This ExAIS_SMS Spam dataset was a project conducted at the Federal University of Agriculture, Abeokuta, Nigeria with the aim of building an indigenous SMS Spam corpus with African-English context. The dataset was collected from the above stated community with the consent of each particpant for the purpose of contributing and developing SMS corpus to aid further detection of SMS spam.
The SMS dataset collected from 20 users of different mobile devices within the University community from both students, faculty member and non-teaching staff within the age group of 20-50 years old. A total of 5,240 SMS messages which consist of 2,350 Spam messages and 2,890 Ham (non-spam) messages was collected. Aside from the textual content, their are also some metadata included in the data such as users’ contact list, time stamp, date, sender's details, and their corresponding SMS messages.
What am we trying to achieve with this SMS? This repository contains the raw SMS data. However, we have tried to mask some vital informations within the SMS such as bank account, phone numbers with the SMS, with the aim of ensuring users private details safety.
The total number of SMS messages for each participant (in CSV) and their metadata is as follows:
• User 1 contains 53 messages.
• User 2 contains 130 messages.
• User 3 contains 23 messages.
• User 4 contains 182 messages.
• User 5 contains 122 messages.
• User 6 contains 116 messages.
• User 7 contains 235 messages.
• User 8 contains 34 messages.
• User 9 contains 49 messages.
• User 10 contains 633 messages.
• User 11 contains 243 messages.
• User 12 contains 387 messages.
• User 13 contains 117 messages.
• User 14 contains 227 messages.
• User 15 contains 605 messages.
• User 1 6contains 1082 messages.
• User 17 contains 180 messages.
• User 18 contains 167 messages.
• User 19 contains 626 messages.
• User 20 contains 29 messages.
You can read the article: "An Adaptive and Collaborative Server-Side SMS"Spam Filtering Scheme Using Artificial Immune System".
Cite article for this paper as: Onashoga, A. S., Abayomi-Alli, O. O., Sodiya, A. S., & Ojo, D. A. (2015). An Adaptive and Collaborative Server-Side SMS Spam Filtering Scheme Using Artificial Immune System. Information Security Journal: A Global Perspective, 24(4-6), 133-145.