Summary of A Big Data Analytics System For Predicting Suicidal Ideation in Real-time Based on Social Media Streaming Data, by Mohamed A. Allayla et al.
A Big Data Analytics System for Predicting Suicidal Ideation in Real-Time Based on Social Media Streaming Data
by Mohamed A. Allayla, Serkan Ayvaz
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Social and Information Networks (cs.SI)
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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 methodology in this paper utilizes a big data architecture to predict suicidal ideation from social media content. The approach involves two phases: batch processing and real-time streaming prediction. The batch dataset was collected from the Reddit forum and used for model building and training, while the Twitter streaming API was used to extract streaming big data for real-time prediction. Multiple Apache Spark ML classifiers were employed, including NB, LR, LinearSVC, DT, RF, and MLP. The experimental results of the batch processing phase showed that a combination of Unigram, Bigram, and CV-IDF features with an MLP classifier achieved high accuracy in classifying suicidal ideation (93.47%). This model was then applied to the real-time streaming prediction phase. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special techniques on big social media data to find people who might be thinking about hurting themselves. It’s like having a superpower to detect when someone needs help before it’s too late! The scientists used two ways: one for processing lots of old data and another for looking at new messages in real-time. They tried different approaches with special algorithms and found that one way, using simple words and feelings, worked really well (93.47% accurate!). This can help save lives by spotting people who might be struggling. |
