Design of a Novel Query System for Social Network

2019 ◽  
Vol 12 (2) ◽  
pp. 175-193
Author(s):  
Charu Virmani ◽  
Dimple Juneja ◽  
Anuradha Pillai
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Vital Role ◽  
Agglomerative Clustering ◽  
User Intention ◽  
User Query ◽  
Proposed Model ◽  
Hierarchical Agglomerative Clustering ◽  
Query System

User intention and nature of network plays a vital role towards the quality of response received as the result of any user query. Therefore, the need of system understanding the user's intent and network dynamism as well is highly apparent. The proposed query processing and analysing system (QPAS) for social networks is based on extracting user's intent from various social networks using existing NLP techniques. It fetches the information and further employs hybrid ensemble k-means hierarchical agglomerative clustering (HEKHAC) and modified Bitonic sort to improve the responses. The proposed approach offers an edge over other mechanisms as it not only retrieves more user-centric results as compared to traditional way of keyword-based searching but also in timely manner as well. It is an innovative approach to investigate the new aspects of social network. The proposed model offers a noteworthy revolution scoring up to precision and recall respectively.

The Usage of Social Network to Students: Does It Improve Student’s Education Quality During COVID-19 Outbreak

2020 ◽  
Vol 17 (12) ◽  
pp. 5224-5228
Author(s):  
Indraah Kolandaisamy ◽  
Raenu Kolandaisamy
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Education Quality ◽  
Quality Of Education ◽  
Online Classes ◽  
Outbreak Period ◽  
Technology Advancement ◽  
The Relationship

In the era of technology advancement and COVID-19 outbreak period, all physical classes have been converted to online classes through social network platforms. Having online classes through social networks are actually very comfortable and flexible for students as they can have their classes at various places. This paper is focuses on the relationship between usages of social network and the quality of education during COVID-19 outbreak.

Unraveling the Taste Fabric of Social Networks

2009 ◽  
pp. 1521-1546
Author(s):  
Hugo Liu ◽  
Pattie Maes ◽  
Glorianna Davenport
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Social Connectedness ◽  
Natural Language Text ◽  
Cross Domain ◽  
Semantic Mining ◽  
The Rich ◽  
Language Text

Popular online social networks such as Friendster and MySpace do more than simply reveal the superficial structure of social connectedness—the rich meanings bottled within social network profiles themselves imply deeper patterns of culture and taste. If these latent semantic fabrics of taste could be harvested formally, the resultant resource would afford completely novel ways for representing and reasoning about web users and people in general. This paper narrates the theory and technique of such a feat—the natural language text of 100,000 social network profiles were captured, mapped into a diverse ontology of music, books, films, foods, etc., and machine learning was applied to infer a semantic fabric of taste. Taste fabrics bring us closer to improvisational manipulations of meaning, and afford us at least three semantic functions—the creation of semantically flexible user representations, cross-domain taste-based recommendation, and the computation of taste-similarity between people— whose use cases are demonstrated within the context of three applications—the InterestMap, Ambient Semantics, and IdentityMirror. Finally, we evaluate the quality of the taste fabrics, and distill from this research reusable methodologies and techniques of consequence to the semantic mining and Semantic Web communities.

scholarly journals Marketing and social networks: a criterion for detecting opinion leaders

2017 ◽  
Vol 26 (3) ◽  
pp. 347-366 ◽  
Author(s):  
Arnaldo Mario Litterio ◽  
Esteban Alberto Nantes ◽  
Juan Manuel Larrosa ◽  
Liliana Julia Gómez
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Semantic Analysis ◽  
Point Of View ◽  
Eigenvector Centrality ◽  
Content Type ◽  
Sporting Event ◽  
Centrality Metrics ◽  
Proposed Model

Purpose The purpose of this paper is to use the practical application of tools provided by social network theory for the detection of potential influencers from the point of view of marketing within online communities. It proposes a method to detect significant actors based on centrality metrics. Design/methodology/approach A matrix is proposed for the classification of the individuals that integrate a social network based on the combination of eigenvector centrality and betweenness centrality. The model is tested on a Facebook fan page for a sporting event. NodeXL is used to extract and analyze information. Semantic analysis and agent-based simulation are used to test the model. Findings The proposed model is effective in detecting actors with the potential to efficiently spread a message in relation to the rest of the community, which is achieved from their position within the network. Social network analysis (SNA) and the proposed model, in particular, are useful to detect subgroups of components with particular characteristics that are not evident from other analysis methods. Originality/value This paper approaches the application of SNA to online social communities from an empirical and experimental perspective. Its originality lies in combining information from two individual metrics to understand the phenomenon of influence. Online social networks are gaining relevance and the literature that exists in relation to this subject is still fragmented and incipient. This paper contributes to a better understanding of this phenomenon of networks and the development of better tools to manage it through the proposal of a novel method.

scholarly journals Image Based Classification of Rumor Information from the Social Network Platform

2021 ◽  
Vol 38 (5) ◽  
pp. 1413-1421
Author(s):  
Vallamchetty Sreenivasulu ◽  
Mohammed Abdul Wajeed
Keyword(s):  
Social Networks ◽  
Social Network ◽  
End Users ◽  
Web Based ◽  
Suggested Approach ◽  
Text Input ◽  
Social Network Data ◽  
Proposed Model ◽  
Spam Filters ◽  
Network Platform

Spam emails based on images readily evade text-based spam email filters. More and more spammers are adopting the technology. The essence of email is necessary in order to recognize image content. Web-based social networking is a method of communication between the information owner and end users for online exchanges that use social network data in the form of images and text. Nowadays, information is passed on to users in shorter time using social networks, and the spread of fraudulent material on social networks has become a major issue. It is critical to assess and decide which features the filters require to combat spammers. Spammers also insert text into photographs, causing text filters to fail. The detection of visual garbage material has become a hotspot study on spam filters on the Internet. The suggested approach includes a supplementary detection engine that uses visuals as well as text input. This paper proposed a system for the assessment of information, the detection of information on fraud-based mails and the avoidance of distribution to end users for the purpose of enhancing data protection and preventing safety problems. The proposed model utilizes Machine Learning and Convolutional Neural Network (CNN) methods to recognize and prevent fraud information being transmitted to end users.

scholarly journals How to make methodological decisions when inferring social networks

2019 ◽  
Author(s):  
André C. Ferreira ◽  
Rita Covas ◽  
Liliana R. Silva ◽  
Sandra C. Esteves ◽  
Inês F. Duarte ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Data Collection ◽  
Network Inference ◽  
A Priori ◽  
Pilot Studies ◽  
Multiple Hypotheses Testing ◽  
Sociable Weavers ◽  
Breeding Groups

ABSTRACTConstructing and analysing social networks data can be challenging. When designing new studies, researchers are confronted with having to make decisions about how data are collected and networks are constructed, and the answers are not always straightforward. The current lack of guidance on building a social network for a new study system might lead researchers to try several different methods, and risk generating false results arising from multiple hypotheses testing. We suggest an approach for making decisions when developing a network without jeopardising the validity of future hypothesis tests. We argue that choosing the best edge definition for a network can be made using a priori knowledge of the species, and testing hypotheses that are known and independent from those that the network will ultimately be used to evaluate. We illustrate this approach by conducting a pilot study with the aim of identifying how to construct a social network for colonies of cooperatively breeding sociable weavers. We first identified two ways of collecting data using different numbers of feeders and three ways to define associations among birds. We then identified which combination of data collection and association definition maximised (i) the assortment of individuals into ‘breeding groups’ (birds that contribute towards the same nest and maintain cohesion when foraging), and (ii) socially differentiated relationships (more strong and weak relationships than expected by chance). Our approach highlights how existing knowledge about a system can be used to help navigate the myriad of methodological decisions about data collection and network inference.SIGNIFICANCE STATEMENTGeneral guidance on how to analyse social networks has been provided in recent papers. However less attention has been given to system-specific methodological decisions when designing new studies, specifically on how data are collected, and how edge weights are defined from the collected data. This lack of guidance can lead researchers into being less critical about their study design and making arbitrary decisions or trying several different methods driven by a given preferred hypothesis of interest without realising the consequences of such approaches. Here we show that pilot studies combined with a priori knowledge of the study species’ social behaviour can greatly facilitate making methodological decisions. Furthermore, we empirically show that different decisions, even if data are collected under the same context (e.g. foraging), can affect the quality of a network.

scholarly journals Social Network and Healthy Ageing among Elderly in Helping Hearts Old Age Home, Coimbatore

2019 ◽  
Vol 8 (12S) ◽  
pp. 571-576
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Participation ◽  
Meaning In Life ◽  
Well Being ◽  
The Elderly ◽  
Healthy Ageing ◽  
The Social ◽  
The Relationship

In the current times, the research cites that elderly definitely need social networks to aid in their mental and physical well being. The previous researches have indicated familyfocused, friend-focused, and restricted types as the types of social networks available. Social network include social interaction and social communication. It is the need of the hour to study about the social network of the elderly population because many of them are left with nobody and loneliness is one of the important factor not to mention about desertion by their loved ones since they are no longer productive individuals. The heterogeneity of social networks is pathway to successful and healthy ageing. Healthy ageing is about using opportunities so that they can have social participation and lead a good quality of life. Elderly need not be burdensome individuals in the society instead they can be involved in lot of activities which contribute to them ageing gracefully. The research studies state that rural elderly have more chances of social participation that they find more meaning in life which is a contributing factor for healthy ageing. The present study aims to find out the relationship between social network and healthy ageing.

