Fake News Detection with Deep Diffusive Neural Network

Abstract: In recent years, due to the booming development of online social networks, fake news for various commercial and political purposes has been appearing in large numbers and widespread in the online world. With deceptive words, online social network users can get infected by these online fake news easily, which has brought about tremendous effects on the offline society already. An important goal in improving the trustworthiness of information in online social networks is to identify the fake news timely. This paper aims at investigating the principles, methodologies and algorithms for detecting fake news articles, creators and subjects from online social networks and evaluating the corresponding performance. This paper addresses the challenges introduced by the unknown characteristics of fake news and diverse connections among news articles, creators and subjects. This paper introduces a novel gated graph neural network, namely FAKEDETECTOR. Based on a set of explicit and latent features extracted from the textual information, FAKEDETECTOR builds a deep diffusive network model to learn the representations of news articles, creators and subjects simultaneously. Extensive experiments have been done on a real-world fake news dataset to compare FAKEDETECTOR with several state-of-the-art models, and the experimental results are provided in the full-version of this paper at [13].

Too good to be true, too good not to share: the social utility of fake news

Abstract: While fake news has been widely reviled as an attack on democracy, less has been written about its threat to interpersonal relationships. Social networks have become increasingly popular for sharin...

Interdependence and the cost of uncoordinated responses to COVID-19.

Abstract: Social distancing is the core policy response to coronavirus disease 2019 (COVID-19). But, as federal, state and local governments begin opening businesses and relaxing shelter-in-place orders worldwide, we lack quantitative evidence on how policies in one region affect mobility and social distancing in other regions and the consequences of uncoordinated regional policies adopted in the presence of such spillovers. To investigate this concern, we combined daily, county-level data on shelter-in-place policies with movement data from over 27 million mobile devices, social network connections among over 220 million Facebook users, daily temperature and precipitation data from 62,000 weather stations, and county-level census data on population demographics to estimate the geographic and social network spillovers created by regional policies across the United States. Our analysis shows that the contact patterns of people in a given region are significantly influenced by the policies and behaviors of people in other, sometimes distant, regions. When just one-third of a state's social and geographic peer states adopt shelter-in-place policies, it creates a reduction in mobility equal to the state's own policy decisions. These spillovers are mediated by peer travel and distancing behaviors in those states. A simple analytical model calibrated with our empirical estimates demonstrated that the "loss from anarchy" in uncoordinated state policies is increasing in the number of noncooperating states and the size of social and geographic spillovers. These results suggest a substantial cost of uncoordinated government responses to COVID-19 when people, ideas, and media move across borders.

Target-aware Holistic Influence Maximization in Spatial Social Networks

Abstract: Influence maximization has recently received significant attention for scheduling online campaigns or advertisements on social network platforms. However, most studies only focus on user influence via cyber interactions while ignoring their physical interactions which are also essential to gauge influence propagation. Additionally, targeted campaigns or advertisements have not received sufficient attention. To address these issues, we first devise a novel holistic influence diffusion model that takes into account both cyber and physical user interactions in an effective and practical way. Based on the new diffusion model, we formulate a new problem of holistic influence maximization, denoted as HIM query, for targeted advertisements in a spatial social network. The HIM query problem aims to find a minimum set of users whose holistic influence can cover all target users in the network, which belongs to a set covering problem. Since the HIM query problem is NP-hard, we develop a greedy baseline algorithm and then improve on this algorithm to reduce the computational cost. To deal with large networks, we also design a spatial-social index to maintain the social, spatial and textual information of users, as well as developing an index-based efficient solution. Finally, we conduct extensive experiments to validate the efficiency and effectiveness of the proposed diffusion model and developed algorithms.

Evolutionary dynamics of higher-order interactions in social networks

Abstract: We live and cooperate in networks. However, links in networks only allow for pairwise interactions, thus making the framework suitable for dyadic games, but not for games that are played in groups of more than two players. Here, we study the evolutionary dynamics of a public goods game in social systems with higher-order interactions. First, we show that the game on uniform hypergraphs corresponds to the replicator dynamics in the well-mixed limit, providing a formal theoretical foundation to study cooperation in networked groups. Secondly, we unveil how the presence of hubs and the coexistence of interactions in groups of different sizes affects the evolution of cooperation. Finally, we apply the proposed framework to extract the actual dependence of the synergy factor on the size of a group from real-world collaboration data in science and technology. Our work provides a way to implement informed actions to boost cooperation in social groups.

