@inproceedings{sandrih-2018-fingerprints,
title = "Fingerprints in {SMS} messages: Automatic Recognition of a Short Message Sender Using Gradient Boosting",
author = "{\v{S}}andrih, Branislava",
booktitle = "Proceedings of the Third International Conference on Computational Linguistics in Bulgaria (CLIB 2018)",
month = may,
year = "2018",
address = "Sofia, Bulgaria",
publisher = "Department of Computational Linguistics, Institute for Bulgarian Language, Bulgarian Academy of Sciences",
url = "https://aclanthology.org/2018.clib-1.25",
pages = "203--210",
abstract = "This paper considers the following question: Is it possible to tell who is the short message sender just by analyzing a typing style of the sender, and not the meaning of the content itself? If possible, how reliable would the judgment be? Are we leaving some kind of {``}fingerprint{''} when we text, and can we tell something about others based just on their typing style? For this purpose, a corpus of ∼ 5,500 SMS messages was gathered from one person{'}s cell phone and two gradient boost classifiers were built: first one is trying to distinguish whether the message was sent by this exact person (cell phone owner) or by someone else; second one was trained to distinguish between messages sent by some public service (e.g. parking service, bank reports etc.) and messages sent by humans. The performance of the classifiers was evaluated in the 5-fold cross-validation setting, resulting in 73.6{\%} and 99.3{\%} overall accuracy for the first and the second classifier, respectively.",
}
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<abstract>This paper considers the following question: Is it possible to tell who is the short message sender just by analyzing a typing style of the sender, and not the meaning of the content itself? If possible, how reliable would the judgment be? Are we leaving some kind of “fingerprint” when we text, and can we tell something about others based just on their typing style? For this purpose, a corpus of ∼ 5,500 SMS messages was gathered from one person’s cell phone and two gradient boost classifiers were built: first one is trying to distinguish whether the message was sent by this exact person (cell phone owner) or by someone else; second one was trained to distinguish between messages sent by some public service (e.g. parking service, bank reports etc.) and messages sent by humans. The performance of the classifiers was evaluated in the 5-fold cross-validation setting, resulting in 73.6% and 99.3% overall accuracy for the first and the second classifier, respectively.</abstract>
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%0 Conference Proceedings
%T Fingerprints in SMS messages: Automatic Recognition of a Short Message Sender Using Gradient Boosting
%A Šandrih, Branislava
%S Proceedings of the Third International Conference on Computational Linguistics in Bulgaria (CLIB 2018)
%D 2018
%8 May
%I Department of Computational Linguistics, Institute for Bulgarian Language, Bulgarian Academy of Sciences
%C Sofia, Bulgaria
%F sandrih-2018-fingerprints
%X This paper considers the following question: Is it possible to tell who is the short message sender just by analyzing a typing style of the sender, and not the meaning of the content itself? If possible, how reliable would the judgment be? Are we leaving some kind of “fingerprint” when we text, and can we tell something about others based just on their typing style? For this purpose, a corpus of ∼ 5,500 SMS messages was gathered from one person’s cell phone and two gradient boost classifiers were built: first one is trying to distinguish whether the message was sent by this exact person (cell phone owner) or by someone else; second one was trained to distinguish between messages sent by some public service (e.g. parking service, bank reports etc.) and messages sent by humans. The performance of the classifiers was evaluated in the 5-fold cross-validation setting, resulting in 73.6% and 99.3% overall accuracy for the first and the second classifier, respectively.
%U https://aclanthology.org/2018.clib-1.25
%P 203-210
Markdown (Informal)
[Fingerprints in SMS messages: Automatic Recognition of a Short Message Sender Using Gradient Boosting](https://aclanthology.org/2018.clib-1.25) (Šandrih, CLIB 2018)
ACL