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Suicide-related media content has preventive or harmful effects depending on the specific content. Proactive media screening for suicide prevention is hampered by the scarcity of machine learning approaches to detect specific characteristics in news reports. This study applied machine learning to label large quantities of broadcast (TV and radio) media data according to media recommendations reporting suicide. We manually labeled 2519 English transcripts from 44 broadcast sources in Oregon and Washington, USA, published between April 2019 and March 2020. We conducted a content analysis of media reports regarding content characteristics. We trained a benchmark of machine learning models including a majority classifier, approaches based on word frequency (TF-IDF with a linear SVM) and a deep learning model (BERT). We applied these models to a selection of more simple (e.g., focus on a suicide death), and subsequently to putatively more complex tasks (e.g., determining the main focus of a text from 14 categories). Tf-idf with SVM and BERT were clearly better than the naive majority classifier for all characteristics. In a test dataset not used during model training, F1-scores (i.e., the harmonic mean of precision and recall) ranged from 0.90 for celebrity suicide down to 0.58 for the identification of the main focus of the media item. Model performance depended strongly on the number of training samples available, and much less on assumed difficulty of the classification task. This study demonstrates that machine learning models can achieve very satisfactory results for classifying suicide-related broadcast media content, including multi-class characteristics, as long as enough training samples are available. The developed models enable future large-scale screening and investigations of broadcast media. [ABSTRACT FROM AUTHOR]

: Between April 7 and 14, 2019, the "Breaking the Silence" media engagement campaign was launched in Oregon. Background : Between April 7 and 14, 2019, the "Breaking the Silence" media engagement campaign was launched in Oregon. Aims: We aimed to assess the consistency of media content related to the campaign with media guidelines and the quantitative footprint on Twitter (now X) over time. Method: Media items related to the campaign were analyzed regarding focus and consistency with media guidelines for suicide reporting and compared with other suicide-related reports published in the same time frame, as well as with reporting in Washington, the control region. Tweets related to the campaign were retrieved to assess the social media footprint. Results: There were n = 104 media items in the campaign month, mainly in the campaign week. Items typically used a narrative featuring suicide advocacy or policy/prevention programs. As compared to other items with a similar focus, they scored better on several protective characteristics listed in media recommendations. Stories of coping with adversity, however, were scarce. The social media footprint on Twitter was small. Limitations: Inability to make causal claims about campaign impact. Conclusion: Media items from the Breaking the Silence campaign appeared mainly consistent with media guidelines, but some aspects, such as stories of recovery, were under-represented.