Social-Spatial Group Queries with Keywords

2022 ◽  
Vol 8 (1) ◽  
pp. 1-32
Author(s):  
Sajid Hasan Apon ◽  
Mohammed Eunus Ali ◽  
Bishwamittra Ghosh ◽  
Timos Sellis

Social networks with location enabling technologies, also known as geo-social networks, allow users to share their location-specific activities and preferences through check-ins. A user in such a geo-social network can be attributed to an associated location (spatial), her preferences as keywords (textual), and the connectivity (social) with her friends. The fusion of social, spatial, and textual data of a large number of users in these networks provide an interesting insight for finding meaningful geo-social groups of users supporting many real-life applications, including activity planning and recommendation systems. In this article, we introduce a novel query, namely, Top- k Flexible Socio-Spatial Keyword-aware Group Query (SSKGQ), which finds the best k groups of varying sizes around different points of interest (POIs), where the groups are ranked based on the social and textual cohesiveness among members and spatial closeness with the corresponding POI and the number of members in the group. We develop an efficient approach to solve the SSKGQ problem based on our theoretical upper bounds on distance, social connectivity, and textual similarity. We prove that the SSKGQ problem is NP-Hard and provide an approximate solution based on our derived relaxed bounds, which run much faster than the exact approach by sacrificing the group quality slightly. Our extensive experiments on real data sets show the effectiveness of our approaches in different real-life settings.

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Nauman Ali Khan ◽  
Sihai Zhang ◽  
Wuyang Zhou ◽  
Ahmad Almogren ◽  
Ikram Ud Din ◽  
...  

Stochastic Internet of Things (IoT)-based communication behavior of the progressing world is tremendously impacting social networks. The growth of social networks helps to quantify the effect on the Social Internet of Things (SIoT). Multiple existences of two persons at several geographical locations in different time frames hint to predict the social connection. We investigate the extent to which social ties between people can be inferred by critically reviewing the social networks. Our study used Chinese telecommunication-based anonymized caller data records (CDRs) and two openly available location-based social network data sets, Brightkite and Gowalla. Our research identified social ties based on mobile communication data and further exploits communication reasons based on geographical location. This paper presents an inference framework that predicts the missing ties as suspicious social connections using pipe and filter architecture-based inference framework. It highlights the secret relationship of users, which does not exist in real data. The proposed framework consists of two major parts. Firstly, users’ cooccurrence based on the mutual location in a specific time frame is computed and inferred as social ties. Results are investigated based upon the cooccurrence count, the gap time threshold values, and mutual friend count values. Secondly, the detail about direct connections is collected and cross-related to the inferred results using Precision and Recall evaluation measures. In the later part of the research, we examine the false-positive results methodically by studying the human cooccurrence patterns to identify hidden relationships using a social activity. The outcomes indicate that the proposed approach achieves comprehensive results that further support the theory of suspicious ties.


Author(s):  
Dimitrios Rafailidis ◽  
Alexandros Nanopoulos ◽  
Yannis Manolopoulos

In popular music information retrieval systems, users have the opportunity to tag musical objects to express their personal preferences, thus providing valuable insights about the formulation of user groups/communities. In this article, the authors focus on the analysis of social tagging data to reveal coherent groups characterized by their users, tags and music objects (e.g., songs and artists), which allows for the expression of discovered groups in a multi-aspect way. For each group, this study reveals the most prominent users, tags, and music objects using a generalization of the popular web-ranking concept in the social data domain. Experimenting with real data, the authors’ results show that each Tag-Aware group corresponds to a specific music topic, and additionally, a three way ranking analysis is performed inside each group. Building Tag-Aware groups is crucial to offer ways to add structure in the unstructured nature of tags.


Author(s):  
Вероника Викторовна Катермина ◽  
Анна Александровна Гнедаш ◽  
Мария Витальевна Николаева

