@inproceedings{desmet-hoste-2014-recognising,
title = "Recognising suicidal messages in {D}utch social media",
author = "Desmet, Bart and
Hoste, V{\'e}ronique",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Loftsson, Hrafn and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
month = may,
year = "2014",
address = "Reykjavik, Iceland",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/597_Paper.pdf",
pages = "830--835",
abstract = "Early detection of suicidal thoughts is an important part of effective suicide prevention. Such thoughts may be expressed online, especially by young people. This paper presents on-going work on the automatic recognition of suicidal messages in social media. We present experiments for automatically detecting relevant messages (with suicide-related content), and those containing suicide threats. A sample of 1357 texts was annotated in a corpus of 2674 blog posts and forum messages from Netlog, indicating relevance, origin, severity of suicide threat and risks as well as protective factors. For the classification experiments, Naive Bayes, SVM and KNN algorithms are combined with shallow features, i.e. bag-of-words of word, lemma and character ngrams, and post length. The best relevance classification is achieved by using SVM with post length, lemma and character ngrams, resulting in an F-score of 85.6{\%} (78.7{\%} precision and 93.8{\%} recall). For the second task (threat detection), a cascaded setup which first filters out irrelevant messages with SVM and then predicts the severity with KNN, performs best: 59.2{\%} F-score (69.5{\%} precision and 51.6{\%} recall).",
}
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<abstract>Early detection of suicidal thoughts is an important part of effective suicide prevention. Such thoughts may be expressed online, especially by young people. This paper presents on-going work on the automatic recognition of suicidal messages in social media. We present experiments for automatically detecting relevant messages (with suicide-related content), and those containing suicide threats. A sample of 1357 texts was annotated in a corpus of 2674 blog posts and forum messages from Netlog, indicating relevance, origin, severity of suicide threat and risks as well as protective factors. For the classification experiments, Naive Bayes, SVM and KNN algorithms are combined with shallow features, i.e. bag-of-words of word, lemma and character ngrams, and post length. The best relevance classification is achieved by using SVM with post length, lemma and character ngrams, resulting in an F-score of 85.6% (78.7% precision and 93.8% recall). For the second task (threat detection), a cascaded setup which first filters out irrelevant messages with SVM and then predicts the severity with KNN, performs best: 59.2% F-score (69.5% precision and 51.6% recall).</abstract>
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%0 Conference Proceedings
%T Recognising suicidal messages in Dutch social media
%A Desmet, Bart
%A Hoste, Véronique
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Loftsson, Hrafn
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)
%D 2014
%8 May
%I European Language Resources Association (ELRA)
%C Reykjavik, Iceland
%F desmet-hoste-2014-recognising
%X Early detection of suicidal thoughts is an important part of effective suicide prevention. Such thoughts may be expressed online, especially by young people. This paper presents on-going work on the automatic recognition of suicidal messages in social media. We present experiments for automatically detecting relevant messages (with suicide-related content), and those containing suicide threats. A sample of 1357 texts was annotated in a corpus of 2674 blog posts and forum messages from Netlog, indicating relevance, origin, severity of suicide threat and risks as well as protective factors. For the classification experiments, Naive Bayes, SVM and KNN algorithms are combined with shallow features, i.e. bag-of-words of word, lemma and character ngrams, and post length. The best relevance classification is achieved by using SVM with post length, lemma and character ngrams, resulting in an F-score of 85.6% (78.7% precision and 93.8% recall). For the second task (threat detection), a cascaded setup which first filters out irrelevant messages with SVM and then predicts the severity with KNN, performs best: 59.2% F-score (69.5% precision and 51.6% recall).
%U http://www.lrec-conf.org/proceedings/lrec2014/pdf/597_Paper.pdf
%P 830-835
Markdown (Informal)
[Recognising suicidal messages in Dutch social media](http://www.lrec-conf.org/proceedings/lrec2014/pdf/597_Paper.pdf) (Desmet & Hoste, LREC 2014)
ACL
- Bart Desmet and Véronique Hoste. 2014. Recognising suicidal messages in Dutch social media. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 830–835, Reykjavik, Iceland. European Language Resources Association (ELRA).