Social Media and the COVID-19: South African and Zimbabwean Netizens’ Response to a Pandemic

Since the end of 2019, the world faced a major health crisis in the form of the Coronavirus (COVID-19) pandemic. To mitigate the impact of the pandemic, governments across the globe instituted measures such as restricting local and international travel and in many cases, ordering citizens to stay indoors. Considering the social and economic impact of these restrictions it becomes crucial to investigate internet citizens’ (netizens) perception about the precautionary measures adopted. The study is anchored in the digital public sphere theory, which treats social media applications as virtual platforms where netizens commune to share ideas and debate about issues that affect them. Social media platforms already have critical public views on the current pandemic. However, the majority of this data is unstructured and difficult to interpret. Natural language processing (NLP), on the other hand, makes the task of gathering and analysing vast amounts of textual data feasible. Extracting structured knowledge from natural language, however, comes with unique challenges due to diverse linguistic properties including abbreviation, spelling mistakes, punctuations, stop words and non-standard text. In this work, The Latent Dirichlet Allocation (LDA) algorithm was applied to tweeter data to extract topics discussed by netzens from Zimbabwe and South Africa.  The primary focus of this paper, is to comparatively explore the variety of topics that occupied twitter communities from the two countries. We examine whether or not the national identities that define and differentiate citizens of these countries also exist on Twitter as evident in the emerging topics. Furthermore, this work investigated public opinion by analysing how citizens discuss the issues around the COVID-19 pandemic on social medi

The Problem of Data Extraction in Social Media: A Theoretical Framework

In today's rapidly evolving digital landscape, the pervasive growth of social media platforms has resulted in an era of unprecedented data generation. These platforms are responsible for generating vast volumes of data on a daily basis, forming intricate webs of patterns and connections that harbor invaluable insights crucial for informed decision-making. Recognizing the significance of exploring social media data, researchers have increasingly turned their attention towards leveraging this data to address a wide array of social research issues. Unlike conventional data collection methods such as questionnaires, interviews, or focus groups, social media data presents unique challenges and opportunities, demanding specialized techniques for its extraction and analysis. However, the absence of a standardized and systematic approach to collect and preprocess social media data remains a gap in the field. This gap not only compromises the quality and credibility of subsequent data analysis but also hinders the realization of the full potential inherent in social media data. This paper aims to bridge this gap by presenting a comprehensive framework designed for the systematic extraction and processing of social media data. The proposed framework offers a clear, step-by-step methodology for the extraction and processing of social media data for analysis. In an era where social media data serves as a pivotal resource for understanding human behavior, sentiment, and societal dynamics, this framework offers a foundational toolset for researchers and practitioners seeking to harness the wealth of insights concealed within the vast expanse of social media data