Supplementary

Table A1. 59 suicide-related search terms of Google Trends.Chinese terms

English translationChinese terms

English translationChinese terms

English translation

煩惱 Trouble 焦慮 Anxiety disorder 性騷擾 Sexual harassment

頹廢 Decadence 絕望 Despair 官司 Lawsuit

遺憾 Regret 失眠 Insomnia 失業 Unemployment

無能 Incompetent工作壓力 Working stress 恆指 Hang Seng Index

失敗 Failure 自閉 Autism 負資產 Negative assets

抵死 Serve sb right 歧視 Discrimination 樓市 Property market

廢柴 Loser思覺失調 Psychosis 綜緩

Comprehensive Social Security Assistance (CSSA)

對不起 Sorry精神分裂症 Schizophrenia 劏房 Subdivided flat

精神分裂 Schizophrenia 精神科 Psychiatry department

自殺 Suicide

自私 Selfishness 抑鬱 Depressed 上吊 Hanging

後悔 Regret 憂鬱症 Major depression 跳樓 Jumping

結束 End 躁鬱症 Bipolar disorder 燒炭 Charcoal-burning

解決 Solve 癌症 Cancer 自殘 Self-harm

有罪 Guilt 毒品 Illicit drug 吊頸 Hanging

離開 Leave 離婚 Divorce 跳樓自殺 Jump off a building

壓力 Stress 虐待 Mistreat 跳海 Jump into the sea

累 Tired 家暴 Domestic violence 天堂 Heaven

抑鬱症 Depressive disorder

分手 Break up完全自殺手冊自殺方法

Complete guide of suicideSuicide method

安眠藥 Hypnotics雙失青年

Not in Employment, Education or Training (NEET)

失望 Disappointment母親的抉擇 Mother's choice 　 　

Table A2. Summary of independent variables (all independent variables are normalized to 0 -

100)Predictor Description

Google

Trends59 suicide related Google search terms

Newspaper

Report typesCounts of news reports about suicide attempt, suicide

incidence and suicide advice

Suicide

methods

Counts of news reports about charcoal-burning, jumping,

gas, hanging, poisoning and others suicide cases

Suicide reasonsCounts of news reports about suicide caused by depression,

finance, relationship, illness and academic

Suicide age

group

Counts of news reports about young, middle-age and old

people suicide cases

Suicide gender Counts of news reports about male and female suicide cases

LIWC

Percentage of words in positive, negative (anxiety, anger

and sadness), biological processes (body, health/illness and

sexuality) and personal concerns (work, money and death)

category out of the total word count of a news report.

Presses of

report

Count of published by group 1 (apple daily, oriental daily

and the Sun), group 2 (am730, headline daily, metro daily,

sky post and take me home), group 3 (Ming Pao, Sing Tao,

HK01, Hong Kong Economic Journal and Hong Kong

Economic Times) and group 4 (the rest)

Table A3a. Summary of significance of media reporting data.　 The number of models each predictor is significant in (%)

Media reporting Young males Middle-aged males Old males Young females Middle-aged females Old females

Report type

Attempt 0 (0%) - - - - -

Death 0 (0%) 0 (0%) - 5 (5%) 0 (0%) 0 (0%)

Suicide methods

Charcoal-burning - - 0 (0%) 0 (0%) - -

Jumping 0 (0%) 13 (12%) - 0 (0%) 0 (0%) -

Gas - - - - 51 (49%) -

Hanging - - 0 (0%) - - -

Poisoning - - 0 (0%) 0 (0%) - -

Others 0 (0%) - - 0 (0%) - -

Suicide reasons

Depression - - - 0 (0%) - -

Finance - 11 (10%) - - - -

Relationship 0 (0%) - - 0 (0%) - -

Illness - - 0 (0%) 1 (1%) 0 (0%) 2 (2%)

Academic 8 (8%) - 0 (0%) - - -

Suicide age group

Young - - - - 2 (2%) -

Old - - 79 (75%) - 0 (0%) 0 (0%)

