# Predicting Suicide Ideation from Social Media Text Using CNN-BiLSTM

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/predicting-suicide-ideation-from-social-media-text-using-cnn-bilstm/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Christianah Titilope Oyewale,Joseph Damilola Akinyemi,Ayodeji Ibitoye,Olufade F. W. Onifade |
| Citations | 4 |
| DOI | 10.1007/978-3-031-53731-8_22 |
| Fields | Psychology,Social Sciences |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W4391736231 |
| Type | conference-paper |
| Year | 2024 |

## Paper authors

- [Joseph Damilola Akinyemi](https://scholariq.org/researchers/joseph-damilola-akinyemi/)

## Paper journal

- [Communications in computer and information science](https://scholariq.org/journals/communications-in-computer-and-information-science/)

## Paper primary topic

- [Mental Health via Writing](https://scholariq.org/topics/mental-health-via-writing/)

## Paper topics

- [Mental Health via Writing](https://scholariq.org/topics/mental-health-via-writing/)
- [Suicide and Self-Harm Studies](https://scholariq.org/topics/suicide-and-self-harm-studies/)
- [Computational and Text Analysis Methods](https://scholariq.org/topics/computational-and-text-analysis-methods/)

---
Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.