В статье приводятся результаты комплексного анализа лингвистических паттернов коммуникации топовых российских журналистов в официальных аккаунтах социальных платформ ВКонтакте, Facebook, Instagram, Twitter. Целью данной статьи является изучение лингвистических паттернов, продуцируемых топовыми журналистами в своих онлайн-аккаунтах, способных задавать векторы восприятия политического контента, создаваемого главными лидерами государств, и приводящих к трансформации дискурсивных полей как в онлайн-, так и в офлайн-пространстве. Среднестатистический россиянин тратит почти половину дня на онлайн-взаимодействие, почти 50 % этого времени приходится на популярные социальные медиа, в том числе интернет-серфинг в среде официальных аккаунтов топовых журналистов. Потребление данных паттернов рядовыми пользователями / читателями, находящимися под «силовым» влиянием дискурсивного поля, становится определяющим фактором в процессе выработки и принятия индивидуальных / коллективных решений, реализация которых формирует то или иное социальное действие как в онлайн-, так и в офлайн-пространстве. Согласно данным мониторинга социальных медиа и СМИ компанией «Медиалогия», нами были выбраны аккаунты Алексея Венедиктова, Владимира Соловьева, Владимира Познера, Маргариты Симоньян и Ксении Собчак в ВКонтакте, Facebook, Instagram, Twitter. Эмпирической базой (дата-сеты) стали все посты, комментарии и ветки дискуссий, отражающие реакцию данных журналистов и общественности на Послание Президента РФ В. В. Путина Федеральному Собранию РФ от 15 января 2020 г. Дата-сеты были получены машинным методом сплошной выборки и подвергнуты комплексному анализу, включившему сетевой, лингводискурсивный, фолксономический анализ. В результате проведенного исследования были сделаны выводы о том, какими лингводискурсивными особенностями характеризуются посты топовых журналистов в популярных социальных сетях; как характеризуются лингвистические паттерны, продуцируемые топовыми журналистами в онлайн-пространстве; как различается контент, создаваемый журналистами в разных социальных сетях; каковы особенности этих различий в зависимости от специфики самих социальных платформ; как влияет политический контекст на лингвистические паттерны, продуцируемые топовыми журналистами в онлайн-пространстве. The article presents the results of a comprehensive analysis of the linguistic communication patterns of top Russian journalists in the official accounts of the social platforms VKontakte, Facebook, Instagram, Twitter. The purpose of this article is to study the linguistic patterns which are produced by the top journalists in their online accounts and which can set vectors of interpretation of political content created by state leaders and cause the transformation of discourse fields both in online and offline spaces. The average Russian spends almost half a day on online interaction, almost 50% of this time is spent on popular social media, including surfing the top journalists’ official accounts. The linguistic patterns produced by journalists in their online accounts are capable of transforming discursive fields both online and offline. The consumption of these patterns by ordinary users / readers who are under the influence of the discourse field becomes a determining factor in the process of making individual / collective decisions, the implementation of which forms a particular social action both in online and offline spaces. According to “Mediologia” monitoring data of social and mass media, the authors selected the accounts of Aleksey Venediktov, Vladimir Solovyev, Vladimir Pozner, Margarita Simonyan, and Ksenia Sobchak in VKontakte, Facebook, Instagram, Twitter. The data sets of the study are all the posts, comments, and threads of discussions that reflect the reaction of the above-mentioned journalists and the public to the Presidential Address to the Federal Assembly on 15 January 2020. The data sets were gained through a continuous sampling method and underwent a comprehensive analysis including network, linguo-discursive, folksonomic analyses. As a result of the study, the authors have drawn the conclusions on what linguistic and discursive features characterize the posts of the top journalists in popular social networks; the way the linguistic patterns produced by the top journalists in online space are characterized; the way the content created by the journalists in various social networks differs; what is the specificity of these differences depending on the specificity of the social platforms themselves; the way a political context affects the linguistic patterns produced by the top journalists in online space.