Suicide gender

Male 0 (0%) - - 0 (0%) - -

Female 0 (0%) - - 105 (100%) 0 (0%) -

LIWC

Body - - 0 (0%) 0 (0%) - -

Positive emotions - - - 20 (19%) -

Presses of report

Group 1 0 (0%) 0 (0%) - 0 (0%) - -

Group 2 0 (0%) 0 (0%) - 0 (0%) - 0 (0%)

Group 3 0 (0%) - - 0 (0%) - 0 (0%)

Group 4 0 (0%) - - 38 (36%) - -

Table A3b. Summary of significance of Google Trends search terms data.　 The number of models each predictor is significant in (%)

Google Trends Young males Middle-aged males Old males Young females Middle-aged females Old females

Jumping 2 (2%) - 75 (71%) - - -

Hypnotics 6 (6%) - - - - -

Jumping off a

building0 (0%) - - - - -

Heaven 95 (90%) 0 (0%) - - 0 (0%) -

Unemployment 105 (100%) 105 (100%) - - - -

Autism 0 (0%) - - - - -

Lawsuit 60 (57%) - - - - -

Hang Seng Index 0 (0%) - - - - -

Psychiatry department 0 (0%) - - - - 6 (6%)

Subdivided flat 4 (4%) - - - 0 (0%) -

Decadence - 0 (0%) - - - 0 (0%)

Regret - 0 (0%) - - - -

Guilt - 41 (39%) - - - 0 (0%)

Disappointment - 0 (0%) - - 0 (0%) -

Despair - 62 (59%) - - 96 (91%) -

CSSA - 93 (89%) - - - -

Depressive disorder - 0 (0%) - - - -

Bipolar disorder - 0 (0%) - - - -

Break up - - 71 (68%) 0 (0%) - -

Hanging - - 105 (100%) 0 (0%) 0 (0%) 42 (40%)

Property market - - 105 (100%) 0 (0%) - -

End - - - 105 (100%) - 2 (2%)

Leave - - - 1 (1%) - -

Cancer - - - 3 (3%) - -

Suicide - - - 57 (54%) - -

Working stress - - - - 27 (26%) -

Mother's choice - - - - 0 (0%) -

Schizophrenia - - - - 27 (26%) -

Sorry - - - - - 0 (0%)

Tired - - - - - 0 (0%)

Psychosis - - - - - 0 (0%)

Figure A1. The overall classification process of suicide related news reports.

For languages like Chinese, text contains of characters written without spaces in between,

specific process is needed to identify the boundaries of words, i.e. to determine where in a

sequence of Chinese characters to put a delimiter such that the separated units (words) are

meaningful units for further tasks. In this study, JiebaR a software package available in R

programming language (Wenfeng and Yanyi, 2018), was used to do Chinese text

segmentation.

With the segmented text, each feature unit is a word. Next step is to determine how to

represent these features quantitatively, which is called word embedding (Bengio et al., 2003;

Collobert and Weston, 2008). Word2Vec (Le and Mikolov, 2014) method was used in this

study. The full corpus containing over 220k titles of news reports representing the suicide

related news reports from 1998 to 2016 published in major Hong Kong newspaper were used

to train the word vectors which will be used later for encoding each news title document into

a document vector.

Finally, FastText, a Facebook open source tool for word vector and text classification

(Bojanowski et al., 2017), was adopted for news title classification. This classifier combines

the technique of Word2Vec to the average of vectors of all the words in a document as the

document vector and then get the classification results.

Reference

Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C., 2003. A Neural Probabilistic Language

Model, in: Journal of Machine Learning Research.

https://doi.org/10.1162/153244303322533223

Bojanowski, P., Grave, E., Joulin, A., Mikolov, T., 2017. Enriching Word Vectors with

Subword Information. Trans. Assoc. Comput. Linguist. 5, 135–146.

Collobert, R., Weston, J., 2008. A unified architecture for natural language processing, in:

Proceedings of the 25th International Conference on Machine Learning - ICML ’08.

https://doi.org/10.1145/1390156.1390177

Le, Q. V., Mikolov, T., 2014. Distributed Representations of Sentences and Documents.

Wenfeng, Q., Yanyi, W., 2018. Package “jiebaR.”