Collective semantic behavior extraction in social networks

Li, Lei; Zhou, Chuan; He, Jianping; Wang, Jiamiao; Li, Xin; Wu, Xindong

doi:10.1016/j.jocs.2017.11.003

Cited by 12 publications

(14 citation statements)

References 31 publications

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“…Social networks are virtual media formed by community-built apps groups, interactions between people for the purpose of sharing knowledge, entertainment, expressing ideas, creating and exchanging content (Kaplan;Haenlein, 2010;Li et al, 2017). These media foster the creation of new forms of social capital and benefits from personal interactions (Ellison;Steinfield;Lampe, 2007).…”

Section: Externalized Behavior In Social Networkmentioning

confidence: 99%

“…These media foster the creation of new forms of social capital and benefits from personal interactions (Ellison;Steinfield;Lampe, 2007). Based on interactions in virtual networks, users form groups, establish or reinforce bonds focused on common interests (Ellison;Steinfield;Lampe, 2007;Li et al, 2017). Externalization in social networks highlights important elements about personal or academic life, as it acts as a mechanism for self-disclosure of intimate information but at the same time imbued with social desirability (Moon, 1998(Moon, , 2000Schau;Gilly, 2003).…”

Section: Externalized Behavior In Social Networkmentioning

confidence: 99%

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Experiências na Pós-Graduação, Comportamento nas Redes Sociais e Bem-Estar

et al. 2020

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Resumo: O objetivo do estudo consiste em analisar a relação entre a satisfação na pós-graduação e o comportamento exteriorizado nas redes sociais, mediado pelo bem-estar subjetivo. O estudo envolve 1.173 alunos de cursos stricto sensu. As evidências encontradas confirmam a relação, em especial quando esta se refere as relações interpessoais. Assim, a satisfação com a pós-graduação ultrapassa os limites da formação profissional e os laços sociais estabelecidos interferem no bem-estar dos alunos, o qual é exteriorizado nas redes sociais. O ambiente de compartilhamento de informações ganha um novo espaço caracterizado pelas redes sociais, que podem ser utilizadas para maximizar os níveis de bem-estar acadêmico.

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Section: Externalized Behavior In Social Networkmentioning

confidence: 99%

Section: Externalized Behavior In Social Networkmentioning

confidence: 99%

Experiências na Pós-Graduação, Comportamento nas Redes Sociais e Bem-Estar

et al. 2020

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Section: Background Technologiesmentioning

confidence: 99%

“…Semantic Reasoning is considered the process of generating new inferences from a collection of given propositions condition. It is deeply related to the logical ontologies perspective provided such as OWL Description Logics (DL) [14]. The Description Logics are used as one of Knowledge Representation Languages (KR) that depends on the artificial intelligence tools and Semantic Web technologies to dedicate a piece of new information based on the given and relevant concepts of the terminological knowledge of applications.…”

Section: Background Technologiesmentioning

confidence: 99%

Semantic Architecture for Modelling and Reasoning IoT Data Resources based on SPARK

Salama¹,

Masoud²,

Al-Feel³

2020

IJACSA

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Electronic Internet-of-Things is one of the foremost valuable techniques today. Through it, everything everywhere the globe became connected and intelligent, eliminating the wants to human-to-human interaction to perform tasks. This by changing all of those objects like humans, machines, devices and something around to be simply an internet Protocol (IP) to be expressed within the network environment through completely different sensors and actuators devices which might facilitate the interaction between all of them. These different types of sensors generate a large volume of various information and data. This type of sensor information created it generally useless because of the heterogeneity and lack of interoperability of it that represents it in unstructured form. So, investing from semantic internet techniques might handle these main challenges that face the IoT applications. Hence, the main contribution behind this research aims to boost the performance and quality of sensors information retrieved from IoT resources and applications by using semantic web technologies to resolve the matter of heterogeneity and interoperability and then convert the unstructured sensor data to structured form to realize the next level of investing of sensors employed in IoT applications. Also, the aim through this research to improve the performance of the tremendous amount of information that represents the demonstrated IoT information utilizing Big Data techniques such as Spark and its query language that's named SPARK-SQL as a streaming inquiry language for a colossal amount of information. The proposed architecture demonstrated that utilizing the semantic techniques to model the streaming sensors data improve the value of information and permit us to gather unused information. Moreover, the improvement by using SPARK leads to extend the performance of utilizing this sensor information in terms of the time retrieval of running queries, particularly when running the same queries utilizing the conventional SPARQL inquiry language.

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“…In addition to that, the effect of semantic web technologies on social media explained clearly [9] as a way to increase the understanding of social behavior and its metrics, such as its effect which influence engagement between social media users, and enrich the knowledge of effect by applying the semantic web technologies. Also, authors in [10] proposed a semantic social behavior extraction process based on the most commonly used social networks, such as Facebook that enable the extraction of semantic behaviors from any semantic information. Besides, learning environmental systems based on social network platforms is introduced in [11] to perform an automotive semantic reasoner that depends on the interactions among people and their relations with the learning resources.…”

Section: Related Workmentioning

confidence: 99%

Rule-based Recommendation System based on Semantic Web of Things

Salama¹,

Shaheen²,

Al-Feel³

2020

International Journal of Engineering Research and Technology

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Social networking has certainly been the most apparent concept of virtual human life where all users may communicate with each other. Thus all users of social networks aim to handle and monitor the demands of their lives through their platforms for virtual lifestyle. And since the Internet of Things is now considered one of the latest technologies which connect everything around us together as objects in the form of an IP address that enables contact between them. On the other side, IoT also struggles from the variability of data obtained from various sensors. Addressing this problem includes the alignment of semantic web technology. We, therefore, aim to take advantage of the Internet of Things and Semantic Web technologies revolution by developing an integrated model based on the semantic web. Through our proposed research, we intend to create a rule-based recommendation framework that advises users to take the most convenient action that IoT applications should execute on their current social network activities utilizing the built-in definition logic rules. By virtue of this social recommender, Users of IoT applications can monitor their apps only by communicating their social status via their social media instead of using special IoT apps on their mobile devices. As a result, there is more integration between IoT resources and Social Network resources.

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Collective semantic behavior extraction in social networks

Cited by 12 publications

References 31 publications

Experiências na Pós-Graduação, Comportamento nas Redes Sociais e Bem-Estar

Experiências na Pós-Graduação, Comportamento nas Redes Sociais e Bem-Estar

Semantic Architecture for Modelling and Reasoning IoT Data Resources based on SPARK

Rule-based Recommendation System based on Semantic Web of Things

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