Document Type
Article
Source of Publication
Data in Brief
Publication Date
2-1-2024
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.
DOI Link
ISSN
Publisher
Elsevier BV
Volume
52
Disciplines
Computer Sciences
Keywords
Classification, Cybersecurity, Deep learning, Labelled data, Machine learning, Social network analysis, Twitter
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Kaddoura, Sanaa and Henno, Safaa, "Dataset of Arabic spam and ham tweets" (2024). All Works. 6241.
https://zuscholars.zu.ac.ae/works/6241
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Hybrid: This publication is openly available in a subscription-based journal/series