SMS Spam Collection

Donated on 6/21/2012

The SMS Spam Collection is a public set of SMS labeled messages that have been collected for mobile phone spam research.

Dataset Characteristics

Multivariate, Text, Domain-Theory

Subject Area

Computer Science

Associated Tasks

Classification, Clustering

Feature Type

Real

# Instances

5574

# Features

-

Dataset Information

Additional 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: http://www.grumbletext.co.uk/. -> 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: http://www.comp.nus.edu.sg/~rpnlpir/downloads/corpora/smsCorpus/. -> A list of 450 SMS ham messages collected from Caroline Tag's PhD Thesis available at http://etheses.bham.ac.uk/253/1/Tagg09PhD.pdf. -> 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: http://www.esp.uem.es/jmgomez/smsspamcorpus/. This corpus has been used in the following academic researches: [1] Gómez Hidalgo, J.M., Cajigas Bringas, G., Puertas Sanz, E., Carrero García, F. Content Based SMS Spam Filtering. Proceedings of the 2006 ACM Symposium on Document Engineering (ACM DOCENG'06), Amsterdam, The Netherlands, 10-13, 2006. [2] Cormack, G. V., Gómez Hidalgo, J. M., and Puertas Sánz, 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. [3] Cormack, G. V., Gómez Hidalgo, J. M., and Puertas Sánz, 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.

Has Missing Values?

No

Introductory Paper

Contributions to the study of SMS spam filtering: new collection and results

By Tiago A. Almeida, J. M. G. Hidalgo, A. Yamakami. 2011

Published in ACM Symposium on Document Engineering

Variable Information

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.

Papers Citing this Dataset

An Immunological-Based Simulation: A Case Study of Risk Concentration for Mobile Spam Context Assessment

By Kamahazira Zainal, Mohd Jali. 2018

Published in International Journal on Advanced Science, Engineering and Information Technology.

Efficient mixture model for clustering of sparse high dimensional binary data

By Marek 'Smieja, Krzysztof Hajto, Jacek Tabor. 2017

Published in ArXiv.

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3 citations
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Creators

Tiago Almeida

Jos Hidalgo

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