Data Mining for the Social Awareness of the Social Networks

K.R, Vidya Kumari; Kavitha, C. R.

doi:10.1109/csitss.2018.8768492

Cited by 6 publications

(1 citation statement)

References 8 publications

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“…Text mining has been found to be useful in various contexts and fields of study. For example, the Python programming language was used to analyse social networks (Vidya Kumari and Kavitha, 2018), for opinion (Dhanalakshmi et al , 2016) or information mining (Gopal et al , 2011). The techniques have been used to study a social media platform (Li et al , 2020), just as the library could use it to study user behaviour.…”

Section: Sentiment Analysismentioning

confidence: 99%

The importance of data mining, user information behaviour and interaction audit for information literacy

Ajibade¹,

Muchaonyerwa²

2022

LHTN

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Purpose This study aims to promote the need for advanced skills acquisition within the LIS and academic libraries. This study focuses on the importance of library management systems and the need for the graduates to be equipped with analytics skills. Combined with basic data, text mining and analytics, knowledge classification and information audit skills would benefit libraries and improve resource allocation. Agile institutional libraries in this big data era success hinge on the ability to perform depth analytics of both data and text to generate useful insight for information literacy training and information governance. Design/methodology/approach This paper adopted a living-lab methodology to use existing technology to conduct system analysis and LMS audit of an academic library of one of the highly ranked universities in the world. One of the benefits of this approach is the ability to apply technological innovation and tools to carry out research that is relevant to the context of LIS or other research fields such as management, education, humanities and social sciences. The techniques allow us to gain access to publicly available information because of system audits that were performed. The level of responsiveness of the online library was accessed, and basic information audits were conducted. Findings This study indicated skill gaps in the LIS training and the academic libraries in response to the fourth industrial technologies. This study argued that the role of skill acquisition and how it can foster data-driven library management operations. Hence, data mining, text mining and analytics are needed to probe into such massive, big data housed in the various libraries’ repositories. This study, however, indicated that without retraining of librarians or including this analytics programming in the LIS curriculum, the libraries would not be able to reap the benefits these techniques provided. Research limitations/implications This paper covered research within the general and academic libraries and the broader LIS fields. The same principle and concept is very important for both public and private libraries with substantial usage and patrons. Practical implications This paper indicated that librarianship training must fill the gaps within the LIS training. This can be done by including data mining, data analytics, text mining and processing in the curriculum. This skill will enable the news graduates to have skills to assist the library managers in making informed decisions based on user-generated content (UGC), LMS system audits and information audits. Thus, this paper provided practical insights and suggested solutions for academic libraries to improve the agility of information services. Social implications The academic librarian can improve institutional and LMS management through insights that are generated from the user. This study indicated that libraries' UGC could serve as robust insights into library management. Originality/value This paper argued that the librarian expertise transcends information literacy and knowledge classification and debated the interwoven of LMS and data analytics, text mining and analysis as a solution to improve efficient resources and training.

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