A survey on semantic Web and big data technologies for social network analysis

Kulcu, Sercan; Doğdu, Erdoğan; Özbayoğlu, A. Murat

doi:10.1109/bigdata.2016.7840792

Cited by 25 publications

(10 citation statements)

References 77 publications

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“…Structural analysis is the first section of the research areas because it deals with initial investigations about the structure and functionality of social networking services. There are many reasons why researchers are interested in this area, such as understanding how a social network is formed, describing the structure of the network (commonly by using graph algorithms), comparing its structure to other known networks, or exploring unseen relations between entities in a network 12 . Traditional structural SNA examines social relations in terms of network theory consisting of nodes and links 30,31 .…”

Section: Fundamentals Of Snamentioning

confidence: 99%

“…There are many reasons why researchers are interested in this area, such as understanding how a social network is formed, describing the structure of the network (commonly by using graph algorithms), comparing its structure to other known networks, or exploring unseen relations between entities in a network. 12 Traditional structural SNA examines social relations in terms of network theory consisting of nodes and links. 30,31 Some network analysis uses a matrix to represent the social network.…”

Section: Structural Analysismentioning

confidence: 99%

“…Recently, social interactions through Web 2.0 social platforms have attracted great attention in the semantic web community. Semantic web frameworks supply a graph model (RDF), a schema definition (RDFS and OWL) and a query language (SPARQL) in order to represent, exchange, and query social data online 12 . These concepts are explained in Section 4 in detail.…”

Section: Introductionmentioning

confidence: 99%

“…Semantic web frameworks supply a graph model (RDF), a schema definition (RDFS and OWL) and a query language (SPARQL) in order to represent, exchange, and query social data online. 12 These concepts are explained in Section 4 in detail.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Semantic analysis on social networks: A survey

Bayrakdar

Yücedağ

Şimşek

et al. 2020

Int J Communication

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Summary As social networks are getting more and more popular day by day, large numbers of users becoming constantly active social network users. In this way, there is a huge amount of data produced by users in social networks. While social networking sites and dynamic applications of these sites are actively used by people, social network analysis is also receiving an increasing interest. Moreover, semantic understanding of text, image, and video shared in a social network has been a significant topic in the network analysis research. To the best of the author's knowledge, there has not been any comprehensive survey of social networks, including semantic analysis. In this survey, we have reviewed over 200 contributions in the field, most of which appeared in recent years. This paper not only aims to provide a comprehensive survey of the research and application of social network analysis based on semantic analysis but also summarizes the state‐of‐the‐art techniques for analyzing social media data. First of all, in this paper, social networks, basic concepts, and components related to social network analysis were examined. Second, semantic analysis methods for text, image, and video in social networks are explained, and various studies about these topics are examined in the literature. Then, the emerging approaches in social network analysis research, especially in semantic social network analysis, are discussed. Finally, the trending topics and applications for future directions of the research are emphasized; the information on what kind of studies may be realized in this area is given.

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Section: Fundamentals Of Snamentioning

confidence: 99%

Section: Structural Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Semantic analysis on social networks: A survey

Bayrakdar

Yücedağ

Şimşek

et al. 2020

Int J Communication

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“…Exploring the links between these nodes on social media has been conducted by social media analysis (SMA) (Bhattacharya et al , 2014; Fang et al , 2014). In recent years, SMA has become a significant agenda for many purposes (Kulcu et al , 2016). Usually graph structure is used for analyzing social networks (Aggarwal, 2011).…”

Section: Introductionmentioning

confidence: 99%

New centrality measure for nodes based on user social status and behavior on Facebook

Forouzandeh

Sheikhahmadi

Aghdam

et al. 2018

IJWIS

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Purpose This paper aims to analyze the role of influential nodes on other users on Facebook social media sites by social and behavioral characteristics of users. Hence, a new centrality for user is defined, applying susceptible-infected recovered (SIR) model to identify influence of users. Results show that the combination of behavioral and social characteristics would be determined the most influential users that influence majority of nodes on social networks. Design/methodology/approach In this paper, the authors define a new centrality for users, considering node status and behaviors. Thus, this node has a high level of influence. Node social status includes node degree, clustering coefficient and average neighbors’ node, and social status of node refers to user activities on Facebook social media website such as sending posts and receiving likes from other users. According to social status and user activity, the new centrality is defined. Finally, through the SIR model, the authors explore infection power of nodes and their influences of other node in the network. Findings Results show that the proposed centrality is more effective than other centrality approaches, infecting more nodes in social network. Another significant point in this research is that users who have high social status and activities on Facebook are more influential than users who have only high social status on the Facebook social media. Originality/value The influence of user on others in social media includes two key factors. The first factor is user social status such as node degree and clustering coefficient in social media graph and the second factor is related to user social activities in social media sites. Most centralities focused on node social status without considering node behavior. This paper analyzes the role of influential nodes on other users on Facebook social media site by social and behavioral characteristics of users.

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