This subsection further analyses community sentiment dynamics in LGAs in NSW. Figure 2 shows an example of community sentiment dynamics in LGAs in NSW on different 2 days: 10 March 2020 and 16 March 2020. It shows that the community sentiment were different across different LGAs on each day. Furthermore, the community sentiment of each LGA was changed on different days. When we zoom in the sentiment map to LGAs around Sydney City areas as shown in Fig. 3, we can see more details of sentiment changes of LGAs around Sydney City areas on these 2 days. For example, Ryde is an LGA close to Sydney City. In an aged care centre in this LGA, a nurse and an 82-year-old elderly resident were tested positive for coronavirus at the beginning of March.3 After that, a number of elderly residents in this aged care centre were tested positive for COVID-19 or even died. At the same time, a childcare centre and a hospital in this LGA have been reported positive COVID-19 cases in March. Many staff from the childcare centre and the hospital were asked to test virus and conduct home isolation for 14 days. All these maybe resulted in public sentiment changes significantly as shown in Fig. 3. For example, the sentiment in Ryde LGA was changed from high positive to high negative on 10 March 2020 and 16 March 2020.
Figure 4 presents the community sentiment dynamics in Ryde LGA over the study period. It shows that the community experienced frequent negative sentiment in March, and recovered to positive dominantly in April. This is maybe because the COVID-19 spread situation in March as mentioned previously in this LGA and the spread was controlled in April, which helped people boost positive sentiment.
We analysed hot topics in tweets labelled with hashtag. The sentiment of each tweet is got using the VADER tool as described previously. If the sentiment of a tweet is positive, the topic key words labelled with hashtag in this tweet are then labelled as positive sentiment. The topic key words with the negative sentiment are got with the similar approach. Different topic key words are then analysed statistically to understand the sentiment dynamics. Fig. 5 shows the dynamics of top 20 Twitter topics during the study period in NSW. Y-axis represents the count of different topic key words occurred in tweets. Positive numbers represent count of topic key words with positive sentiment, and negative numbers represent count of topic key words with negative sentiment. This is same for other figures from Figs. 6, 7, 8 and 9.
Figure 5 shows that COVID is the dominant topic in the study period from the week of 2 March. When we drill down into details of this figure, we can see that the occurrence of the topic of COVID appeared from the week of 20 January, and began to increase significantly from the week of 2 March. It reached the peak in the week of 23 March. After that, the occurrence of the topic of COVID decreased gradually and kept stable from the week of 20 April. This trend is well aligned with the trend of COVID-19 situation in NSW as shown in Fig. 1a. Furthermore, Fig. 5 shows that the dominant sentiment around the topic of COVID was positive. This conclusion is aligned with findings in previous research. This is maybe because that people had a positive belief to the combat against COVID-19 by the government and the society.
Figure 6 shows the dynamics of Twitter topic of lockdown. From this figure, we can see that the topic of lockdown was started from the week of 9 March and reached to the peak point at the week of 23 March. On that week, NSW government officially announced the state lockdown on 30 March and the restrictions were begun from 31 March.4 After that, this topic was kept stable until the week of 27 April. From the week of 4 May, there was a small peak of the topic occurrences. Maybe this was because that the NSW government announced the ease of restrictions on 10 May.5 Overall, the community kept a positive sentiment dominantly to lockdown despite negative sentiment existing. This was maybe because that some people were not accustomed to the restrictions which caused negative sentiment.
Figure 7 presents the dynamics of Twitter topic of social-distancing. It shows that the topic of social-distancing appeared from the week of 9 March and significantly increased to a peak after a week. This is maybe because that the confirmed cases of COVID-19 were increased significantly and the government encouraged people to increase social-distancing, which caused a significant increase of public conversation on social-distancing on Twitter. After that week, the occurrence of the topic of social-distancing was gradually decreased until the week of 6 April and then kept stable relatively. Overall, the community showed the positive sentiment dominantly from the beginning of social-distancing until the week of 11 May. This is maybe because that the NSW government announced the ease of restrictions on 10 May as mentioned previously, which resulted in the negative sentiment to the social-distancing from the community.
The Australian Government introduced the Jobkeeper wage subsidy program at the end of March, allowing businesses to claim a fortnightly payment of $1500 per eligible employee from 30 March 2020, for a maximum of 6 months.6 The starting time of jobkeeper topic was also clearly demonstrated in the dynamics of Twitter topic of jobkeeper as shown in Fig. 8. This figure shows that the public kept a positive sentiment dominantly to jobkeeper program from the beginning of its introduction until the week of 27 April. From the week of 4 May, people showed a negative sentiment dominantly. This is maybe because that people had received negative news on operations of jobkeeper program. For example, it was reported that the number of people on jobkeeper was revised down by 3 million due to the errors at the beginning of the program.7
Community sentiment may also be affected by big events during the COVID-19 period. For example, the Ruby Princess Cruise docked in Sydney Harbour on 18 March 2020. Despite having passengers with COVID-19 symptoms, about 2700 passengers were allowed to disembark on 19 March without isolation or other measures, which was considered to create a coronavirus hotbed in Australia. The Ruby Princess Cruise has been linked to at least 662 confirmed cases and 22 deaths of COVID-19.8
Figure 9 presents the community sentiment dynamics related to the Ruby Princess. It shows that the topic of the Ruby Princess appeared from the week of 16 March and was increased significantly after that. It reached a peak at the week of 30 March and kept high occurrences until the beginning of May. This trend is well aligned with the actual timeline of the public reporting of confirmed cases and deaths as well as other events (e.g. police in NSW announced a criminal investigation into the Ruby Princess cruise ship debacle on 5 April) related to the Ruby Princess. Overall, the community showed a dominant negative sentiment to Ruby Princess throughout the event period.