Jdkong/Nowcasting_Unemployment

Nowcasting Unemployment Rate Using Twitter Data: The Case of South Africa # Nowcasting Unemployment Rate Using Twitter Data: The Case of South Africa This repository includes the dataset of our manuscript: ZM Nia, A Asgary, N Bragazzi, B Mellado, J Orbinski, J Wu, J Kong (2022) Nowcasting Unemployment Rate Using Twitter Data: The Case of South Africa, Frontiers in Public Health, doi: 10.3389/fpubh.2022.952363 This dataset includes 1182632 tweet IDs, their sentiment classes, and their sentiment scores: - TweetID: every tweet has a unique ID that could be used by Twitter API to retrieve the the text and other metadata. - Sentiment: indicates whether the tweet has a positive, neutral, or negative sentiment. - Score: gives a number between [-1, 1] to the tweet based on its sentiment. Tweets with positive, neutral, and negative sentiments have a score close to 1, 0, and -1, respectively. In compliance with Twitter developer's term of use and privacy policy, only the tweet IDs are shared with public. To find other meta-inforamtion such as date, geo-information, and author-information, the tweets need to be hydrated. Since the dataset is very large, it is divided into three files: - dataset1.csv: includes 394210 tweets posted from June 8, 2010 to February 22, 2017. - dataset2.csv: includes 394210 tweets posted from February 22, 2017 to October 22, 2019. - dataset3.csv: includes 394212 tweets posted from October 22, 2019 to December 23, 2021. If you use this dataset for your work, please kindly cite our article: ZM Nia, A Asgary, N Bragazzi, B Mellado, J Orbinski, J Wu, J Kong (2022) Nowcasting Unemployment Rate Using Twitter Data: The Case of South Africa, Frontiers in Public Health, doi: 10.3389/fpubh.2022.952363