Matches in SemOpenAlex for { <https://semopenalex.org/work/W2999665201> ?p ?o ?g. }
Showing items 1 to 85 of
85
with 100 items per page.
- W2999665201 abstract "The goal from this study is to identify suicide risk factors on Twitter. We propose a machine learning framework that could be potentially useful for suicide prevention interventions. We applied search terms from the suicidal ideation tracking framework pro-posed by Jashinsky et al. and downloaded 12,066 public tweets from 3,873 users via Twitter's application programming interface (API). We created HighRisk or AtRisk labels for users based on their suicidal ideation terms' usage and applied three topic discovery algorithms to find underlying suicide risk factors among users, which were subsequently used to classify users into HighRisk or AtRisk. Algorithms applied included Latent Semantic Analysis, Latent Dirichlet Allocation, Non-negative Matrix Factorization, Decision Tree and K-means Clustering. Our topic discovery approach detected 7 out of 12 suicide risk factors proposed by Jashinsky et al. Using a decision tree classification model that utilized these factors, we achieved 0.844 in precision, 0.912 in sensitivity, and 0.829 in specificity in classifying users into HighRisk and AtRisk groups. The development of this framework supplements suicide researchers and suicide preven-tion efforts, with a potential to be employed at run-time." @default.
- W2999665201 created "2020-01-23" @default.
- W2999665201 creator A5005267835 @default.
- W2999665201 creator A5024439180 @default.
- W2999665201 creator A5025703949 @default.
- W2999665201 creator A5027667913 @default.
- W2999665201 creator A5028409314 @default.
- W2999665201 creator A5041494719 @default.
- W2999665201 creator A5043510569 @default.
- W2999665201 creator A5065797548 @default.
- W2999665201 creator A5070422519 @default.
- W2999665201 date "2019-11-01" @default.
- W2999665201 modified "2023-10-16" @default.
- W2999665201 title "Using Machine Learning Algorithms to Detect Suicide Risk Factors on Twitter" @default.
- W2999665201 cites W1877481539 @default.
- W2999665201 cites W2003834798 @default.
- W2999665201 cites W2129587854 @default.
- W2999665201 cites W2155002669 @default.
- W2999665201 cites W2157035150 @default.
- W2999665201 cites W2165343833 @default.
- W2999665201 cites W2407086192 @default.
- W2999665201 cites W2508051475 @default.
- W2999665201 cites W2589777923 @default.
- W2999665201 doi "https://doi.org/10.1109/icdmw.2019.00137" @default.
- W2999665201 hasPublicationYear "2019" @default.
- W2999665201 type Work @default.
- W2999665201 sameAs 2999665201 @default.
- W2999665201 citedByCount "18" @default.
- W2999665201 countsByYear W29996652012020 @default.
- W2999665201 countsByYear W29996652012021 @default.
- W2999665201 countsByYear W29996652012022 @default.
- W2999665201 countsByYear W29996652012023 @default.
- W2999665201 crossrefType "proceedings-article" @default.
- W2999665201 hasAuthorship W2999665201A5005267835 @default.
- W2999665201 hasAuthorship W2999665201A5024439180 @default.
- W2999665201 hasAuthorship W2999665201A5025703949 @default.
- W2999665201 hasAuthorship W2999665201A5027667913 @default.
- W2999665201 hasAuthorship W2999665201A5028409314 @default.
- W2999665201 hasAuthorship W2999665201A5041494719 @default.
- W2999665201 hasAuthorship W2999665201A5043510569 @default.
- W2999665201 hasAuthorship W2999665201A5065797548 @default.
- W2999665201 hasAuthorship W2999665201A5070422519 @default.
- W2999665201 hasConcept C119857082 @default.
- W2999665201 hasConcept C154945302 @default.
- W2999665201 hasConcept C169258074 @default.
- W2999665201 hasConcept C171686336 @default.
- W2999665201 hasConcept C2776641880 @default.
- W2999665201 hasConcept C3017944768 @default.
- W2999665201 hasConcept C41008148 @default.
- W2999665201 hasConcept C500882744 @default.
- W2999665201 hasConcept C526869908 @default.
- W2999665201 hasConcept C71924100 @default.
- W2999665201 hasConcept C73555534 @default.
- W2999665201 hasConcept C84525736 @default.
- W2999665201 hasConcept C99454951 @default.
- W2999665201 hasConceptScore W2999665201C119857082 @default.
- W2999665201 hasConceptScore W2999665201C154945302 @default.
- W2999665201 hasConceptScore W2999665201C169258074 @default.
- W2999665201 hasConceptScore W2999665201C171686336 @default.
- W2999665201 hasConceptScore W2999665201C2776641880 @default.
- W2999665201 hasConceptScore W2999665201C3017944768 @default.
- W2999665201 hasConceptScore W2999665201C41008148 @default.
- W2999665201 hasConceptScore W2999665201C500882744 @default.
- W2999665201 hasConceptScore W2999665201C526869908 @default.
- W2999665201 hasConceptScore W2999665201C71924100 @default.
- W2999665201 hasConceptScore W2999665201C73555534 @default.
- W2999665201 hasConceptScore W2999665201C84525736 @default.
- W2999665201 hasConceptScore W2999665201C99454951 @default.
- W2999665201 hasLocation W29996652011 @default.
- W2999665201 hasOpenAccess W2999665201 @default.
- W2999665201 hasPrimaryLocation W29996652011 @default.
- W2999665201 hasRelatedWork W2999665201 @default.
- W2999665201 hasRelatedWork W3204641204 @default.
- W2999665201 hasRelatedWork W3210877509 @default.
- W2999665201 hasRelatedWork W4200196661 @default.
- W2999665201 hasRelatedWork W4205958290 @default.
- W2999665201 hasRelatedWork W4249746146 @default.
- W2999665201 hasRelatedWork W4283016678 @default.
- W2999665201 hasRelatedWork W4293525103 @default.
- W2999665201 hasRelatedWork W4308191010 @default.
- W2999665201 hasRelatedWork W4318350883 @default.
- W2999665201 isParatext "false" @default.
- W2999665201 isRetracted "false" @default.
- W2999665201 magId "2999665201" @default.
- W2999665201 workType "article" @default.