Dataset of Arabic spam and ham tweets

Sanaa Kaddoura; Safaa Henno

doi:10.1016/j.dib.2023.109904

Dataset of Arabic spam and ham tweets

Data Brief. 2023 Dec 5:52:109904. doi: 10.1016/j.dib.2023.109904. eCollection 2024 Feb.

Authors

Sanaa Kaddoura¹, Safaa Henno¹

Affiliation

¹ Zayed University, Abu Dhabi, UAE.

Abstract

This data article provides a dataset of 132421 posts and their corresponding information collected from Twitter social media. The data has two classes, ham or spam, where ham indicates non-spam clean tweets. The main target of this dataset is to study a way to classify whether a post is a spam or not automatically. The data is in Arabic language only, which makes the data essential to the researchers in Arabic natural language processing (NLP) due to the lack of resources in this language. The data is made publicly available to allow researchers to use it as a benchmark for their research in Arabic NLP. The dataset was collected using the Twitter REST API between January 27, 2021, and March 10, 2021. An ad-hoc crawler was constructed using Python programming language to collect the data. Many scientists and researchers will benefit from this dataset in the domain of cybersecurity, NLP, data science and social networking analysis.

Keywords: Classification; Cybersecurity; Deep learning; Labelled data; Machine learning; Social network analysis; Twitter.