The raw data used for the analysis was derived from the Reddit post "No-kill, lab-grown meat to go on sale for first time. Singapore’s approval of chicken cells grown in bioreactors is seen as landmark moment across industry". The Python Reddit API Wrapper (PRAW) library was used to scrape the comments. After cleaning the data by removing stopwords and nonsensical comments, 448 comments were gathered before generating a word cloud as seen in Figure 1. We can see from the word cloud that there are concerns pertinent to “taste”, “waste”, “better”, “price” and “suffering”.

The data was then analysed by applying VADER (Valence Aware Dictionary and sEntiment Reasoner) sentiment analysis, which is a tool specifically attuned to sentiments on social media comments. In short, the VADER tool determines how positive or negative a particular comment is. The percentage of comments by sentiment type (Positive/Neutral/Negative) is seen in Table 1, and the distribution of the VADER scores are represented in Figure 2. We can see that the scores are more positive than negative.

Topic modelling was also conducted to try and group different comments and classify them under a single topic as can be seen from Figure 3. Some broadly-defined topics can be centred around the ethicality (zombie, animal, farm), taste (eat, taste, vegan, food, tofu), future of lab-grown meat (future, cost, year, time) and the industrialisation (risk, industry, tyson, company).