Framework for Social Network Data Mining

Fernando, Gayana; Gapar,; MdJohar, MdJohar

doi:10.5120/20434-2765

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…In this context, some of the models only consider a single entity of analysis, namely the user (Gjoka et al 2011;Banica et al 2015;Madan and Chopra 2015;Ghafoor and Niazi 2016). Vosecky et al (2014), Fernando et al (2015, Lai and To (2015) only consider the modeling of the semantic content, by using hashtags and web document links to identify keywords. The models that consider all the three entities of web discourse (Oussalah et al 2013;Vosecky et al 2014;Walha et al 2017;Appel et al 2018) restrict their usage to a specific OSN.…”

Section: Evaluative Comparisonmentioning

confidence: 99%

“…to interpret semantic data. Moreover, some of the models (Oussalah et al 2013;Zielinski et al 2013;Vosecky et al 2014;Fernando et al 2015;Vicario et al 2017;Alhalabi et al 2021;Arafeh et al 2021) also require complex programming knowledge (namely Twitter or Facebook APIs). Conversely, the proposed model does not depend on external data sources or specific software, using commonly available software (Excel and Gephi) to interpret semantic data.…”

Section: Evaluative Comparisonmentioning

confidence: 99%

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Getting decision support from context-specific online social networks: a case study

Freire

Antunes

Costa

2022

Soc. Netw. Anal. Min.

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The combination between online social networks (OSN) and decision processes provides a favorable social data analysis paradigm for efficient decision support and business-processes integration. This paper presents a framework for handling OSN's contents, providing a simpler and effective approach for information retrieval and processing. The objective is to address a decision-making problem, by using that framework to extract, process, structure and analyze the OSN's data. The decision process is not only guided by OSN data, but also by social network analysis methodology and is entirely based on the communications among social media users. Our framework combines two different, though complementary, perspectives: the analysis of the interactions among users and the semantic analysis of their discourses. In addition, it aims to bridge technology and manual-based approaches, thus enhancing the possibilities for making a better use of an OSN, using free-available software. The case study, herein, aims to estimate customers' requests, solely based on their Facebook posts, showing that the unstructured data of the web's discourse can be used to support this kind of decision processes.

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Section: Evaluative Comparisonmentioning

confidence: 99%

Section: Evaluative Comparisonmentioning

confidence: 99%

Getting decision support from context-specific online social networks: a case study

Freire

Antunes

Costa

2022

Soc. Netw. Anal. Min.

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“…The social networks can be mapped as a Markov chain Crnovrsanin et.al introduces a new procedure for social network discovery based on the Markov centrality of a node [22]. Peng et.al have used the Hidden Markov model along with SVM classifier to predict sports, weather and social activities on Twitter messages [23].…”

Section: Literature Reviewmentioning

confidence: 99%

Personalized Marketing in Facebook using Hidden Markov Model

Fernando¹,

Gapar²

2018

IJCA

Self Cite

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In the current dynamic business world, each business is compelled to keep up with the state-of-the-art technologies to survive in the ever competitive market. With the dawn of the digital era, marketers were opened up to a brand new path of marketing strategies that allows them to not only manage their existing consumers but also to capture the world wide attention of potential consumers at a trivial cost. Social networks are now pronounced to be the next generation of consumer-centric interactions. This is proven further by the data released by popular social networks like Facebook regarding their consumer interactions. Big data analysis is another innovative technology concept used by businesses to recognize consumer buying patterns. Big data analysis has made path to web data mining. With the growth of social networks its profile holders have become the creators and distributors of business data. Due to this, social networks hold terabytes of raw data. With these massive advantages in line, web mining techniques and theory requires to be researched thoroughly to uncover the potential patterns and user access behaviors of these large amounts of online data. The major dilemma faced by researches in this field is the dynamic behavior of online data. To add to this dilemma the data is also quite unstructured. With these difficulties, the need to discover an efficient approach to mine unorganized and dynamic data in social networks is of utmost importance. This research aims to fill this research gap by introducing a framework that can be used to mine profile user data extracted from Facebook and use the knowledge in personalized marketing. This research paper explains framework build based on Hidden Markov Model to predict the next movement to handle the dynamic behavior of web data.

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National Security Intelligence through Social Network Data Mining

Ekwunife

2020

2020 IEEE International Conference on Big Data (Big Data)

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Framework for Social Network Data Mining

Cited by 4 publications

References 11 publications

Getting decision support from context-specific online social networks: a case study

Getting decision support from context-specific online social networks: a case study

Personalized Marketing in Facebook using Hidden Markov Model

National Security Intelligence through Social Network Data Mining

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