2019 ◽  
Vol 1 ◽  
pp. 1-2
Author(s):  
Clio Andris ◽  
Dipto Sarkar

<p><strong>Abstract.</strong> Interpersonal relationships are an important part of social and personal health. Studies of social capital show that individuals and communities with stronger ties are have an economic and health advantage. Yet, loneliness and isolation are becoming major public health issues. There is a pressing need to measure where relationships are strong and how accessible one’s social ties are, in order to learn how to better support face-to-face meetings and promote social health in society. However, the datasets we use to study people and human behaviour are most often mobility data and census data &amp;ndash; which tell us little about personal relationships. These data can be augmented with information about where people have ties, and how their relationships unfold over geographic space. The data we use to study the built environment include building footprints and infrastructure, and we can annotate these data by how (well) infrastructure supports different kinds of relationships, in order to ask new questions about how the landscape encourages relationships.</p><p> We suggest a list of methods for representing interpersonal relationships and social life at various socio-spatial levels of aggregation. We give an example of each, with an effort to span various use cases and spatial scales of data modelling.</p><p> <strong>Dyads (line) and Ego-based (star):</strong> This geometric model represents a relationship between two individuals (Figure 1A). The individuals can be geolocated to households, administrative units, real-time locations, etc. The tie can be given a nominal category such as family or co-worker, and edge weights that signify reported relationship strength, frequency of contact, frequency of face-to-face meeting, et cetera. Star models represent a central individual and his/her geolocated ties (that radiate from the centre). The star illustrates the theoretical concept of personal extensibility.</p><p> <strong>Points of Interest (points):</strong> Points of interest provide a place-based perspective (note that these entities can also be represented as polygons such as building footprints, or lines such as gradients of interaction on a subway). Certain places are better suited for fostering relationships than others (Figure 1B), and each can be annotated with their ability to foster: new ties (a nightclub), gender-bonding ties (bowling leagues), romantic ties (romantic restaurants), inter-generational ties (a religious facility), professional ties (conferences), et cetera.</p><p> <strong>Polygons/Administrative Units (polygons):</strong> These data are attached to administrative areal units (Census boundaries, provinces, zones, etc.). The data represent surveyed data on relationship-related variables in censuses, social surveys and social capital surveys. These surveys ask about trust, friendliness with neighbours, social life, belongingness to institutions, and more (Figure 1C), illustrating the social health of an area.</p><p> <strong>Aggregate Flows and Social Networks (lies and networks):</strong> This model illustrates the geolocated, social ties within a spatial extent, i.e. the social networks of a group of many people over a large extent (Figure 1D). Data can be sourced from social media, telecommunications patterns, and other declarations of relationships.</p><p> <strong>Regions (polygons):</strong> Regions, that may describe neighbourhoods within one city, or an agglomeration of cities, can be defined by social ties. Instead of commuting or economic ties, regions are defined by a preponderance of social ties within a given polygon, and a lack of ties between polygons (or between the polygon and any external area). Social regions represent a likeness and strong ties between the people that live within the region (Figure 1E).</p><p> Given these methods for representing social life and interpersonal relationships as GIS data, new questions may arise. At the <strong>dyadic level</strong>: how can we map the presence of a relationship between two people? At the <strong>ego-based level</strong>: how far and with what kind of diversity do people have ties? At the <strong>point of interest level</strong>: what kinds of mapable data can describe places’ ability to create new relationships and foster existing relationships? At the <strong>polygonal level</strong>: what kinds of mapable data can show where relationships are strong or weak? At the <strong>levels of flows and networks</strong>: what kinds of mapable data can describe systems of diffusion? At the <strong>regional level</strong>: what physical and administrative boundaries guide social ties?</p><p> For cartographers and geographic modellers looking to study social life, data acquisition, analysis, and mapping are challenges. The point of this extended abstract is to inventory the possibilities of mapping these data, open a dialog for experimenting with what kinds of symbologies, associated variables, classification schemes, visualization techniques and data collection opportunities are available for this purpose. We also hope to create spaces for comparative studies that describe the implications of these choices. In our search, we find that the major research challenges are the following: 1) privacy 2) geolocatable data 3) qualitative vs. quantitative data and 4) assurance statistically-significant samples sizes 5) analysis and modelling 6) visualization. Nevertheless, our goal is to make these indicators and data more GIS-friendly and available to geospatial analysts, modellers and cartographers.</p>


The article analyzes different approaches to the definition of «social networks» as technological complexes of organization and management of electronic information exchange among the subjects of social relations, united by common interests, information needs and skills. Based on the analysis of the scientific literature the essential characteristics of social networks that affect the formation and development of the adolescent's personality are revealed. Role of social networks at the present stage of development of society, which is manifested in the representation of interests not only of social groups but also of entire social groups, is defined in the article. The negative impact of social networks on the personality of the adolescent, which is manifested in the expansion of adolescents in cyberspace, the desire for independence and adulthood, selfexperimentation, which leads to risky activities both on the Internet and in real life are revealed. Concept of safe behavior in social networks as a set of actions of the individual when using the Internet, helping to meet the needs and at the same time prevent the possibility of causing damage to physical, mental, social well-being and property of man and others is analyzed. The basic rules of safe behavior in social Internet communities are highlighted. The structural components of safe behavior of adolescents in social networks are singled out: cognitive, motivational and actionreflexive; the concept of «professional training of future social professionals for the formation of safe behavior of adolescents in social networks» is revealed. Readiness is revealed as a result of the process of training future social specialists for professional activity on the formation of safe behavior of adolescents in social networks; the author's definition of the concept «readiness of future social professionals to form safe behavior of adolescents in social networks» is given. Components of readiness of future social workers to form safe behavior of teenagers in social networks, such as cognitive, motivational-personal and activity, are described.


Author(s):  
Yi Song ◽  
Xuesong Lu ◽  
Sadegh Nobari ◽  
Stéphane Bressan ◽  
Panagiotis Karras

One is either on Facebook or not. Of course, this assessment is controversial and its rationale arguable. It is nevertheless not far, for many, from the reason behind joining social media and publishing and sharing details of their professional and private lives. Not only the personal details that may be revealed, but also the structure of the networks are sources of invaluable information for any organization wanting to understand and learn about social groups, their dynamics and members. These organizations may or may not be benevolent. It is important to devise, design and evaluate solutions that guarantee some privacy. One approach that reconciles the different stakeholders’ requirement is the publication of a modified graph. The perturbation is hoped to be sufficient to protect members’ privacy while it maintains sufficient utility for analysts wanting to study the social media as a whole. In this paper, the authors try to empirically quantify the inevitable trade-off between utility and privacy. They do so for two state-of-the-art graph anonymization algorithms that protect against most structural attacks, the k-automorphism algorithm and the k-degree anonymity algorithm. The authors measure several metrics for a series of real graphs from various social media before and after their anonymization under various settings.


