Advanced Daily Prediction Model for National Suicide Numbers with Social Media Data.

OBJECTIVE: Suicide is a significant public health concern worldwide. Social media data have a potential role in identifying high suicide risk individuals and also in predicting suicide rate at the population level. In this study, we report an advanced daily suicide prediction model using social media data combined with economic/meteorological variables along with observed suicide data lagged by 1 week. METHODS: The social media data were drawn from weblog posts. We examined a total of 10,035 social media keywords for suicide prediction. We made predictions of national suicide numbers 7 days in advance daily for 2 years, based on a daily moving 5-year prediction modeling period. RESULTS: Our model predicted the likely range of daily national suicide numbers with 82.9% accuracy. Among the social media variables, words denoting economic issues and mood status showed high predictive strength. Observed number of suicides one week previously, recent celebrity suicide, and day of week followed by stock index, consumer price index, and sunlight duration 7 days before the target date were notable predictors along with the social media variables. CONCLUSION: These results strengthen the case for social media data to supplement classical social/economic/climatic data in forecasting national suicide events.

Identification of Keywords From Twitter and Web Blog Posts to Detect Influenza Epidemics in Korea.

OBJECTIVE: Social media data are a highly contextual health information source. The objective of this study was to identify Korean keywords for detecting influenza epidemics from social media data. METHODS: We included data from Twitter and online blog posts to obtain a sufficient number of candidate indicators and to represent a larger proportion of the Korean population. We performed the following steps: initial keyword selection; generation of a keyword time series using a preprocessing approach; optimal feature selection; model building and validation using least absolute shrinkage and selection operator, support vector machine (SVM), and random forest regression (RFR). RESULTS: A total of 15 keywords optimally detected the influenza epidemic, evenly distributed across Twitter and blog data sources. Model estimates generated using our SVM model were highly correlated with recent influenza incidence data. CONCLUSIONS: The basic principles underpinning our approach could be applied to other countries, languages, infectious diseases, and social media sources. Social media monitoring using our approach may support and extend the capacity of traditional surveillance systems for detecting emerging influenza. (Disaster Med Public Health Preparedness. 2018; 12: 352-359).

Public Trauma after the Sewol Ferry Disaster: The Role of Social Media in Understanding the Public Mood.

The Sewol ferry disaster severely shocked Korean society. The objective of this study was to explore how the public mood in Korea changed following the Sewol disaster using Twitter data. Data were collected from daily Twitter posts from 1 January 2011 to 31 December 2013 and from 1 March 2014 to 30 June 2014 using natural language-processing and text-mining technologies. We investigated the emotional utterances in reaction to the disaster by analyzing the appearance of keywords, the human-made disaster-related keywords and suicide-related keywords. This disaster elicited immediate emotional reactions from the public, including anger directed at various social and political events occurring in the aftermath of the disaster. We also found that although the frequency of Twitter keywords fluctuated greatly during the month after the Sewol disaster, keywords associated with suicide were common in the general population. Policy makers should recognize that both those directly affected and the general public still suffers from the effects of this traumatic event and its aftermath. The mood changes experienced by the general population should be monitored after a disaster, and social media data can be useful for this purpose.

Predicting national suicide numbers with social media data.

Suicide is not only an individual phenomenon, but it is also influenced by social and environmental factors. With the high suicide rate and the abundance of social media data in South Korea, we have studied the potential of this new medium for predicting completed suicide at the population level. We tested two social media variables (suicide-related and dysphoria-related weblog entries) along with classical social, economic and meteorological variables as predictors of suicide over 3 years (2008 through 2010). Both social media variables were powerfully associated with suicide frequency. The suicide variable displayed high variability and was reactive to celebrity suicide events, while the dysphoria variable showed longer secular trends, with lower variability. We interpret these as reflections of social affect and social mood, respectively. In the final multivariate model, the two social media variables, especially the dysphoria variable, displaced two classical economic predictors - consumer price index and unemployment rate. The prediction model developed with the 2-year training data set (2008 through 2009) was validated in the data for 2010 and was robust in a sensitivity analysis controlling for celebrity suicide effects. These results indicate that social media data may be of value in national suicide forecasting and prevention.