Department of Information Science

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Browsing Department of Information Science by browse.metadata.advisor "Cornelissen, Laurenz Aldu"

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    A case study on how employees use social media in a consulting work environment
    (Stellenbosch : Stellenbosch University, 2022-12) Luposo, Claire Benedict Mwadi; Cornelissen, Laurenz Aldu; Watson, Bruce; Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Information Science.
    ENGLISH SUMMARY: Organisations are currently performing in a highly competitive environment and are increasingly interested in adopting advance technologies for their business operations. The importance and use of social media in the workplace has greatly increased and gained a wide interest from researchers. Social media usage in the workplace cannot be ignored or mismanaged in this information age. However, despite the increase in social media usage today, there is little understanding of the tools and platforms in developing countries. There is limited research on the ways in which social media is used in consulting firms. Thus, the study critically investigates how social media is used by employees in the consulting work environment and the role it plays on employees’ productivity. The purpose of this research is to establish the behavioural intent of how and why employees use social media in the work environment. The problem and gap the research aims to address is to critically investigate whether social media contributes to the productivity of employees and whether social media platforms can be used as a knowledge sharing tool in organisations. The study used a descriptive research design, the total population for this study was 153 participants. Structured questionnaires were used for the collection of relevant primary data. The findings revealed that social media usage in the workplace does not affect productivity and mostly contributes to knowledge sharing, a flow of communication and ongoing learning. The study sought to fill the existing gap in research literature with regards to the use of social media in the workplace and employee productivity. As social media cannot be disregarded during working hours, organisations can reinforce social media policies.
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    Classification of social media event-discussions using interaction patterns : a social network analysis approach
    (Stellenbosch : Stellenbosch University, 2020-12) Schoonwinkel, Petrus; Cornelissen, Laurenz Aldu; Parry, Douglas A.; Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Information Science.
    ENGLISH SUMMARY : This thesis uses social network analysis to explore the classification of social media discussions, utilising network structure derived from interactions on Twitter, while requiring minimal domain knowledge. In academia and industry, researchers strive to understand the patterns of interaction between actors on social media platforms, and how their actions may relate to particular events, topics, network characteristics, personalities and characters, among other factors. From literature, it is found that researchers in a wide range of disciplines lack the tools to classify in a variety of event-discussions. Further exemplified with the scenario where topics of interest to researchers on social media can overlap and that users are often engaged in a multitude of topics simultaneously, an approach to classification that necessitates minimal prior domain knowledge on the contents of the datasets is required. This study is a proof of concept for the use of network metrics to characterise and classify a diverse set of events that were discussed on social media. To classify social media data, one can utilise unsupervised machine learning methods. From the literature it is found that a multitude of clustering methods with regards to social media has been explored, in multi-media, networks, textual and other contexts. However, only limited approaches to classifying social media data—specifically Twitter—in terms of their network structure have been explored. This study does not aim to replace those methods but add to an array of tools that can be used by researchers, both in academia and in industry, to maximise the value obtained from social media data. In order to obtain metrics whereby to perform classification, a novel approach to modelling interactions with the data source, Twitter, was developed and a set of network measures and data descriptors that characterise the data were explored. The network measures and data descriptors were subjected to dimensionality reduction to account for co-variance in the measurements and to evaluate the contribution of each network measure, in order to expand the literature on what they define in the context of this study. The resulting principal components were used to classify the discussions of diverse events and the quality and quantity of clusters were evaluated. Finally, a set of tests and criteria were defined with which the research question was addressed. The study found that the approach produced an optimal number of clusters with reasonable structure quality without requiring any domain knowledge to produce them. Although the method proposed in this study is effective in finding underlying patterns and similarities, it mainly serves to point researchers in the right direction, more detailed analysis is necessary for definite conclusions and labelled categorisation. The study recognises the prior work performed in classifying social media data and recommends that future work include a wide variety of user features, sentiment, topic, and network measures. Furthermore, the study can be expanded upon by testing alternative dimensionality reduction and clustering methods at each stage of the proposed approach. The study furthered the understanding of classifying social media data in terms of social network analysis and the various network measures and data descriptors that was discussed.