Automatic Identification of Suicide Notes with a Transformer-based Deep Learning ModelCitation formats

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Automatic Identification of Suicide Notes with a Transformer-based Deep Learning Model. / Zhang, Tianlin; Schoene, Annika; Ananiadou, Sophia.

In: Internet interventions, 22.06.2021.

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@article{f52d4db6230a495e9b0650543ddf8328,
title = "Automatic Identification of Suicide Notes with a Transformer-based Deep Learning Model",
abstract = "Suicide is one of the leading causes of death worldwide. At the same time, the widespread use of social media has led to an increase in people posting their suicide notes online. Therefore, designing a learning model that can aid the detection of suicide notes online is of great importance. However, current methods cannot capture both local and global semantic features. In this paper, we propose a transformer-based model named TransformerRNN, which can effectively extract contextual and long-term dependency information by using a transformer encoder and a Bi-directional Long Short-Term Memory (BiLSTM) structure. We evaluate our model with baseline approaches on a dataset collected from online sources (including 659 suicide notes, 431 last statements, and 2000 neutral posts). Our proposed TransformerRNN achieves 95.0%, 94.9% and 94.9% performance in P, R and F1-score metrics respectively and therefore outperforms comparable machine learning and state-of-the-art deep learning models. The proposed model is effective for classifying suicide notes, which in turn, may help to develop suicide prevention technologies for social media.",
author = "Tianlin Zhang and Annika Schoene and Sophia Ananiadou",
year = "2021",
month = jun,
day = "22",
language = "English",
journal = "Internet interventions",
issn = "2214-7829",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Automatic Identification of Suicide Notes with a Transformer-based Deep Learning Model

AU - Zhang, Tianlin

AU - Schoene, Annika

AU - Ananiadou, Sophia

PY - 2021/6/22

Y1 - 2021/6/22

N2 - Suicide is one of the leading causes of death worldwide. At the same time, the widespread use of social media has led to an increase in people posting their suicide notes online. Therefore, designing a learning model that can aid the detection of suicide notes online is of great importance. However, current methods cannot capture both local and global semantic features. In this paper, we propose a transformer-based model named TransformerRNN, which can effectively extract contextual and long-term dependency information by using a transformer encoder and a Bi-directional Long Short-Term Memory (BiLSTM) structure. We evaluate our model with baseline approaches on a dataset collected from online sources (including 659 suicide notes, 431 last statements, and 2000 neutral posts). Our proposed TransformerRNN achieves 95.0%, 94.9% and 94.9% performance in P, R and F1-score metrics respectively and therefore outperforms comparable machine learning and state-of-the-art deep learning models. The proposed model is effective for classifying suicide notes, which in turn, may help to develop suicide prevention technologies for social media.

AB - Suicide is one of the leading causes of death worldwide. At the same time, the widespread use of social media has led to an increase in people posting their suicide notes online. Therefore, designing a learning model that can aid the detection of suicide notes online is of great importance. However, current methods cannot capture both local and global semantic features. In this paper, we propose a transformer-based model named TransformerRNN, which can effectively extract contextual and long-term dependency information by using a transformer encoder and a Bi-directional Long Short-Term Memory (BiLSTM) structure. We evaluate our model with baseline approaches on a dataset collected from online sources (including 659 suicide notes, 431 last statements, and 2000 neutral posts). Our proposed TransformerRNN achieves 95.0%, 94.9% and 94.9% performance in P, R and F1-score metrics respectively and therefore outperforms comparable machine learning and state-of-the-art deep learning models. The proposed model is effective for classifying suicide notes, which in turn, may help to develop suicide prevention technologies for social media.

M3 - Article

JO - Internet interventions

JF - Internet interventions

SN - 2214-7829

ER -