Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Suicidal Ideation Detection on Social Media: A Review of Machine
  Learning Methods   Social media platforms have transformed traditional communication methods by
allowing users worldwide to communicate instantly, openly, and frequently.
People use social media to express their opinion and share their personal
stories and struggles. Negative feelings that express hardship, thoughts of
death, and self-harm are widespread in social media, especially among young
generations. Therefore, using social media to detect and identify suicidal
ideation will help provide proper intervention that will eventually dissuade
others from self-harming and committing suicide and prevent the spread of
suicidal ideations on social media. Many studies have been carried out to
identify suicidal ideation and behaviors in social media. This paper presents a
comprehensive summary of current research efforts to detect suicidal ideation
using machine learning algorithms on social media. This review 24 studies
investigating the feasibility of social media usage for suicidal ideation
detection is intended to facilitate further research in the field and will be a
beneficial resource for researchers engaged in suicidal text classification.

37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics