Time-dependent Intrinsic Correlation Research Articles

Suicide and media reporting: a longitudinal and spatial analysis

The impact of media reporting on copycat suicides has been well established in various cases of celebrity suicide. However, knowledge is limited about the spatial and temporal relationship between suicide death and media reporting over a long period of time. This study investigated the association of suicide deaths with suicide news in longitudinal and spatial dimensions. All suicides during 2003-2010 (n=31,364) were included. Suicide news in the study period was retrieved from Google News, and included all available news media in Taiwan. Empirical mode decomposition was used to identify the main intrinsic oscillation, reflecting both major and minor suicide events, and time-dependent intrinsic correlation was used to quantify the temporal correlation between suicide deaths and suicide news. The media reporting of suicide was synchronized with increased suicide deaths during major suicide events such as celebrity death, and slightly lagged behind the suicide deaths for 1month in other periods without notable celebrity deaths. The means of suicide reported in the media diversely affected the suicide models. Reports of charcoal burning suicide exhibited an exclusive copycat effect on actual charcoal burning deaths, whereas media reports of jumping had a wide association with various suicide models. Media reports of suicide had a higher association with suicide deaths in urban than in rural areas. This report suggested that a delayed effect of copycat suicide may exist in media reports of minor suicide events. The competitive reporting of minor suicide events must be avoided and addressed by media professionals.

THE TIME-DEPENDENT INTRINSIC CORRELATION BASED ON THE EMPIRICAL MODE DECOMPOSITION

A Time-Dependent Intrinsic Correlation (TDIC) method is introduced. This new approach includes both auto- and cross-correlation analysis designed especially to analyze, capture and track the local correlations between nonlinear and nonstationary time series pairs. The approach is based on Empirical Mode Decomposition (EMD) to decompose the nonlinear and nonstationary data into their intrinsic mode functions (IMFs) and uses the instantaneous periods of the IMFs to determine a set of the sliding window sizes for the computation of the running correlation coefficients for multi-scale data. This new method treats the selection of the sliding window sizes as an adaptive process determined by the data itself, not a "tuning" process. Therefore, it gives an intrinsic correlation analysis of the data. Furthermore, the multi-window approach makes the new method applicable to complicated data from multi-scale phenomena. The synthetic and time series from real world are used to demonstrate conclusively that the new approach is far more superior over the traditional method in its ability to reveal detailed and subtle correlations unavailable through any other methods in existence. Thus, the TDIC represents a major advance in statistical analysis of data from nonlinear and nonstationary processes.