A Psycholinguistic Analysis of Responses to Live-Stream Suicides on Social Media
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Collection
2.2. Data Pre-Processing
2.3. Data Analysis
3. Results
3.1. Coding
3.2. Differentiating between Negative/Non-Negative Responses
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Incident 1 | Incident 2 | Incident 3 | Incident 4 | |
---|---|---|---|---|
Time | 12/07/2012 21:05 | 16/02/2013 23:57 | 09/12/2013 17:36 | 19/02/2016 19:57 |
Location | Sichuan | Beijing | Shandong | Shanghai |
Gender | Female | Female | Female | Male |
Age | 31 | 31 | 33 | 40 |
Suicide method | Taking poisons & charcoal burning | Jumping from a high place | Drowning | Hanging |
Suicide cause | Relationship breakup | Mental illness | Work-related stress | Mental illness |
Result | Rescued | Died | Died | Died |
Category | Definition | Example Weibo Post |
---|---|---|
Providing social support | Expressing care and compassion, or providing information, advice, and resources | “I hope you are ok! Don′t die” “You should get professional help now ...” “My mom is an expert in this field! Don′t die!! I can ask her to help you!!!” |
Calling for help | Calling police and other users for help | “Call the police!” “Is there anyone who knows his address and can go to his place to help him? ...” |
Expressing shock | Feeling surprised and upset | “What′s up, don′t scare me” |
Making negative responses | Expressing cynical, dismissive, and indifferent attitudes, or refusing to offer help and encouraging suicide | “Is there anything wrong with your brain? Why not to kill yourself quietly ...” “It is her last wishes, Why do you want to stop her” “If you want to kill yourself, please be quick ...” |
Unspecified | Replying with unspecified intent or meaning | “ ” “.........” “Sigh” |
Category | Incident 1 | Incident 2 | Incident 3 | Incident 4 | Sum |
---|---|---|---|---|---|
Providing social support | 671 (41.06%) | 1583 (79.75%) | 912 (59.07%) | 1640 (80.04%) | 4806 (66.64%) |
Calling for help | 87 (5.32%) | 20 (1.01%) | 111 (7.19%) | 19 (0.93%) | 237 (3.29%) |
Expressing shock | 41 (2.51%) | 37 (1.86%) | 28 (1.81%) | 67 (3.27%) | 173 (2.40%) |
Making negative responses | 697 (42.66%) | 192 (9.67%) | 371 (24.03%) | 150 (7.32%) | 1410 (19.55%) |
Unspecified | 138 (8.45%) | 153 (7.71%) | 122 (7.90%) | 173 (8.44%) | 586 (8.13%) |
Gain Ratio Attribute Evaluator | Significance Attribute Evaluator | Chi-Squared Attribute Evaluator | |
---|---|---|---|
1 | Total Pronouns | Total Pronouns | Total Function Words |
2 | Adverbs | Affective Processes | Affective Processes |
3 | Cognitive Processes | Cognitive Processes | Adverbs |
4 | Death | Adverbs | Total Pronouns |
5 | Affective Processes | Exclusive | Cognitive Processes |
6 | Total Function Words | Biological Processes | Biological Processes |
7 | Exclusive | Death | Exclusive |
8 | Biological Processes | Total Function Words | Death |
9 | Body | Body | Auxiliary Verbs |
10 | Impersonal Pronouns | Impersonal Pronouns | Impersonal Pronouns |
11 | Common Verbs | Auxiliary Verbs | Conjunctions |
12 | Conjunctions | Assent | Body |
13 | Auxiliary Verbs | Conjunctions | Common Verbs |
14 | Fillers | Common Verbs | Personal Pronouns |
15 | Family | Personal Pronouns | Positive Emotion |
16 | Swear Words | Fillers | Assent |
17 | Third Pers Plural | Negative Emotion | Second Pers Singular |
18 | Anger | Relativity | Negative Emotion |
19 | Personal Pronouns | Family | Prepositions |
20 | Negative Emotion | Second Pers Singular | Inclusive |
21 | Assent | Inclusive | Discrepancy |
22 | Nonfluencies | Humans | Health |
23 | Inclusive | Positive Emotion | Relativity |
24 | Social Processes | Social Processes | Humans |
25 | Humans | Swear Words | Social Processes |
Precision | Recall | F-Measure | Screening Efficacy | |
---|---|---|---|---|
Simple Logistic Regression | 68.8% | 72.8% | 70.7% | 47.1% |
Multilayer Perception Neural Networks | 66.2% | 79.6% | 72.3% | 39.9% |
Support Vector Machine | 62.9% | 85.9% | 72.6% | 31.7% |
Random Forest | 69.8% | 76.2% | 72.9% | 45.4% |
Predictors | β | |
---|---|---|
Negative responses (1) vs. Non-negative responses (0) | Total Function Words | 2.31 |
Total Pronouns | 0.30 | |
Impersonal Pronouns | 1.05 | |
Common Verbs | −0.62 | |
Auxiliary Verbs | 0.54 | |
Adverbs | −0.24 | |
Conjunctions | 3.15 | |
Humans | 2.21 | |
Affective Processes | 0.83 | |
Negative Emotion | 1.66 | |
Inclusive | −0.99 | |
Exclusive | 2.28 | |
Biological Processes | −0.16 | |
Body | 5.19 | |
Death | 4.47 | |
Assent | −1.66 |
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Li, A.; Jiao, D.; Liu, X.; Sun, J.; Zhu, T. A Psycholinguistic Analysis of Responses to Live-Stream Suicides on Social Media. Int. J. Environ. Res. Public Health 2019, 16, 2848. https://doi.org/10.3390/ijerph16162848
Li A, Jiao D, Liu X, Sun J, Zhu T. A Psycholinguistic Analysis of Responses to Live-Stream Suicides on Social Media. International Journal of Environmental Research and Public Health. 2019; 16(16):2848. https://doi.org/10.3390/ijerph16162848
Chicago/Turabian StyleLi, Ang, Dongdong Jiao, Xingyun Liu, Jiumo Sun, and Tingshao Zhu. 2019. "A Psycholinguistic Analysis of Responses to Live-Stream Suicides on Social Media" International Journal of Environmental Research and Public Health 16, no. 16: 2848. https://doi.org/10.3390/ijerph16162848