The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook

Abstract: We use aggregated data from Facebook to show that COVID-19 is more likely to spread between regions with stronger social network connections. Areas with more social ties to two early COVID-19 "hotspots" (Westchester County, NY, in the U.S. and Lodi province in Italy) generally had more confirmed COVID-19 cases by the end of March. These relationships hold after controlling for geographic distance to the hotspots as well as the population density and demographics of the regions. As the pandemic progressed in the U.S., a county's social proximity to recent COVID-19 cases and deaths predicts future outbreaks over and above physical proximity and demographics. In part due to its broad coverage, social connectedness data provides additional predictive power to measures based on smartphone location or online search data. These results suggest that data from online social networks can be useful to epidemiologists and others hoping to forecast the spread of communicable diseases such as COVID-19.

Why Don't We Agree? Evidence from a Social Network of Investors

Abstract: We study sources of investor disagreement using sentiment of investors from a social media investing platform, combined with information on the users' investment approaches (e.g., technical, fundamental). We examine how much of overall disagreement is driven by different information sets versus differential interpretation of information by studying disagreement within and across investment approaches. Overall disagreement is evenly split between both sources of disagreement, but within‐group disagreement is more tightly related to trading volume than cross‐group disagreement. Although both sources of disagreement are important, our findings suggest that information differences are more important for trading than differences across market approaches.

Use of Clustering Sentiments for Opinion Mining: An Experimental Analysis

Abstract: In E-commerce, the social media network analytics are in a key position for the extraction of a service or product information. Opinion mining is now the key ingredient for social network analytics. In the current study, we study opinion mining process in networks while different kinds of documents are dealt with that are opinionated and a formal discussion on the challenges for sentiment analysis via social networks is being updated [1]. The social network Twitter has now become a huge online platform which has millions of people with their opinions shared as a social activity [2]. One major concern is how to get reviews for products on the basis of its features. The application of the K-means algorithm on a dataset sample from twitter and twitter dataset which will be clustered by different opinions in the context of various product features and has been evaluated along with explanation by the hand of a machine learning tool.

Gandhipedia: A one-stop AI-enabled portal for browsing Gandhian literature, life-events and his social network

Abstract: We introduce an AI-enabled portal that presents an excellent visualization of Mahatma Gandhi's life events by constructing temporal and spatial social networks from the Gandhian literature. Applying an ensemble of methods drawn from NLTK, Polyglot and Spacy we extract the key persons and places that find mentions in Gandhi's written works. We visualize these entities and connections between them based on co-mentions within the same time frame as networks in an interactive web portal. The nodes in the network, when clicked, fire search queries about the entity and all the information about the entity presented in the corresponding book from which the network is constructed, are retrieved and presented back on the portal. Overall, this system can be used as a digital and user-friendly resource to study Gandhian literature.

Social network-based distancing strategies to flatten the COVID 19 curve in a post-lockdown world

Abstract: Social distancing and isolation have been introduced widely to counter the COVID-19 pandemic. However, more moderate contact reduction policies become desirable owing to adverse social, psychological, and economic consequences of a complete or near-complete lockdown. Adopting a social network approach, we evaluate the effectiveness of three targeted distancing strategies designed to 'keep the curve flat' and aid compliance in a post-lockdown world. These are limiting interaction to a few repeated contacts, seeking similarity across contacts, and strengthening communities via triadic strategies. We simulate stochastic infection curves that incorporate core elements from infection models, ideal-type social network models, and statistical relational event models. We demonstrate that strategic reduction of contact can strongly increase the efficiency of social distancing measures, introducing the possibility of allowing some social contact while keeping risks low. This approach provides nuanced insights to policy makers for effective social distancing that can mitigate negative consequences of social isolation.

Social determinants of adaptive and transformative responses to climate change

Abstract: To cope effectively with the impacts of climate change, people will need to change existing practices or behaviours within existing social-ecological systems (adaptation) or enact more fundamental changes that can alter dominant social-ecological relationships and create new systems or futures (transformation). Here we use multilevel network modelling to examine how different domains of adaptive capacity-assets, flexibility, organization, learning, socio-cognitive constructs and agency-are related to adaptive and transformative actions. We find evidence consistent with an influence process in which aspects of social organization (exposure to others in social networks) encourage both adaptive and transformative actions among Papua New Guinean islanders experiencing climate change impacts. Adaptive and transformative actions are also related to social-ecological network structures between people and ecological resources that enable learning and the internalization of ecological feedbacks. Agency is also key, yet we show that while perceived power may encourage adaptations, it may discourage more transformative actions. Multilevel network modelling shows that social network exposure promotes both adaptive and transformative responses to climate change among Papua New Guinean islanders. Different social-ecological network structures are associated with adaptation versus transformation.

