Table 2.

Fitted SARIMA models and excess number of calls to the Suicide Prevention Lifeline by Twitter content category, 2016–2018 (1096 days), United States.

Content category	Raw association (only controlled for total tweets)		Adjusted for all other categories and total tweets
	Estimate (SE)	p	Estimate (SE)	p
Awareness	0.69 (0.73)	0.35	0.96 (0.76)	0.20
Prevention	1.95 (0.71)	0.007	1.94 (0.73)	0.008
Coping	9.34 (3.51)	0.008	9.40 (3.53)	0.008
Suicidal ideation / attempt without coping	−4.74 (2.54)	0.062	−5.79 (2.52)	0.022
Suicide case	−0.58 (0.60)	0.33	0.06 (0.64)	0.93
Total tweets	−0.01 (0.0003)	0.022	−0.01 (0.0003)	0.007

SARIMA: Seasonal Autoregressive Integrated Moving Average; SE: standard error.

The time series (1 January 2016 to 31 December 2018) data were checked for additive outliers (i.e. outliers affecting only one observation) and innovative outliers (i.e. outliers affecting several consecutive observations) and level shifts, which were integrated when necessary. There were 20 outliers in total, with some of them related to specific events, others to possible technical glitches in call registration. A SARIMA(0,1,5)(1,0,1) model, stationary R² = 0.60, Box-Ljung-Q = 16.28, df = 13, p = 0.20, was fitted to the data. Note that this model required not five, but only three moving average terms (at lags 1, 3 and 5). We subsequently added explanatory models to the basic model. We modelled outliers most likely related to technical glitches in call registration as discussed with the Lifeline, see Supplemental Appendix (Supplemental Text S2). In total, nine outliers were modelled in the final model, SARIMA(0,1,5)(1,0,1), stationary R² = 0.43, Box-Ljung-Q = 18.11, df = 13, p = 0.15.

The bold value indicates the significant p < .05.