There were 17 times more tweets about NFTs than WFH in 2021, with a 75 per cent growth in “#FinTwit”. Communities of experts and everyday people are joining forces to share knowledge, sparking a 185 per cent increase in financial literacy discussions.Ĭonversations about money have evolved, and concepts like decentralised finance (hello, crypto!) and digital assets have entered pop culture with ‘NFT and Bollywood’, ‘Bollycoin’, and ‘Beyond Life’ emerging as topics to watch out for. The report mentions a 78 per cent year-on-year increase in finance-related tweets. There was a 320 per cent growth in topics like passive income, NFT collaborations,fashion and collectibles, NFT avatars and crypto-gaming. The fandom space also saw double the numbers in Tweet replies with niche fandoms like ‘#crickettwitter’ growing by 55 per cent. Further research in this area is warranted to enhance decision-making processes and resource allocation in the fight against obesity and related health issues.There was a 994 per cent year-on-year growth in “fan tokens” conversations and a 47 per cent increase in discussions around fandoms. It also demonstrates how social media data can be leveraged to improve public health policies beyond soda taxes. Overall, this research highlights the untapped potential of AI in understanding public sentiment and its influence on policy-making. They suggested that analyzing social media sentiments could help in designing, implementing, and modifying soda tax policies while minimizing misinterpretations and confusion. As this post is regarding Twitter trends, so you must be here for looking the. The researchers emphasized the potential of social media analysis in informing and shaping public health policies. The study also revealed that factors such as the number of total tweets posted, number of followers, and number of retweets predicted tweet sentiment. The prevalence of positive sentiment remained relatively unchanged. The prevalence of tweets expressing a neutral sentiment towards soda taxes increased, while the prevalence of negative sentiment rose steadily until 2019 and then leveled off. The attention paid to soda taxes on Twitter peaked in 2016 but decreased significantly thereafter. The findings provided insights into the evolving public sentiment on soda taxes. Additionally, a random forest classifier model was used to assess the factors associated with each sentiment category. This model was then used to classify over 370,000 tweets based on sentiments and track their annual trends. Using these labeled tweets, the researchers trained a natural language processing (NLP) model with an accuracy of 88% and an F1 score of 0.87. The tweets were then manually labeled into four categories: positive, negative, neutral, and linking to news. They developed a search algorithm to identify and collect tweets related to soda taxes from January 2015 to April 2022. The researchers turned to Twitter as a platform to gauge public sentiment on soda taxes. However, the success of these policies depends heavily on public opinion. Soda consumption has long been linked to obesity, and soda taxes have been proposed as a solution. A recent study titled “Sentiment Analysis of Tweets on Soda Taxes” has utilized artificial intelligence (AI) and public health research to analyze public sentiment towards soda taxes in the United States.
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