A review on user influence ranking factors in social networks

Amalanathan, Anthoniraj; Anouncia, S. Margret

doi:10.1504/ijwbc.2016.074274

Cited by 10 publications

(7 citation statements)

References 20 publications

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“…This is one of the key building block of several major SM network including Facebook, YouTube, Twitter, Linked, Google+, MySpace, etc. (Amalanathan & Anouncia, 2016;Kietzmann et al , 2011). The value of many social networks, and the users of those social networks, is determined on basis of the level of conversations they are able to generate.…”

Section: Conversationmentioning

confidence: 99%

Unfolding Utilization of Functional Blocks of Social Media in Building Brand Equity in Higher Education Institutes

Mehmood¹,

Janjua²,

Aftab³

2021

IRMBR

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The purpose of this research was to explore how HEIs are using Social Media (SM) as a marketing communication tool and to understand their branding focus through SM. It was investigated by understanding the utilization of functional blocks of SM by HEIs, which are believed to contribute towards elements of brand equity. Utilization of functional blocks of SM was studied through theoretical lens of honeycomb framework of SM. This study used qualitative methodology, based on inductive research approach. The data was collected via Netnography method, based on six months usage of SM accounts of seven selected HEIs of Pakistan, out of 225, which exhibited active SM presence. HEIs were found using nine different content types on SM. The content types that showed highest utilization of functional blocks, i.e. endorsements and promotional-messages, were amongst the least preferred content types of HEIs. However, their most preferred content types, i.e. event coverage & announcements, showed lowest engagement level, and were seen contributing towards lower stages of brand building i.e. brand image and awareness only, thus reflecting a myopic focus of HEIs in terms of their brand building through SM. Findings of this study will enable HEIs to revisit their content preference on SM based on the desired branding objectives. This research has extended theoretical knowledge regarding SM Marketing in context of HEIs. Further, it has extended comprehension of various functional blocks of SM, in organizational context, based on literature review. Keywords: Social Media Marketing, Brand Equity, Honeycomb Framework, Higher Education Institutes.

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Section: Conversationmentioning

confidence: 99%

Unfolding Utilization of Functional Blocks of Social Media in Building Brand Equity in Higher Education Institutes

Mehmood¹,

Janjua²,

Aftab³

2021

IRMBR

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“…The literature of trust in social media shows a lack of approaches for measuring domain-based trust. Several reviews have carried out to highlight the importance of conducting a fine-grained trustworthiness analysis in the context of SBD [15,[75][76][77]. In particular, measuring the user's trustworthiness in each domain of knowledge is vital to better understanding users' behaviours in the online social services.…”

Section: Domain-based/ Topic-specific Trustworthiness Approachesmentioning

confidence: 99%

Credibility Analysis in Social Big Data

Abu-Salih

Wongthongtham

Zhu

et al. 2021

Social Big Data Analytics

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The concept of social trust has attracted an attention of information processors/data scientists and information consumers / business firms. One of the main reasons for acquiring the value of SBD is to provide frameworks and methodologies using which the credibility of online social services users can be evaluated. These approaches should be scalable to accommodate large-scale social data. Hence, there is a need for well comprehending of social trust to improve and expand the analysis process and inferring credibility of social big data. Given the exposed environment's settings and fewer limitations related to online social services, the medium allows legitimate and genuine users as well as spammers and other low trustworthy users to publish and spread their content. This chapter presents an overview of the notion of credibility in the context of SBD. It also list an array of approaches to measure and evaluate the trustworthiness of users and their contents. Finally, a case study is presented that incorporates semantic analysis and machine learning modules to measure and predict users' trustworthiness in numerous domains in different time periods. The evaluation of the conducted experiment validates the applicability of the incorporated machine learning techniques to predict highly trustworthy domain-based users.

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“…The literature of trust in social media shows a lack of approaches for measuring domain-based trust. Several reviews have carried out to highlight the importance of conducting a finegrained trustworthiness analysis in the context of SBD [8,[21][22][23]. In particular, measuring the user's trustworthiness in each domain of knowledge is vital to better understanding users' behaviours in the OSNs.…”

Section: Domain-based/ Topic-specific Trustworthiness Approachesmentioning

confidence: 99%

Social Credibility Incorporating Semantic Analysis and Machine Learning: A Survey of the State-of-the-Art and Future Research Directions

Abu-Salih

Bremie

Wongthongtham

et al. 2019

Advances in Intelligent Systems and Computing

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The wealth of Social Big Data(SBD) represents a unique opportunity for organisations to obtain the excessive use of such data abundance to increase their revenues. Hence, there is an imperative need to capture, load, store, process, analyse, transform, interpret, and visualise such manifold social datasets to develop meaningful insights that are specific to an application's domain. This paper lays the theoretical background by introducing the state-ofthe-art literature review of the research topic. This is associated with a critical evaluation of the current approaches, and fortified with certain recommendations indicated to bridge the research gap.

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A review on user influence ranking factors in social networks

Cited by 10 publications

References 20 publications

Unfolding Utilization of Functional Blocks of Social Media in Building Brand Equity in Higher Education Institutes

Unfolding Utilization of Functional Blocks of Social Media in Building Brand Equity in Higher Education Institutes

Credibility Analysis in Social Big Data

Social Credibility Incorporating Semantic Analysis and Machine Learning: A Survey of the State-of-the-Art and Future Research Directions

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