Abstract
The Twitter platform is appealing to researchers due to the ease of obtaining data and the ability to analyze and produce results rapidly. However, sampling Twitter data for research purposes needs to be regulated to produce unbiased results. In this paper, factors that lead to sampling bias are addressed, case studies that have been encountered are presented, and an approach is proposed to reduce sampling bias and flaws in datasets collected from Twitter. Then, experiments are conducted on two case studies, and a larger dataset is achieved by following the proposed guideline. The results indicate that using multiple Twitter application programming interfaces (APIs) for data collection is the best way to obtain a randomly sampled dataset.
Abdulaziz Alhamadani عبدالعزيز الهمداني
PhD Candidate
My research interests include ML applications, text classification, event detection, pandemic forecasting, and ethical AI.