Summary of Data Quality Matters: Suicide Intention Detection on Social Media Posts Using Roberta-cnn, by Emily Lin et al.
Data Quality Matters: Suicide Intention Detection on Social Media Posts Using RoBERTa-CNN
by Emily Lin, Jian Sun, Hsingyu Chen, Mohammad H. Mahoor
First submitted to arxiv on: 3 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel deep-learning approach utilizes the state-of-the-art RoBERTa-CNN model to identify suicidal intentions in posts from the SuicideWatch subreddit. The RoBERTa model excels at capturing textual nuances and forming semantic relationships, while the CNN head enhances its capacity to discern critical patterns from extensive datasets. Evaluating the model on the Suicide and Depression Detection dataset yields promising results, with a mean accuracy of 98% and standard deviation of 0.0009. Data quality significantly impacts training a robust model, and techniques such as manual cleaning or utilizing the OpenAI API can improve data quality by removing noise while preserving contextual content. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to detect suicidal thoughts online is developed using artificial intelligence. The AI model, called RoBERTa-CNN, looks at text posts from a special subreddit where people talk about suicide and depression. It’s very good at understanding what people are saying in their own words. The results show that the model can accurately identify when someone might be feeling suicidal or depressed. However, it’s important to make sure the data used to train the model is accurate and clean. This can be done by removing noise from the text or using tools like OpenAI. |
Keywords
» Artificial intelligence » Cnn » Deep learning
