## 3 Research Design
### Social Media Analytics  
Social media data are increasingly used in disaster-related research [62; 63; 64; 36] due to the advantages of data availability and flexibility in data collection. Therefore, as the first methodological approach, this study analysed social media data to understand community perceptions about the prevailing bushfire disaster education.  
Among many social media platforms, Twitter data were used in the analysis undertaken due to the following reasons: (a) Twitter has become the fastest growing social media platform since its beginning in 2006; (b) Twitter offers application programming interface (API) access to researchers and practitioners to conduct studies based on their interests [65]; (c) Compared to Facebook and Instagram, Twitter data are considered 'open data', which provides succinct real-time data to the public [66]; and (d) Searching and streaming the APIs of Twitter permits researchers to write queries and download data based on specific keywords.  
Accordingly, Twitter messages circulated from August 2019 to January 2020--during the Australian Black Summer bushfires--with the keywords of bushfire, education, plan, awareness, prepare, knowledge, disaster, information, learn, and lessons were downloaded. In total, 174,158 tweets were downloaded. Following this, the five-step data cleaning process introduced by [4] was adopted--time zone filter, date filter, bot filter, relevance filter, and text filter to clean the data. Text filtering was done at the time the data were downloaded through a Twitter API using a set of keywords. Among them, the first two filtering steps--the time zone filter and date filter--were completed at the time the data  
Figure 2: Overall methodological framework.  
were downloaded. The other two filtering steps--the bot filter (removal of automated messages) and relevance filter (removal of irrelevant meanings)--were applied later. All the filtering was conducted using a Python programming software and a macro-enabled Excel sheet. Consequently, 13,683 tweets were identified as being relevant to the research theme.  
The cleaned data were analysed in four stages to identify community perceptions about the prevailing bushfire education in Australia. Firstly, a word-tree diagram was created using NVivo software to understand the main perception clusters existing around the words which are synonyms for education. A word-tree diagram provides an opportunity for the reader to study the ways that a particular word or phrase is used in a text or a particular context. It further expresses broad patterns of word clusters [67]. Accordingly, a word-tree diagram created for the term 'education' and related words will show all instances of education and its synonyms within the tweets circulated during the 2019 Australian bushfires.  
Secondly, a sentiment analysis was conducted to identify the emotions of the community related to bushfire disaster education. All the tweets were categorized into four emotional categories from strong positive emotions to strong negative emotions. Thirdly, word-cloud analysis was conducted to identify the keywords in each emotional category. Finally, based on the word-cloud analysis, the main attitudes in each emotional category were derived.