Summary of the paper

Title Recognising Suicidal Messages in Dutch Social Media
Authors Bart Desmet and Véronique Hoste
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).
Topics Document Classification, Text categorisation, Profiling
Full paper Recognising Suicidal Messages in Dutch Social Media
Bibtex @InProceedings{DESMET14.597,
  author = {Bart Desmet and Véronique Hoste},
  title = {Recognising Suicidal Messages in Dutch Social Media},
  booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)},
  year = {2014},
  month = {may},
  date = {26-31},
  address = {Reykjavik, Iceland},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {978-2-9517408-8-4},
  language = {english}
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