Social Network Data Research Articles

Estimating the incidence of Venezuelan migration and other socioeconomic factors on urban growth in Colombia.

This study investigates the intricate relationship between Venezuelan migration and urban growth in Colombia from 2018 to 2021. The study employs remote sensing data and social network metrics to uncover migration patterns and their impact on urban expansion. The methodology consists of three stages. Firstly, nighttime satellite imagery is used to analyze year-over-year urban growth in Colombia. Secondly, social network data estimates Venezuelan migration, overcoming challenges of underreporting and informal border crossings. Lastly, an econometric analysis explores the quantitative link between Venezuelan migration and urban growth, integrating socioeconomic variables to address endogeneity. The findings reveal the complex interplay of Venezuelan migration, socioeconomic factors, and urban growth. The study outlines remote sensing analysis, introducing the Anthropogenic Footprint Expansion Index (AFEI) to quantify urban growth. Facebook API data estimates migration trends and explores socioeconomic impacts on urban expansion. The analysis uncovers migration, poverty, aging, and urban population proportion as key factors affecting Colombia's urban landscape. Furthermore, the research underscores how Venezuelan migration affected short-term urban expansion pre- and post-COVID-19. Migration had a notable effect before the pandemic, but this influence waned afterward. The study highlights migration's short-term nature and emphasizes age demographics' role in medium-term dynamics.

Multi-view contrastive learning with virtual social group influence for social recommendation

Social recommendation systems leverage user–item interaction and user–user social network data to model user preferences and provide recommendations. Previous research has shown that capturing the influence of high-order neighbors and social groups in social networks can help model user preferences. However, challenges still exist in identifying important high-order neighbors from many candidates and modeling social group influence without direct information on social groups. This paper proposes a Multi-view Contrastive Learning with Social Group Influence (MCLSGI) method to address these challenges. Our approach uses a graph walk method to identify users’ virtual social groups and a GNN-based framework to model user preferences in different views, capturing the influence of direct and high-order neighbors as well as social groups. We also adopt multi-view contrastive learning to fuse users’ preferences in different views. We conducted experiments on two real-world datasets (Ciao and Epinions) to validate our method’s effectiveness. Compared to the best baseline, we improved by 1.39% and 1.21% with MAE, and 1.79% and 0.96% with RMSE.

Spatial Urban Analysis of Attractions in Riyadh City Using Location-Based Social Network Data (LBSNs)

Spatial Urban Analysis of Attractions in Riyadh City Using Location-Based Social Network Data (LBSNs)

Spatial Urban Analysis of Attractions in Riyadh City Using Location-Based Social Network Data (LBSNs)

With the proliferation of technology and the advancement of mobile devices and the internet, the use of Location-Based Social Networks (LBSNs) has become popular in urban communities. That providing a rich source of data for analyzing social and urban behaviour in cities. Which allows for the tracking of urban interaction patterns and human activity to understand visitor behaviour in these places. This paper aims to identify spatial patterns and the distribution the attractions using LBSNs. Density estimation, spatial direction monitoring, and identification of spatial correlation of attractions within Riyadh city were conducted. Heat maps were utilized to reveal the relative density of attraction spots in Riyadh. On the other hand, the distribution trend was analysed using the SDE tool. Moreover, Moran's I index was employed to measure the spatial correlation of attractions within the city. The results of directional and density analysis of attractions indicate a tendency for attraction spots to concentrate in the city centre. Additionally, the results reflect variations between types of places across the studied categories, including parks, shopping centres, entertainment venues, and tourist attractions. The Moran index indicates spatial correlation for the attractions. Overall, LBSNs can be considered an additional and reliable source of big data for social media to monitor urban places and interactions within the city. Future integration of temporal analysis with spatial analysis can be pursued to identify spatiotemporal changes over time. Key words: GIS, Spatial Analysis, LBSNs, Google maps.