Microbial transmission in animal social networks and the social microbiome.

Abstract: Host-associated microbiomes play an increasingly appreciated role in animal metabolism, immunity and health. The microbes in turn depend on their host for resources and can be transmitted across the host's social network. In this Perspective, we describe how animal social interactions and networks may provide channels for microbial transmission. We propose the 'social microbiome' as the microbial metacommunity of an animal social group. We then consider the various social and environmental forces that are likely to influence the social microbiome at multiple scales, including at the individual level, within social groups, between groups, within populations and species, and finally between species. Through our comprehensive discussion of the ways in which sociobiological and ecological factors may affect microbial transmission, we outline new research directions for the field.

A survey on influence maximization in a social network

Abstract: Given a social network with diffusion probabilities as edge weights and a positive integer k, which k nodes should be chosen for initial injection of information to maximize the influence in the network? This problem is popularly known as the Social Influence Maximization Problem (SIM Problem). This is an active area of research in computational social network analysis domain, since one and half decades or so. Due to its practical importance in various domains, such as viral marketing, target advertisement and personalized recommendation, the problem has been studied in different variants, and different solution methodologies have been proposed over the years. This paper presents a survey on the progress in and around SIM Problem. At last, it discusses current research trends and future research directions as well.

Age differences in reported social networks and well-being.

Abstract: Social networks can consist of close friends, family members, and neighbors as well as peripheral others. Studies of social networks and associations with well-being have mostly focused on age-restricted samples of older adults or specific geographic areas, thus limiting their generalizability. We analyzed 2 online surveys conducted with RAND's American Life Panel, a national adult life span sample recruited through multiple probability-based approaches. In Survey 1, 496 participants assessed the sizes of their social networks, including the number of close friends, family members, neighbors, and peripheral others. Of those, 287 rated their social satisfaction and well-being on Survey 2. Older participants reported smaller social networks, largely because of reporting fewer peripheral others. Yet older age was associated with better well-being. Although the reported number of close friends was unrelated to age, it was the main driver of well-being across the life span-even after accounting for the number of family members, neighbors, and peripheral others. However, well-being was more strongly related to social satisfaction than to the reported number of close friends-suggesting that it is the perception of relationship quality rather than the perception of relationship quantity that is relevant to reporting better well-being. We discuss implications for social network interventions that aim to promote well-being. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction

Abstract: Cross-platform account matching plays a significant role in social network analytics, and is beneficial for a wide range of applications. However, existing methods either heavily rely on high-quality user generated content (including user profiles) or suffer from data insufficiency problem if only focusing on network topology, which brings researchers into an insoluble dilemma of model selection. In this paper, to address this problem, we propose a novel framework that considers multi-level graph convolutions on both local network structure and hypergraph structure in a unified manner. The proposed method overcomes data insufficiency problem of existing work and does not necessarily rely on user demographic information. Moreover, to adapt the proposed method to be capable of handling large-scale social networks, we propose a two-phase space reconciliation mechanism to align the embedding spaces in both network partitioning based parallel training and account matching across different social networks. Extensive experiments have been conducted on two large-scale real-life social networks. The experimental results demonstrate that the proposed method outperforms the state-of-the-art models with a big margin.

Graph Convolutional Networks with Markov Random Field Reasoning for Social Spammer Detection

Abstract: The recent growth of social networking platforms also led to the emergence of social spammers, who overwhelm legitimate users with unwanted content. The existing social spammer detection methods can be characterized into two categories: features based ones and propagation-based ones. Features based methods mainly rely on matrix factorization using tweet text features, and regularization using social graphs is incorporated. However, these methods are fully supervised and can only utilize labeled part of social graphs, which fail to work in a real-world semi-supervised setting. The propagation-based methods primarily employ Markov Random Fields (MRFs) to capture human intuitions in user following relations, which cannot take advantages of rich text features. In this paper, we propose a novel social spammer detection model based on Graph Convolutional Networks (GCNs) that operate on directed social graphs by explicitly considering three types of neighbors. Furthermore, inspired by the propagation-based methods, we propose a MRF layer with refining effects to encapsulate these human insights in social relations, which can be formulated as a RNN through mean-field approximate inference, and stack on top of GCN layers to enable end-to-end training. We evaluate our proposed method on two real-world social network datasets, and the results demonstrate that our method outperforms the state-of-the-art approaches.