Detecting and Measuring Depression on Social Media Using a Machine Learning Approach: Systematic Review.

Danxia Liu, Xing Lin Feng, Farooq Ahmed, Muhammad Shahid, Jing Guo

March 2022 JMIR Ment Health

Synopsis of Social media discussions

Several discussions highlight the promising role of machine learning for depression detection, using phrases like 'transformative potential' and 'key challenges,' which demonstrate both excitement and critical analysis. The tone balances optimism with concern about ethical and sampling issues, reflecting a deep engagement with the article’s implications.

A
Agreement
 Moderate agreement

Most discussions acknowledge the value of the review, with some expressing strong support for the potential of machine learning in mental health detection.

I
Interest
 High level of interest

The discussions show high interest, with participants motivated to explore the implications for public health and technology.

E
Engagement
 Moderate level of engagement

Comments include detailed reflections on the methodology and challenges, indicating a moderate level of engagement beyond surface-level reactions.

I
Impact
 High level of impact

There’s consensus that the research could influence future mental health practices and policy, emphasizing its significance.

Social Mentions

YouTube

2 Videos

Facebook

2 Posts

Twitter

2 Posts

News

8 Articles

Metrics

Video Views

12

Extended Reach

10,360

Social Features

14

Timeline: Posts about article

Top Social Media Posts

Posts referencing the article

Machine Learning for Detecting Depression on Social Media

Machine Learning for Detecting Depression on Social Media

This systematic review summarizes past studies that use machine learning methods to detect depression through social media text data, highlighting their potential as tools for public mental health. Further research is needed to address limitations and improve applications.

Social RTCL TV

November 20, 2023

9 views

Machine Learning Techniques for Detecting Depression on Social Media

Machine Learning Techniques for Detecting Depression on Social Media

This systematic review summarizes past studies that use machine learning methods to detect depression through social media text data, highlighting the potential of these approaches as tools for public mental health.

Social RTCL TV

December 10, 2023

3 views

  • JMIR Mental Health
    @JMIR_JMH (Twitter)

    RT @jmirpub: JMIR Mental Health: Detecting and Measuring #depression on #SocialMedia #SoMe #hcsm Using a Machine Learning #Approach: System…
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    March 1, 2022

    3

  • Machine Learning Bot
    @ML_Tweet_Bot (Twitter)

    RT @jmirpub: JMIR Mental Health: Detecting and Measuring #depression on #SocialMedia #SoMe #hcsm Using a Machine Learning #Approach: System…
    view full post

    March 1, 2022

    3

Abstract Synopsis

  • This systematic review summarizes past studies that use machine learning (ML) methods to detect depression through social media text data, highlighting the potential of these approaches as tools for public mental health.
  • The review analyzed 17 relevant studies, with most employing supervised ML techniques, and identified key challenges such as sampling bias, ethical concerns, privacy issues, and the need for better prediction methods.
  • The findings suggest that ML techniques can effectively identify depression on social media, but further research is necessary to address current limitations and improve their application in mental health practices.