Protection of children’s personal data in social networks in the context of the right to be forgotten

The article is devoted to a legal analysis of the protection of the confidentiality of children’s personal data on social networks in the context of the right to be forgotten and the General Data Protection Regulation. It is established that most social networks have their own usage rules, but their priority is to attract more users and company profit rather than the security of users’ personal data. Legal mechanisms for the implementation and protection of human rights in the digital environment are in the process of formation and improvement. It is proven that the application of the right to be forgotten has become one of the forms of personal data protection in social networks and on the Internet in general. Although initially the right to erasure emerged only at the level of the judicial practice of the Court of Justice of the EU, an effective mechanism has now been created to protect the individual’s right to delete outdated information about themselves, taking into account the balance between the right to privacy and the right to freedom of expression. Due to psychological immaturity and limited life experience, children and adolescents may casually disclose sensitive personal information, increasing the likelihood of abuse by both social networks and third parties. Therefore, minors receive special protection as vulnerable user categories according to the General Data Protection Regulation. It is argued that the incorporation of the right to be forgotten into EU legislation helps eliminate undesirable consequences of oversharing and restores minors’ ability to control their digital footprint. The terms of service of most social networks ensure additional protection of minors’ data and provide several options for deleting already disseminated information. Obstacles to the application of the right to be forgotten in social networks are identified, such as the lack of a developed mechanism for deleting photos or videos with minors published without the consent of the child or their parents by another user, the limited ability of users under 13 years old to request deletion without intermediaries, and the limited liability of social networks for the loss of information, data, and direct or indirect damages from such actions.

The correlation between independent edge and triangle degrees promote the explosive information spreading

In recent years, a novel phenomenon of explosive information spreading has been observed in the simplicial configuration model under the higher-order interactions, where a discontinuous transition exists. The model usually considers that the independent edge degree (i.e., the number of single edges attached to a node excluding those belonging to the triangles) and triangle degree (i.e., the number of triangles connected to a node) are uncorrelated. However, whether these degrees correlation exist in real networks and induce the explosive transition of information spreading at higher-order interactions is not very clear. Based on the real social networks data analysis, we find that the triangle degree increases with the independent edge degree approximatively as a power law, indicating a positive correlation between them. To understand how these correlations affect the spreading dynamics, we use an information-spreading model with higher-order interactions on synthetic networks and control the correlated strength of these degrees. Interestingly, our results reveal that the correlated structure would accelerate the information spreading with a fast-spreading speed. In addition, we uncover that the higher-order interactions will induce a discontinuous transition and that there is a bistable region with healthy and endemic states co-existing. Further, we discover that a highly correlated independent edge and triangle degrees will reduce the spreading threshold, which means the correlated structure promotes explosive information spreading. Finally, we utilize the microscopic Markov chain approach(MMCA) to explain the simulations. Our findings may shed some light on understanding the fast-spreading phenomenon of information in real life.

Loneliness, social support, and social networks: urban–rural variation and links to wellbeing in Scotland

Abstract Aim Social relationships are key public health priorities, with their relevance to wellbeing amplified in recent years. Relationships are embedded within the wider environment in which they occur; however, we lack understanding of whether, and how, places may affect social relationships. This study utilises an urban vs rural framework to examine variation in three specific aspects of relationships (loneliness, social support, and social networks), and their links to wellbeing. Subjects and method Cross-sectional social network data, collected in Scotland in 2021 (N = 191), was used. Bivariate comparisons tested for differences in loneliness, social support, and social networks between urban and rural participants. Multivariable regression tested for associations between each construct and wellbeing, and interaction effects assessed differences in associations between the urban and rural locations. Results Urban participants had higher levels of loneliness and poorer wellbeing, even though reported levels of social support didn’t differ. After adjusting for sociodemographic characteristics, loneliness, social support, and perceived emotional closeness of social networks were all associated with levels of wellbeing. There was no evidence that these associations differed between participants in urban and rural areas. Conclusion Overall, findings highlight key place-based differences that inform the design of efforts to promote social connectivity and enhance wellbeing. Given that urban residents had lower wellbeing and higher levels of loneliness, coupled with evidence demonstrating the importance of close, supportive social relationships, intervention efforts that promote social connectivity in urban environments are particularly needed.

Learning Time Slot Preferences via Mobility Tree for Next POI Recommendation

Next Point-of-Interests (POIs) recommendation task aims to provide a dynamic ranking of POIs based on users' current check-in trajectories. The recommendation performance of this task is contingent upon a comprehensive understanding of users' personalized behavioral patterns through Location-based Social Networks (LBSNs) data. While prior studies have adeptly captured sequential patterns and transitional relationships within users' check-in trajectories, a noticeable gap persists in devising a mechanism for discerning specialized behavioral patterns during distinct time slots, such as noon, afternoon, or evening. In this paper, we introduce an innovative data structure termed the ``Mobility Tree'', tailored for hierarchically describing users' check-in records. The Mobility Tree encompasses multi-granularity time slot nodes to learn user preferences across varying temporal periods. Meanwhile, we propose the Mobility Tree Network (MTNet), a multitask framework for personalized preference learning based on Mobility Trees. We develop a four-step node interaction operation to propagate feature information from the leaf nodes to the root node. Additionally, we adopt a multitask training strategy to push the model towards learning a robust representation. The comprehensive experimental results demonstrate the superiority of MTNet over eleven state-of-the-art next POI recommendation models across three real-world LBSN datasets, substantiating the efficacy of time slot preference learning facilitated by Mobility Tree.

Community Detection Methods Based on Exploiting Attributes and Interactions on Social Networks: A Survey and Future Directions

In recent years, community detection in social network analysis has been a hot research topic that has attracted much more attention in the scientific community and a large number of articles have already been published in the literature. Community detection problems focus on determining the subgraphs of the network that have dense edges and whose nodes are expected to have similar features. Community detection has significant applications in different domains of social network analysis such as sociology, business, criminal detection and recommendation systems. The computational complexity is hampered by the huge size and the dynamic nature of social networks. This paper introduces a comprehensive survey of community detection methods in social networks based on mining attributes and interactions of users. We divide the community detection approaches into three basic categories, namely (1) node content-based approaches, (2) edge content-based approaches, and (3) node- and edge content-based approaches. We also discuss in detail the main idea of each method that is suitable for social network data. Furthermore, to promote the study of community detection, we introduce the benchmark datasets from available sources. Last, we present further research directions as well as some open challenges for the community detection problem in social networks.

Understanding the Spatial Distribution of Ecotourism in Indonesia and Its Relevance to the Protected Landscape

Ecotourism, a dynamic force in global tourism, holds promise for conserving the environment while ensuring benefits for local economies. In this study, we developed an ecotourism distribution map of Indonesia. We utilized location-based social networks (LSBNs) data derived from Google Maps API to map 172 ecotourism sites in Indonesia. Furthermore, we investigated the distribution patterns of ecotourism within Indonesia’s protected landscapes and ecoregions. The factors that influenced ecotourism distribution in the region were analyzed using the MaxEnt model (because of its application for presence-only data). The key findings revealed that ecotourism sites are predominantly distributed across national parks and protected forest areas, and generally consist of mountainous and hilly terrain according to the ecoregion types. The MaxEnt model results indicated that population density was the most influential factor in ecotourism distribution. The significance of our study lies in its methodologies and results, which offered novel approaches to nationwide mapping and addressed the lack of an ecotourism site map of Indonesia. Notably, the proposed model can be customized for other regions with limited ecotourism data; thus, our study can serve as a foundation for future interdisciplinary studies on ecotourism, sustainability, and landscape planning.

