Year: 2022 Source: 11 pp. SIEC No: 20220005

Suicide remains a serious public-health concern that is difficult to accurately predict in real-world settings. To identify potential predictors of suicide, we examined the emotional content of suicide notes using methods from cognitive network science. Specifically, we compared the co-occurrence networks of suicide notes with those constructed out of emotion words written by individuals scoring low or high on measures of depression, anxiety, and stress. Our objective was to identify which networks were most similar to the suicide notes network, in particular with regard to the connectivity between words and their emotional contents. We also investigated what types of words remained in the high/low emotion networks after controlling for the words present in the suicide notes, which we conceptualize as the “words not said” in the suicide notes. We found that patterns of connectivity among emotion words in suicide notes were most similar to those in texts written by low-anxiety individuals. However, upon analyzing the “words not said” in suicide notes, we observed that the remaining collection of emotions in suicide notes was most similar to those expressed by high-anxiety individuals. We discuss how these findings relate with existing clinical psychological literature as well as their potential implications for predicting suicidal behavior.