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Association Rule Analysis-Based Identification of Influential Users in the social Media

Computers, Materials & Continua 2022年第12期73卷 6479-6493页

作者： Saqib Iqbal Rehan Khan Hikmat Ullah Khan Fawaz Khaled Alarfaj Abdullah Mohammed Alomair Muzamil Ahmed College of Engineering Al Ain UniversityAl AinUnited Arab Emirates Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan Department of Computer Science and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)Al-HoufufSaudi Arabia

The exchange of information is an innate and natural process that assist in content dispersal.social networking sites emerge to enrich their users by providing the facility for sharing information and social interaction.The extensive adoption of social networking sites also resulted in user content generation.There are diverse research areas explored by the researchers to investigate the influence of social media on users and confirmed that social media sites have a significant impact on markets,politics and social life.Facebook is extensively used platform to share information,thoughts and opinions through posts and comments.The identification of influential users on the social web has grown as hot research field because of vast applications in diverse areas for instance political campaigns marketing,e-commerce,commercial and,etc.Prior research studies either uses linguistic content or graph-based representation of social network for the detection of influential users.In this article,we incorporate association rule mining algorithms to identify the top influential users through frequent patterns.The association rules have been computed using the standard evaluation measures such as support,confidence,lift,and conviction.To verify the results,we also involve conventional metrics for example accuracy,precision,recall and F1-measure according to the association rules perspective.The detailed experiments are carried out using the benchmark College-Msg dataset extracted by Facebook.The obtained results validate the quality and visibility of the proposed approach.The outcome of propose model verify that the association rule mining is able to generate rules to identify the temporal influential users on Facebook who are consistent on regular basis.The preparation of rule set help to create knowledge-based systems which are efficient and widely used in recent era for decision making to solve real-world problems.

关键词： Association rule mining ranking social web influential users social media

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“My ADHD Hellbrain”: A Twitter Data Science Perspective on a Behavioural Disorder

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Journal of Data and Information Science 2021年第1期6卷 13-34页

作者： Mike Thelwall Meiko Makita Amalia Mas-Bleda Emma Stuart Statistical Cybermetrics Research Group University of WolverhamptonUK

Purpose: Attention deficit hyperactivity disorder(ADHD) is a common behavioural condition. This article introduces a new data science method, word association thematic analysis, to investigate whether ADHD tweets can give insights into patient concerns and online communication needs. Design/methodology/approach: Tweets matching "my ADHD"(n=58,893) and 99 other conditions(n=1,341,442) were gathered and two thematic analyses conducted. Analysis 1: A standard thematic analysis of ADHD-related tweets. Analysis 2: A word association thematic analysis of themes unique to ADHD.Findings: The themes that emerged from the two analyses included people ascribing their brains agency to explain and justify their symptoms and using the concept of neurodivergence for a positive self-image. Research limitations: This is a single case study and the results may differ for other topics.Practical implications: Health professionals should be sensitive to patients' needs to understand their behaviour, find ways to justify and explain it to others and to be positive about their condition.Originality/value: Word association thematic analysis can give new insights into the(self-reported) patient perspective.

关键词： ADHD Word association thematic analysis social behaviour social web Twitter

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social Semantic Knowledge Management in AEC:Where Wiki meets Ontology

Social Semantic Knowledge Management in AEC:Where Wiki meets...

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2010 International Conference on Management Science (ICMS 2010)

作者： Jinyue Zhang Department of Civil Engineering University of Toronto Toronto Canada

Along with the increasing application of Wiki for knowledge management,researchers began to study the approach to embedding semantics in Wiki sites.Taking AEC（Architecture,Engineering and Construction） as the application domain,this paper argues the advantages of using ontologies in Wiki sites for semantic navigation among Wiki pages,meaningful knowledge annotation of Wiki contents,and better retrieval of information and knowledge.

关键词： Wiki ontology knowledge management Semantic web social web

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