Using social networking evidence to examine the impact of environmental factors on social Followings: An innovative Machine learning method

Social media platforms have changed the way of communication. They are making the world more connected and provide information more quickly, which has resulted in beneficial and harmful effects on society. Analyzing data from multiple social media platforms to spot patterns and trends in user behavior in response to environmental changes is part of using social networking evidence to investigate the effects of environmental variables.The infinite collection of personal data generates privacy issues and ethical questions about data security and utilization.We present a novel framework called meta-heuristic atom search optimized dynamic artificial neural network (MASO-DANN) method for examining the impact of environmental factors using social networking evidence and the complementary abilities between MASO as well as DANN. The study aims to determine the connections between the dynamics of users' social relationships and involvement by examining the data from various online social platforms. We collected a Twitter dataset and preprocessed the data using the stop word removal preprocessing method.With the use of MASO-DANN, extensive social networking data analysis has been rendered achievable, which offers an exhaustive overview of the complex dynamics influencing online communities. The result findings examined various metrics, such as accuracy (95.07%), precision (89%), recall (88%), F1-score (90%) and RMSE (1.6).These metrics demonstrate the robustness along with the dependability of MASO-DANN and its capacity to provide more accurate predictions that evaluate the effects of environmental factors based on social networking evidence.

Distance lending & social connectedness

Using Facebook’s social network data for the US counties, we examine whether social connectedness reduces the informational disadvantage in lending to small businesses at a distance. We find that for a given distance, there is a pecking order of lending. Banks first lend to more socially connected counties, and later, banks expand credit to socially less connected areas. The probability of loan charge-off decreases in social connectedness and more so for the loans originated by small banks. In the cross-section, the positive effect of social connectedness on loan performance is higher for the loans originated by out of state banks. These findings suggest that loan officers get valuable information through their social networks.

A corpus-based real-time text classification and tagging approach for social data

With the rapid accumulation of large amounts of user-generated content through social media, social data reuse and integration have gained increasing attention recently. This has made it almost obsolete for software applications to collect, store, and work with their own data stored on local servers. While, with the provision of Application Programming Interfaces from the leading social networking sites, data acquisition and integration has become possible, the meaningful usage of such unstructured, non-uniform, and incoherent data collections needs special procedures of data summarization, understanding, and visualization. One particular aspect in this regard that needs special attention is the procedures for data (text snippets in the form of social media posts) categorization and concept tagging to filter out the relevant and most suitable data for the particular audience and for the particular purpose. In this regard, we propose a corpus-based approach for searching and successively categorizing and tagging the social data with relevant concepts in real time. The proposed approach is capable of addressing the semantical and morphological similarities, as well as domain-specific vocabularies of query strings and tagged concepts. We demonstrate the feasibility and application of our proposed approach in a web-based tool that allows searching Facebook posts and provides search results together with a concept map for further navigation, filtering, and refining of search results. The tool has been evaluated by performing multiple search queries, and resultant concept maps and annotated texts are analyzed in terms of their precision. The approach is thereby found effective in achieving its stated goal of classifying text snippets in real time.

Actors participation and power relations of REDD+ implementation in Bale Eco Region, Ethiopia

ABSTRACT The study focuses on actor participation and power dynamics in the REDD+ project within the Bale eco-region, a vital facet of global climate policy for sustainable forest resource management. The Forest Carbon Partnership Facility (FCPF) of the World Bank supports developing countries in preparing for international REDD+ policy implementation, emphasizing the necessity of effective actor participation. The study uses text documents, Socio metric survey, and interviews with project actors and participants. This study employs both qualitative framework and social network analysis (SNA). While SNA looks at power dynamics and interactions between actors, the qualitative framework allows for a comprehensive evaluation of actor participation. The social network data was analyzed using an open source software called NodeXL. Results show that Bale eco-region REDD+ involves actors from different groups. Actors contribute to national strategies, funding, capacity building, and knowledge sharing. However, local communities have limited involvement in decision-making, lack knowledge sharing and capacity building. This hinders the implementation process and contributes to unequal power dynamics and lack the comprehensive input needed for effective and tailored strategies in the region. Calculated centrality values highlight a strong alliance among Ethiopia's national government entity overseeing REDD+ Environment, Forest and Climate Change Commission (EFCCC), and the regional entities Oromiya Forest and Wildlife Enterprise (OFWI), as well as Farm Africa, indicating significant institutional power across national and local levels. EFCCC holds influence in decision-making and strategy development, while OFWI wields substantial regional institutional power in information control and collaboration. Thus, climate policies must prioritize involvement of local communities in decision-making, capacity development, and resource accessibility. This helps to tackle power imbalances through collaborative governance structures. It also enables stakeholders to actively manage forests through imparting skills, knowledge and share benefits equally.