Author(s):  
PRANAV NERURKAR ◽  
MADHAV CHANDANE ◽  
SUNIL BHIRUD

Social circles, groups, lists, etc. are functionalities that allow users of online social network (OSN) platforms to manually organize their social media contacts. However, this facility provided by OSNs has not received appreciation from users due to the tedious nature of the task of organizing the ones that are only contacted periodically. In view of the numerous benefits of this functionality, it may be advantageous to investigate measures that lead to enhancements in its efficacy by allowing for automatic creation of customized groups of users (social circles, groups, lists, etc). The field of study for this purpose, i.e. creating coarse-grained descriptions from data, consists of two families of techniques, community discovery and clustering. These approaches are infeasible for the purpose of automation of social circle creation as they fail on social networks. A reason for this failure could be lack of knowledge of the global structure of the social network or the sparsity that exists in data from social networking websites. As individuals do in real life, OSN clients dependably attempt to broaden their groups of contacts in order to fulfill different social demands. This means that ‘homophily’ would exist among OSN users and prove useful in the task of social circle detection. Based on this intuition, the current inquiry is focused on understanding ‘homophily’ and its role in the process of social circle formation. Extensive experiments are performed on egocentric networks (ego is user, alters are friends) extracted from prominent OSNs like Facebook, Twitter, and Google+. The results of these experiments are used to propose a unified framework: feature extraction for social circles discovery (FESC). FESC detects social circles by jointly modeling ego-net topology and attributes of alters. The performance of FESC is compared with standard benchmark frameworks using metrics like edit distance, modularity, and running time to highlight its efficacy.


Author(s):  
Dalia Sulieman ◽  
Maria Malek ◽  
Hubert Kadima ◽  
Dominique Laurent

In this article, the authors consider the basic problem of recommender systems that is identifying a set of users to whom a given item is to be recommended. In practice recommender systems are run against huge sets of users, and the problem is then to avoid scanning the whole user set in order to produce the recommendation list. To cope with problem, they consider that users are connected through a social network and that taxonomy over the items has been defined. These two kinds of information are respectively called social and semantic information. In their contribution the authors suggest combining social information with semantic information in one algorithm in order to compute recommendation lists by visiting a limited part of the social network. In their experiments, the authors use two real data sets, namely Amazon.com and MovieLens, and they compare their algorithms with the standard item-based collaborative filtering and hybrid recommendation algorithms. The results show satisfying accuracy values and a very significant improvement of performance, by exploring a small part of the graph instead of exploring the whole graph.


2006 ◽  
Vol 11 (4) ◽  
pp. 39-52 ◽  
Author(s):  
Christina Prell

Social capital's rise in popularity is a phenomenon many have noted (Kadushin, 2006; Warde and Tampubolon, 2002; Portes, 1998). Although the concept is a relatively old one, it is the works of Bourdieu (1986), Coleman (1988; 1990), and Putnam (1993, 2000) that often get credited for popularizing the concept. These three, while sharing a view that social networks are important for social groups and society, place differing levels of emphasis on the role of networks in building trust or the exchange of various types of resources. In this paper, I briefly revisit these three theorists, and the criticisms each have received, to provide background for discussing recent research on social capital from a social networks approach. The social network approach is then applied to my own case study looking at the relations among not-for-profits, and special attention is given to the unique context of not-for-profits, and how this context might elaborate or challenge current thoughts on social, aka ‘network’ capital. A final discussion is also given to some measurement problems with the network approach to social capital.


Author(s):  
Rodrigo Carvalho ◽  
Leonardo Rocha

Currently, so-called Recommendation Systems (SRs) have been used to assist users in discovering relevant Points of Interest (POIs) on Location-Based Social Networks (LBSN), such as FourSquare and Yelp. Given the main challenges of data-sparse and geographic influence in this scenario, most of the work on POI recommendations has focused only on improving the effectiveness (i.e. accuracy) of the systems. However, there is a growing consensus that just effectiveness is not sufficient to assess the practical utility of these systems. In real scenarios, categorical and geographic diversities were identified as the main complementary dimensions for assessing user satisfaction and the usefulness of recommendations. The works in the literature are concentrated on only one of these concepts. In this work, we propose a new post-processing strategy, which combines these concepts in order to improve the user’s interest in POIs. Our experimental results in the Yelp data sets show that our strategy can improve user satisfaction, considering different SRs and multiple diversification metrics. Our method is capable of improving diversity by up to 120 % without significant losses in terms of effectiveness.


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