scholarly journals Complex Neutrosophic Hypergraphs: New Social Network Models

Algorithms ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 234 ◽  
Author(s):  
Anam Luqman ◽  
Muhammad Akram ◽  
Florentin Smarandache
Keyword(s):  
Social Networks ◽  
Sustainable Development ◽  
Social Network ◽  
Complex Systems ◽  
Network Models ◽  
Neutrosophic Set ◽  
Neutrosophic Sets ◽  
Score Functions ◽  
Proposed Model ◽  
Complex Valued

A complex neutrosophic set is a useful model to handle indeterminate situations with a periodic nature. This is characterized by truth, indeterminacy, and falsity degrees which are the combination of real-valued amplitude terms and complex-valued phase terms. Hypergraphs are objects that enable us to dig out invisible connections between the underlying structures of complex systems such as those leading to sustainable development. In this paper, we apply the most fruitful concept of complex neutrosophic sets to theory of hypergraphs. We define complex neutrosophic hypergraphs and discuss their certain properties including lower truncation, upper truncation, and transition levels. Furthermore, we define T-related complex neutrosophic hypergraphs and properties of minimal transversals of complex neutrosophic hypergraphs. Finally, we represent the modeling of certain social networks with intersecting communities through the score functions and choice values of complex neutrosophic hypergraphs. We also give a brief comparison of our proposed model with other existing models.

Extracting Influential Nodes in Social Networks on Local Weight Aspect

2016 ◽  
Vol 8 (2) ◽  
pp. 21-35
Author(s):  
Jun Jun Cheng ◽  
Yan Chao Zhang ◽  
Xin Zhou ◽  
Hui Cheng
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Time Complexity ◽  
Heuristic Algorithms ◽  
Transmission Model ◽  
User Interest ◽  
Local Weight ◽  
Weight Index ◽  
Influential Nodes

Studies have shown that influential nodes play an important role in all kinds of dynamic behavior in the complex network. Excavation or recognition of such nodes contributes to the development of application areas such as social network advertising and user interest recommendation. Although some heuristic algorithms such as degree, betweenness, closeness and k-shell (or k-core) can identify influential nodes at the same time, they are disadvantaged in terms of accuracy and time complexity. Based on this, the authors propose a novel local weight index to distinguish the node influence based on the theory of ties strength. This index emphasizes that the node influence is jointly decided by the quantity and quality of the neighbors, and its time complexity is much lower than closeness and betweenness. With the aid of SIR information transmission model, this paper verifies the validity of local weight index.

Unraveling the Taste Fabric of Social Networks

2009 ◽  
pp. 18-43
Author(s):  
Hugo Liu
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Social Connectedness ◽  
Natural Language Text ◽  
Cross Domain ◽  
Semantic Mining ◽  
The Rich ◽  
Language Text

Popular online social networks such as Friendster and MySpace do more than simply reveal the superficial structure of social connectedness — the rich meanings bottled within social network profiles themselves imply deeper patterns of culture and taste. If these latent semantic fabrics of taste could be harvested formally, the resultant resource would afford completely novel ways for representing and reasoning about web users and people in general. This paper narrates the theory and technique of such a feat — the natural language text of 100,000 social network profiles were captured, mapped into a diverse ontology of music, books, films, foods, etc., and machine learning was applied to infer a semantic fabric of taste. Taste fabrics bring us closer to improvisational manipulations of meaning, and afford us at least three semantic functions — the creation of semantically flexible user representations, cross-domain taste-based recommendation, and the computation of taste-similarity between people — whose use cases are demonstrated within the context of three applications — the InterestMap, Ambient Semantics, and IdentityMirror. Finally, we evaluate the quality of the taste fabrics, and distill from this research reusable methodologies and techniques of consequence to the semantic mining and Semantic Web communities.

Predicting the Usage Intention of Social Network Games

2013 ◽  
pp. 195-204
Author(s):  
Chiao-Chen Chang ◽  
Yang-Chieh Chin
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Technology Acceptance ◽  
Extrinsic Motivation ◽  
Network Games ◽  
User Intention ◽  
Personal Innovativeness ◽  
Personal Involvement ◽  
Network Game ◽  
Acceptance Model

Social network sites (SNSs) are new communication channels with which people can share information. The main functions of SNSs, such as MySpace, Facebook, and Orkut, consist of displaying a user’s social contacts, enabling people to view each other’s social networks and search for common friends or interesting content. Social networks are also connected to gaming and it is quickly becoming one of the most popular categories of applications on SNSs. The goal of this project is to gain insight into the factors that affect user intention to use a social network game. The study uses an extended technology acceptance model and focuses on combining personal innovativeness, personal involvement, intrinsic motivation and extrinsic motivation to explain usage intentions for social network games. The proposed model was tested with data collected from potential users of a social network game. A multiple regression analysis and MANOVA analysis were then conducted to identify the key causal relationships. It is expected that personal innovativeness and personal involvement will have positive effects on intrinsic and extrinsic motivation and ultimately influence usage intentions with regard to social network games.

Sign in / Sign up

Export Citation Format

Share Document