Case Study: Hamad Bin Khalifa University improves Arabic tweet annotation accuracy with aiXplain

Enhancing Social Sciences Research with Data Analysis through Arabic Tweets

Hamad Bin Khalifa University needed to perform data analysis for social sciences research using labeled Arabic tweets. Their challenge was ensuring the quality and consistency of annotating approximately 20,000 tweets for emotions and offensive content, a task complicated by the diverse dialects found in the Arabic language.

aiXplain implemented an automated annotation solution that used an inter-annotator agreement (IAA) process to identify top-performing annotators. This significantly boosted the accuracy of the labeled dataset, providing the university with reliable data for its research. The solution also saved the project valuable time and resources by optimizing the workflow and task management.

Hamad Bin Khalifa University

Wajdi Zaghouani

Associate Professor in Digital Humanities