Data Preview ? 199K
Data Structure ?
Tiago A. Almeida (talmeida
Data Set Information:
This corpus has been collected from free or free for research sources at the Internet:
-> A collection of 425 SMS spam messages was manually extracted from the Grumbletext Web site. This is a UK forum in which cell phone users make public claims about SMS spam messages, most of them without reporting the very spam message received. The identification of the text of spam messages in the claims is a very hard and time-consuming task, and it involved carefully scanning hundreds of web pages. The Grumbletext Web site is: [Web link].
-> A subset of 3,375 SMS randomly chosen ham messages of the NUS SMS Corpus (NSC), which is a dataset of about 10,000 legitimate messages collected for research at the Department of Computer Science at the National University of Singapore. The messages largely originate from Singaporeans and mostly from students attending the University. These messages were collected from volunteers who were made aware that their contributions were going to be made publicly available. The NUS SMS Corpus is avalaible at: [Web link].
-> A list of 450 SMS ham messages collected from Caroline Tag's PhD Thesis available at [Web link].
-> Finally, we have incorporated the SMS Spam Corpus v.0.1 Big. It has 1,002 SMS ham messages and 322 spam messages and it is public available at: [Web link]. This corpus has been used in the following academic researches:
 G?3mez Hidalgo, J.M., Cajigas Bringas, G., Puertas Sanz, E., Carrero Garc?-a, F. Content based SMS Spam Filtering. Proceedings of the 2006 ACM Symposium on document Engineering (ACM DOCENG'06), Amsterdam, The Netherlands, 10-13, 2006.
 Cormack, G. V., G?3mez Hidalgo, J. M., and Puertas S??nz, E. Feature engineering for mobile (SMS) spam filtering. Proceedings of the 30th Annual international ACM Conference on Research and Development in information Retrieval (ACM SIGIR'07), New York, NY, 871-872, 2007.
 Cormack, G. V., G?3mez Hidalgo, J. M., and Puertas S??nz, E. Spam filtering for short messages. Proceedings of the 16th ACM Conference on Information and Knowledge Management (ACM CIKM'07). Lisbon, Portugal, 313-320, 2007.
The collection is composed by just one text file, where each line has the correct class followed by the raw message. We offer some examples bellow:
ham What you doing?how are you?
ham Ok lar... Joking wif u oni...
ham dun say so early hor... U c already then say...
ham MY NO. IN LUTON 0125698789 RING ME IF UR AROUND! H*
ham Siva is in hostel aha:-.
ham Cos i was out shopping wif darren jus now n i called him 2 ask wat present he wan lor. Then he started guessing who i was wif n he finally guessed darren lor.
spam FreeMsg: Txt: CALL to No: 86888 & claim your reward of 3 hours talk time to use from your phone now! ubscribe6GBP/ mnth inc 3hrs 16 stop?txtStop
spam Sunshine Quiz! Win a super Sony DVD recorder if you canname the capital of Australia? Text MQUIZ to 82277. B
spam URGENT! Your Mobile No 07808726822 was awarded a L2,000 Bonus Caller Prize on 02/09/03! This is our 2nd attempt to contact YOU! Call 0871-872-9758 BOX95QU
Note: the messages are not chronologically sorted.
We offer a comprehensive study of this corpus in the following paper. This work presents a number of statistics, studies and baseline results for several machine learning methods.
Almeida, T.A., G?3mez Hidalgo, J.M., Yamakami, A. Contributions to the Study of SMS Spam Filtering: New Collection and Results. Proceedings of the 2011 ACM Symposium on document Engineering (DOCENG'11), Mountain View, CA, USA, 2011.
If you find this dataset useful, you make a reference to our paper and the web page: [Web link] in your papers, research, etc;
Send us a message to talmeida