  Currently, the process of evaluating suicides is highly subjective, which
limits the efficacy and accuracy of prevention efforts. Artificial intelligence
(AI) has emerged as a means of investigating large datasets to identify
patterns within "big data" that can determine the factors on suicide outcomes.
Here, we use AI tools to extract the topic from (press and social) media text.
However, news media articles lack of suicide tags. Using tweets with hashtags
related to sucide, we train a neuronal model which identifies if a given text
has a suicidade-related contagion. Our results suggest a high level of the
impact of mediatic into suicide cases, and a intrinsic thematic relationship of
suicide news. These results pave the way to build more interpretable suicide
data, which may help to better track, understand its origin, and improve
prevention strategies.
