Munyoka, WillardOchara, Nixon MugandaRamaru, Rifhiwa Rodney2025-02-192025-02-192024-09-06Ramaru, R. R. 2023. A Sense-making Perspective of Algorithmic Accountability during Infodemics. Thohoyandou, South Africa.<https://univendspace.univen.ac.za/handle/11602/2807>.https://univendspace.univen.ac.za/handle/11602/2807M. Com (Business Information SystemsDepartment of Business Information SystemsDuring the pandemic era, enormous amounts of data were available to decision-makers. Much of the data was generated using algorithms in which people did not want to be held accountable for any wrong/false information (misinformation) that was being provided to the public, either on internet websites, social media or television and radios. Algorithmic Accountability examines the process of assigning responsibility for harm when algorithmic decision-making results in discriminatory and inequitable outcomes. Mis-and disinformation about science, technology, and health is neither new nor unique to the COVID-19 era. Amid an unprecedented global health crisis, many journalists, policy makers, and academics have echoed what World Health Organisation has stressed that misinformation about the pandemic presents a serious risk to public health and public action. The biggest challenge is that this misinformation or fake news is drowning official public health advice on COVID-19, making it extremely problematic for the voices of healthcare professionals to be heard; the implications of this may be enormous as it may cause the virus and other pandemics to spread more rapidly within diverse populations. The purpose of this study was to establish the extent of Misinformation during Infodemics, to address the lack of Algorithmic Accountability. A mixed-methods approach was used as the topic required a purposeful mixing of methods in data collection, data analysis, and interpretation of the evidence. The key word is ‘mixed’; this explains that an essential step in this approach is data linkage or integration at appropriate stages in the research process. The researcher used Spyder Python for extracting data from the Twitter API and then used Feedly which is a news aggregator application for various web browsers and mobile devices running iOS and Android; this procedure is also available as a cloud-based service. Python was used to compile news feeds from a variety of online sources for the researcher to customize and share with others; after filtering for the Covid-19 Tweets there were 21 of 508 records. The analysis suggests that misinformation about COVID-19 comes in many different forms, from many different sources, and makes many different claims. The process of misinformation often rearranges existing or accurate content relatively than formulated on a large scale, and where it is manipulated, it is edited with simple tools. Given the breadth of the pandemic, independent media and actions by platforms and others played a vital role in addressing virus-related misinformation. Government websites and the World Health Organization (WHO) can help sort false from true material, and accurate from misleading assertations. With running topic modelling for the data consisting of 21 of 508 tweets, topic modelling provided a way to compress the big data qualitative phase of the research.1 online resource ( ii, 37 leaves)enUniversity of VendaInfodemicsUCTDMisinformationAlgorithmic AccountabilitySense-makingCOVID-19Algorithmic decision-making systemsAlgorithmic transparencyDissertationRamaru RR. A Sense-making Perspective of Algorithmic Accountability during Infodemics. []. , 2024 [cited yyyy month dd]. Available from:Ramaru, R. R. (2024). <i>A Sense-making Perspective of Algorithmic Accountability during Infodemics</i>. (). . Retrieved fromRamaru, Rifhiwa Rodney. <i>"A Sense-making Perspective of Algorithmic Accountability during Infodemics."</i> ., , 2024.TY - Dissertation AU - Ramaru, Rifhiwa Rodney AB - During the pandemic era, enormous amounts of data were available to decision-makers. Much of the data was generated using algorithms in which people did not want to be held accountable for any wrong/false information (misinformation) that was being provided to the public, either on internet websites, social media or television and radios. Algorithmic Accountability examines the process of assigning responsibility for harm when algorithmic decision-making results in discriminatory and inequitable outcomes. Mis-and disinformation about science, technology, and health is neither new nor unique to the COVID-19 era. Amid an unprecedented global health crisis, many journalists, policy makers, and academics have echoed what World Health Organisation has stressed that misinformation about the pandemic presents a serious risk to public health and public action. The biggest challenge is that this misinformation or fake news is drowning official public health advice on COVID-19, making it extremely problematic for the voices of healthcare professionals to be heard; the implications of this may be enormous as it may cause the virus and other pandemics to spread more rapidly within diverse populations. The purpose of this study was to establish the extent of Misinformation during Infodemics, to address the lack of Algorithmic Accountability. A mixed-methods approach was used as the topic required a purposeful mixing of methods in data collection, data analysis, and interpretation of the evidence. The key word is ‘mixed’; this explains that an essential step in this approach is data linkage or integration at appropriate stages in the research process. The researcher used Spyder Python for extracting data from the Twitter API and then used Feedly which is a news aggregator application for various web browsers and mobile devices running iOS and Android; this procedure is also available as a cloud-based service. Python was used to compile news feeds from a variety of online sources for the researcher to customize and share with others; after filtering for the Covid-19 Tweets there were 21 of 508 records. The analysis suggests that misinformation about COVID-19 comes in many different forms, from many different sources, and makes many different claims. The process of misinformation often rearranges existing or accurate content relatively than formulated on a large scale, and where it is manipulated, it is edited with simple tools. Given the breadth of the pandemic, independent media and actions by platforms and others played a vital role in addressing virus-related misinformation. Government websites and the World Health Organization (WHO) can help sort false from true material, and accurate from misleading assertations. With running topic modelling for the data consisting of 21 of 508 tweets, topic modelling provided a way to compress the big data qualitative phase of the research. DA - 2024-09-06 DB - ResearchSpace DP - Univen KW - Infodemics KW - Misinformation KW - Algorithmic Accountability KW - Sense-making KW - COVID-19 KW - Algorithmic decision-making systems KW - Algorithmic transparency LK - https://univendspace.univen.ac.za PY - 2024 T1 - A Sense-making Perspective of Algorithmic Accountability during Infodemics TI - A Sense-making Perspective of Algorithmic Accountability during Infodemics UR - ER -