Machine Learning-based Approach for Depression Detection in Twitter Using Content and Activity Features

• URL: http://arxiv.org/abs/2003.04763v1
• Date: Mon, 9 Mar 2020 11:27:39 GMT
• Title: Machine Learning-based Approach for Depression Detection in Twitter Using Content and Activity Features
• Authors: Hatoon S. AlSagri, Mourad Ykhlef
• Abstract summary: Recent studies have indicated a correlation between high usage of social media sites and increased depression. The present study aims to exploit machine learning techniques for detecting a probable depressed Twitter user based on both, his/her network behavior and tweets.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Social media channels, such as Facebook, Twitter, and Instagram, have altered our world forever. People are now increasingly connected than ever and reveal a sort of digital persona. Although social media certainly has several remarkable features, the demerits are undeniable as well. Recent studies have indicated a correlation between high usage of social media sites and increased depression. The present study aims to exploit machine learning techniques for detecting a probable depressed Twitter user based on both, his/her network behavior and tweets. For this purpose, we trained and tested classifiers to distinguish whether a user is depressed or not using features extracted from his/ her activities in the network and tweets. The results showed that the more features are used, the higher are the accuracy and F-measure scores in detecting depressed users. This method is a data-driven, predictive approach for early detection of depression or other mental illnesses. This study's main contribution is the exploration part of the features and its impact on detecting the depression level.

Related papers

• Multi Class Depression Detection Through Tweets using Artificial Intelligence [0.0]
  Five types of depression (Bipolar, major, psychotic, atypical, and postpartum) were predicted using tweets from the Twitter database based on lexicon labeling. Bidirectional Representations from Transformers (BERT) was used for feature extraction and training. The BERT model presented the most promising results, achieving an overall accuracy of 0.96.
  arXiv  Detail & Related papers  (2024-04-19T12:47:56Z)
• Cordyceps@LT-EDI: Depression Detection with Reddit and Self-training [0.0]
  Depression is debilitating, and not uncommon. Indeed, studies of excessive social media users show correlations with depression, ADHD, and other mental health concerns. We propose a severity depression detection system using a semi-supervised learning technique to predict if a post is from a user who is experiencing severe, moderate, or low levels of depression.
  arXiv  Detail & Related papers  (2023-09-24T01:14:49Z)
• Depression detection in social media posts using affective and social norm features [84.12658971655253]
  We propose a deep architecture for depression detection from social media posts. We incorporate profanity and morality features of posts and words in our architecture using a late fusion scheme. The inclusion of the proposed features yields state-of-the-art results in both settings.
  arXiv  Detail & Related papers  (2023-03-24T21:26:27Z)
• Machine Learning Algorithms for Depression Detection and Their Comparison [0.0]
  We have designed an automatic depression detection of online social media users by analyzing their social media behavior. The underlying classifier is made using state-of-art technology in emotional artificial intelligence.
  arXiv  Detail & Related papers  (2023-01-09T09:34:38Z)
• Semantic Similarity Models for Depression Severity Estimation [53.72188878602294]
  This paper presents an efficient semantic pipeline to study depression severity in individuals based on their social media writings. We use test user sentences for producing semantic rankings over an index of representative training sentences corresponding to depressive symptoms and severity levels. We evaluate our methods on two Reddit-based benchmarks, achieving 30% improvement over state of the art in terms of measuring depression severity.
  arXiv  Detail & Related papers  (2022-11-14T18:47:26Z)
• Data set creation and empirical analysis for detecting signs of depression from social media postings [0.0]
  Depression is a common mental illness that has to be detected and treated at an early stage to avoid serious consequences. We developed a gold standard data set that detects the levels of depression as not depressed', moderately depressed' and severely depressed' from the social media postings.
  arXiv  Detail & Related papers  (2022-02-07T10:24:33Z)
• Identification of Twitter Bots based on an Explainable ML Framework: the US 2020 Elections Case Study [72.61531092316092]
  This paper focuses on the design of a novel system for identifying Twitter bots based on labeled Twitter data. Supervised machine learning (ML) framework is adopted using an Extreme Gradient Boosting (XGBoost) algorithm. Our study also deploys Shapley Additive Explanations (SHAP) for explaining the ML model predictions.
  arXiv  Detail & Related papers  (2021-12-08T14:12:24Z)
• Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data [74.60507696087966]
  Mental health conditions remain underdiagnosed even in countries with common access to advanced medical care. One promising data source to help monitor human behavior is daily smartphone usage. We study behavioral markers of daily mood using a recent dataset of mobile behaviors from adolescent populations at high risk of suicidal behaviors.
  arXiv  Detail & Related papers  (2021-06-24T17:46:03Z)
• Two-Faced Humans on Twitter and Facebook: Harvesting Social Multimedia for Human Personality Profiling [74.83957286553924]
  We infer the Myers-Briggs Personality Type indicators by applying a novel multi-view fusion framework, called "PERS" Our experimental results demonstrate the PERS's ability to learn from multi-view data for personality profiling by efficiently leveraging on the significantly different data arriving from diverse social multimedia sources.
  arXiv  Detail & Related papers  (2021-06-20T10:48:49Z)
• DepressionNet: A Novel Summarization Boosted Deep Framework for Depression Detection on Social Media [12.820775223409857]
  Twitter is a popular online social media platform which allows users to share their user-generated content. One of the applications is in automatically discovering mental health problems, e.g., depression. Previous studies to automatically detect a depressed user on online social media have largely relied upon the user behaviour and their linguistic patterns.
  arXiv  Detail & Related papers  (2021-05-23T08:05:53Z)
• Deep Multi-task Learning for Depression Detection and Prediction in Longitudinal Data [50.02223091927777]
  Depression is among the most prevalent mental disorders, affecting millions of people of all ages globally. Machine learning techniques have shown effective in enabling automated detection and prediction of depression for early intervention and treatment. We introduce a novel deep multi-task recurrent neural network to tackle this challenge, in which depression classification is jointly optimized with two auxiliary tasks.
  arXiv  Detail & Related papers  (2020-12-05T05:14:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.