Applying explainable artificial intelligence methods to models for diagnosing personal traits and cognitive abilities by social network data

This study utilizes advanced artificial intelligence techniques to analyze the social media behavior of 1358 users on VK, the largest Russian online social networking service. The analysis comprises 753,252 posts and reposts, combined with Big Five personality traits test results, as well as assessments of verbal and fluid intelligence. The objective of this research is to understand the manifestation of psychological attributes in social media users' behavior and determine their implications on user-interaction models. We employ the integrated gradients method to identify the most influential feature groups. The partial dependence plot technique aids in understanding how these features function across varying severity degrees of the predicted trait. To evaluate feature stability within the models, we cluster calculated Shapley values. Our findings suggest that the emotional tone (joy, surprise, anger, fear) of posts significantly influences the prediction of three personality traits: Extraversion, Agreeableness, and Openness to Experience. Additionally, user social engagement metrics (such as friend count, subscribers, likes, views, and comments) correlate directly with the predicted level of Logical thinking. We also observe a trend towards provocative and socially reprehensible content among users with high Neuroticism levels. The theme of religion demonstrates a multidirectional relationship with Consciousness and Agreeableness. Further findings, including an analysis of post frequency and key text characteristics, are also discussed, contributing to our understanding of the complex interplay between social media behavior and psychological traits. The study proposes a transition from the analysis of correlations between psychological (cognitive) traits to the analysis of indicators of behavior in a social network that are significant for diagnostic models of the corresponding traits.

Mining Utilities Itemsets based on social network

Mining utility item sets based on social network data involves extracting meaningful patterns and associations from user interactions. In this paper, the process begins by collecting and preprocessing data from platforms like Facebook, Twitter, or LinkedIn. Utility measures are defined based on frequency of occurrence, user engagement metrics, or other domain-specific criteria. Itemsets that meet certain thresholds are identified using techniques like frequent itemset mining or advanced algorithms like Apriori or FP-growth. Additional analyses, such as association rule mining, uncover relationships between different itemsets or user segments, providing valuable insights for personalized recommendations, targeted advertising, and decision-making processes.

Deep Attentional Implanted Graph Clustering Algorithm for the Visualization and Analysis of Social Networks

As the user base expands, social network data becomes more intricate, making analyzing the interconnections between various entities challenging. Various graph visualization technologies are employed to analyze extensive and intricate network data. Network graphs inherently possess intricacy and may have overlapping elements. Graph clustering is a basic endeavor that aims to identify communities or groupings inside networks. Recent research has mostly concentrated on developing deep learning techniques to acquire a concise representation of graphs, which is then utilized with traditional clustering methods such as k-means or spectral clustering techniques. Multiplying these two-step architectures is challenging and sometimes results in unsatisfactory performance. This is mostly due to the lack of a goal-oriented graph encoding developed explicitly for the clustering job. This work introduces a novel Deep Learning (DL) method called Deep Attentional Implanted Graph Clustering (DAIGC), designed to achieve goal-oriented clustering. Our approach centers on associated graphs to thoroughly investigate both aspects of data in graphs. The proposed DAIGC technique utilizes a Graph Attention Autoencoder (GAA) to determine the significance of nearby nodes about a target node. This allows encoding a graph's topographical structure and node value into a concise representation. Based on this representation, an interior product decoder has been trained to rebuild the graph structure. The performance of the proposed approach has been evaluated on four distinct types and sizes of real-world intricate networks, varying in vertex count from 𝑁=102 𝑡𝑜 𝑁=107. The performance of the suggested methods is evaluated by comparing them with two established and commonly used graph clustering techniques. The testing findings demonstrate the effectiveness of the proposed method in terms of processing speed and visualization compared to the state-of-the-art algorithms.

Alcohol consumption as a socially contagious phenomenon in the Framingham Heart Study social network

We use longitudinal social network data from the Framingham Heart Study to examine the extent to which alcohol consumption is influenced by the network structure. We assess the spread of alcohol use in a three-state SIS-type model, classifying individuals as abstainers, moderate drinkers, and heavy drinkers. We find that the use of three-states improves on the more canonical two-state classification, as the data show that all three states are highly stable and have different social dynamics. We show that when modelling the spread of alcohol use, it is important to model the topology of social interactions by incorporating the network structure. The population is not homogeneously mixed, and clustering is high with abstainers and heavy drinkers. We find that both abstainers and heavy drinkers have a strong influence on their social environment; for every heavy drinker and abstainer connection, the probability of a moderate drinker adopting their drinking behaviour increases by 40%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$40\\%$$\\end{document} and 18%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$18\\%$$\\end{document}, respectively. We also find that abstinent connections have a significant positive effect on heavy drinkers quitting drinking. Using simulations, we find that while both are effective, increasing the influence of abstainers appears to be the more effective intervention compared to reducing the influence of heavy drinkers.

Trial protocol for the Building Resilience through Socio-Emotional Training (ReSET) programme: a cluster randomised controlled trial of a new transdiagnostic preventative intervention for adolescents

BackgroundAdolescence is a period of heightened vulnerability to developing mental health problems, and rates of mental health disorder in this age group have increased in the last decade. Preventing mental health problems developing before they become entrenched, particularly in adolescents who are at high risk, is an important research and clinical target. Here, we report the protocol for the trial of the ‘Building Resilience through Socioemotional Training’ (ReSET) intervention. ReSET is a new, preventative intervention that incorporates individual-based emotional training techniques and group-based social and communication skills training. We take a transdiagnostic approach, focusing on emotion processing and social mechanisms implicated in the onset and maintenance of various forms of psychopathology.MethodsA cluster randomised allocation design is adopted with randomisation at the school year level. Five-hundred and forty adolescents (aged 12–14) will be randomised to either receive the intervention or not (passive control). The intervention is comprised of weekly sessions over an 8-week period, supplemented by two individual sessions. The primary outcomes, psychopathology symptoms and mental wellbeing, will be assessed pre- and post-intervention, and at a 1-year follow-up. Secondary outcomes are task-based assessments of emotion processing, social network data based on peer nominations, and subjective ratings of social relationships. These measures will be taken at baseline, post-intervention and 1-year follow-up. A subgroup of participants and stakeholders will be invited to take part in focus groups to assess the acceptability of the intervention.DiscussionThis project adopts a theory-based approach to the development of a new intervention designed to target the close connections between young people’s emotions and their interpersonal relationships. By embedding the intervention within a school setting and using a cluster-randomised design, we aim to develop and test a feasible, scalable intervention to prevent the onset of psychopathology in adolescence.Trial registrationISRCTN88585916. Trial registration date: 20/04/2023.

Micro-Macro Mediation Analysis in Social Networks

Mediation analysis is increasingly used in the social sciences. Extension to social network data, however, has proved difficult because statistical network models are formulated at a lower level of analysis (the dyad) than many outcomes of interest. This study introduces a general approach for micro-macro mediation analysis in social networks. The author defines the average mediated micro effect (AMME) as the indirect effect of a network selection process on an individual, group, or organizational outcome through its effect on an intervening network variable. The author shows that the AMME can be nonparametrically identified using a wide range of common statistical network and regression modeling strategies under the assumption of conditional independence among multiple mediators. Nonparametric and parametric algorithms are introduced to generically estimate the AMME in a multitude of research designs. The author illustrates the utility of the method with an applied example using cross-sectional National Longitudinal Study of Adolescent to Adult Health data to examine the friendship selection mechanisms that indirectly shape adolescent school performance through their effect on network structure.