It is a few months since the first reported COVID-19 case in South Africa (Kiewit, 2020) . This has been a time in the country's history that has been categorised with unprecedented government engagement. Government tries to balance their responses in taking care of the health crisis to also respond to the social and economic impacts (de Kadt, 2020) . Research continues to be done on studying the pandemic spread in South Africa (Mbuvha and Marwala, 2020; Arashi et al., 2020; Marivate and Combrink, 2020) . We take a different direction in this work in that we look at the engagement on social media as a window into some of the concerns that the public in South Africa might have.
In this work, we extract patterns of social media interactions between the South African government (through its representatives) and citizens (the public). Extracting the discussion patterns is important, even with the limitation that social media is not a representation of all parts of our society. South Africa has a 60% internet penetration and 36% of the population on any type of social media. If we concentrate on Twitter, South Africa has just over 2.3 million users on Twitter (Kemp, 2020) , out of an estimated total population of 58.8 million (Africa, 2019).
We use an empirical approach to extract topics from gathered social media data from South Africa. We analyse this data to answer the following questions: What are the most discussed issues?; How have the discussions changed over time in relation to the announced policy changes by the government?; What topics are similar and dissimilar between official and public sources?
Africa and online discussion
The COVID-19 response by the South African government has been multi-modal (briefings, announcements, question & answer sessions) as the government tries to bring citizens into their confidence (Dayile, 2020) . The Minister of Health, Dr. Zweli Mkhize, holds daily briefings on a number of topics related to the Healthcare system's response to COVID-19. The president, Cyril Ramaphosa, has held briefings before major changes (such as introducing and declaring a national state of disaster, announcing lockdown and lockdown levels etc.). The public has many opinions and reactions on the COVID-19 pandemic and interventions (by government and other sectors). There is a need to find ways to better understand these reactions. Social media analysis and traditional media analysis can be useful in extracting topics that are important in tracking a pandemic (Ghosh et al., 2017) . Analysis of media information (traditional and social) can extract what may be concerns to citizens but can also help us highlight gaps in the engagement (Dayile, 2020; Ghosh et al., 2017) . Social Media, in South Africa and beyond, has been used to engage in debate and organise groups that mobilise volunteers that assist the vulnerable (Hamann et al., 2020) or for COVID-19 data collection efforts (Marivate and Combrink, 2020) , mirroring past use in times of disaster (Chae et al., 2014) .
We collected data from Twitter that covers the periods of 01 March 2020 to May 17 2020. We collect government communication from a few official accounts (see Table 1 ). Traditional media analysis has highlighted that journalists had focused on President Ramaphosa and Dr Zweli Mkhize for comments when it comes to to COVID-19 in South Africa (Dayile, 2020). We utilised TWINT 1 to collect the data. The data collection was under ethical clearance EBIT/88/2020.
We collected 919,019 social media posts Week 0 from 189,266 unique users. The data was segmented into two subsets, TweetsOfficial and TweetsPublic. TweetsOfficial contains tweets published by accounts in Table 1 , Tweet-sPublic contains the remaining tweets that reference the accounts in Table 1 . TweetsOfficial contains 2,537 tweets from 4 unique users. TweetsOfficial contains a coherent message from government officials to the public whereas TweetsPublic contains the public response which can be incoherent and noisy due to the number of users contributing to the data.
Preliminary experiments with an unsupervised topic model built on TweetsPublic produced topics that could not be deciphered. Furthermore, the model poorly segmented the data into representative clusters. In work by (Ghosh et al., 2017; Gallagher et al., 2017) , a supervised topic modelling approach was used to seed the topic model with a manually curated set of words to guide the formulation of top-ics. Our approach aims to test whether topics created from a model trained on TweetsOfficial and supervised by curated seed words can be used to supervise the training of a model trained on TweetsPublic to identify associated topics in the larger, noisier corpus. If the Tweet-sOfficial model can effectively supervise the TweetsPublic model to find topics that are associated with TweetsOfficial topics, it will provide a reflection of the public response to the different topics of information propagated by government. The models were trained with 20 topics and trained for 100 iterations. The models were used to label tweets with a topic label to conduct analysis on the topics produced.
The topics produced by the TweetsOfficial and TweetsPublic model were very similar due to the effects of the supervised approach taken. The topics in Table 2 provide a general overview of COVID-19 themes that were central to the engagements during the observed period. The keywords were similar for both models thus they are illustrated together in one column for brevity. The models produced topics directly related to COVID-19 and heathcare; the officials from Table 1 ; the imposed lockdown; and the policies applied by government for the lockdown (alcohol and cigarette bans, school closure, job loss mitigation and hygiene guidelines).
We use topic timelines to study the temporal properties of topics. Since the topics from both models are aligned in context, the objective is to analyse spikes in volume to understand the interaction between government and the public. Key dates are highlighted to associate spikes in volume with events that transpired. Figure 2 illustrates topic timelines for lockdown-related topics. The lockdown topic is consistently discussed with spikes in volume occurring near key dates. Official communication spikes in volume at the start of lockdown but receives limited coverage elsewhere. The cigarettes topic receives spikes in public attention near key dates, most notably in the period between level 4 being announced and being active because government changed their decision to allow cigarette sales in level 4 lockdown in that period. Official tweets don't appear to focus on this topic. The other topics experience spikes aligned to the lockdown topic. Figure 3 illustrates topic timelines for topics related to public officials, personal protective equipment (PPE) and fake news. Public en- Figure 3 : Official vs Public Topics on Other Issues gagement towards key officials consistently occurred throughout with spikes in volume near key dates. The spike in volume before the announcement of level 4 lockdown can be attributed to the public's eagerness for the lockdown to be eased. The PPE/healthcare topic receives low volumes of attention but is consistently present in the public discourse. The fake news topic peaks alongside the public official topic.
As a result of the supervised topic modelling approach, the TweetsOfficial and TweetsPublic models had topics described by very similar keywords. We use topic similarity heatmaps to analyse the syntactic similarity between topics from different models. The method consists of creating sub-corpora for each topic from each model, train a vectorizer on the union of a pair of sub-corpora, one from each model. Thereafter, a heatmap illustrates the pairwise cosine similarity of two models. Figure 4 illustrates the topic similarity heatmap for the official and public topics. The strong similarity seen on the diagonal of the heatmap indicates that related topics from each model have a strong syntactic similarity to each other. The 'heat' seen in most of the heatmap Figure 4 : Topic similarity heatmap is a result of the data covering a limited domain of public engagements with government officials related to the pandemic. The very high similarity seen for topics 2 (Case Reports), 3(Testing/Screening) and 12(PPE/Heathcare) are likely due to these topics being informative in nature resulting in the scope for public opinion being limited.
The topics produced highlight central themes in the discourse between government officials and the public. The topics address themes such as lockdown and the restrictions put in place; general information on COVID-19.The topic timelines indicated that the public attempted to engage more near key dates in the observed period. Peaks in volume provided insights into when certain topics received attention. The high syntactic similarity seen between related topics from each model indicates that the supervised topic modelling approach taken can be used to find targeted insights from a larger noisier data set by seeding the training process with a smaller and more coherent data set that relates to the domain being studied. The application of this approach can yield informative insights about the interactions between government officials and the public. Furthermore, the topics can be used as features in downstream supervised tasks aimed at stronger citizen engagement on social media.