4.5 Review

Detecting Depression Signs on Social Media: A Systematic Literature Review

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Hardware & Architecture

Automatic detection of depression symptoms in twitter using multimodal analysis

Ramin Safa et al.

Summary: The paper proposes an approach to predict depression symptoms by analyzing social platform data, achieving high accuracy through analyzing tweets and user profile text. The authors believe that performance improvements can be achieved by limiting the user domain or presence of clinical information.

JOURNAL OF SUPERCOMPUTING (2022)

Article Computer Science, Artificial Intelligence

Multimodal depression detection on instagram considering time interval of posts

Chun Yueh Chiu et al.

Summary: Depression is a common and serious mental disorder. With the rapid development of social media, researchers now have access to a vast amount of data for analysis.

JOURNAL OF INTELLIGENT INFORMATION SYSTEMS (2021)

Article Clinical Neurology

Detecting changes in attitudes toward depression on Chinese social media: A text analysis

Lixia Yu et al.

Summary: The study investigated public attitudes towards depression and trends over three years using big data analysis of social media posts in China. Results showed an increase in terms related to emotion, cognition, and conjunctions over time, with common themes including the severe effects of depression, stigma, combating stigma, appeals for understanding, and providing support. References to social support for depressed individuals increased, while mentions of severe consequences of depression decreased over time.

JOURNAL OF AFFECTIVE DISORDERS (2021)

Article Biology

A textual-based featuring approach for depression detection using machine learning classifiers and social media texts

Raymond Chiong et al.

Summary: The study aims to detect signs of depression in social media users by using machine learning to analyze their posts without specific keywords. By examining various text preprocessing methods and machine learning classifiers, a generalized approach for depression detection using social media texts was proposed. Analysis of public Twitter datasets and non-Twitter depression-class-only datasets suggests that the approach can effectively detect depression via social media texts.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

Article Mathematics, Interdisciplinary Applications

A profile-based sentiment-aware approach for depression detection in social media

Jose de Jesus Titla-Tlatelpa et al.

Summary: Depression is a serious mental health issue that has gained increasing attention in the development of computational tools for its detection. In addition to words, the context of words contains valuable information that can enhance the detection of depression signs. By focusing on characteristics of users and sentiments expressed in messages, a new text representation method can improve the accuracy of detecting depression symptoms.

EPJ DATA SCIENCE (2021)

Article Communication

The Effects of Social Media Use on Preventive Behaviors during Infectious Disease Outbreaks: The Mediating Role of Self-relevant Emotions and Public Risk Perception

Sang-Hwa Oh et al.

Summary: This study utilized data from the 2015 MERS-CoV outbreak in South Korea to explore the relationships among social media use, risk perception, and preventive behaviors. The findings show that social media use can significantly increase preventive behaviors through the emotions of fear and anger, as well as the public's risk perception.

HEALTH COMMUNICATION (2021)

Article Computer Science, Artificial Intelligence

A deep architecture for depression detection using posting, behavior, and living environment data

Min Yen Wu et al.

JOURNAL OF INTELLIGENT INFORMATION SYSTEMS (2020)

Article Computer Science, Artificial Intelligence

Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequences

Marcel Trotzek et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2020)

Article Medicine, General & Internal

A Novel Coronavirus from Patients with Pneumonia in China, 2019

Na Zhu et al.

NEW ENGLAND JOURNAL OF MEDICINE (2020)

Article Multidisciplinary Sciences

Multimodal mental health analysis in social media

Amir Hossein Yazdavar et al.

PLOS ONE (2020)

Article Environmental Sciences

A Big Data Platform for Real Time Analysis of Signs of Depression in Social Media

Rodrigo Martinez-Castano et al.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH (2020)

Article Information Science & Library Science

A big data analytics framework for detecting user-level depression from social networks

Xingwei Yang et al.

INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT (2020)

Review Computer Science, Artificial Intelligence

A review of feature selection techniques in sentiment analysis

Siti Rohaidah Ahmad et al.

INTELLIGENT DATA ANALYSIS (2019)

Article Health Care Sciences & Services

Detecting Signs of Depression in Tweets in Spanish: Behavioral and Linguistic Analysis

Angela Leis et al.

JOURNAL OF MEDICAL INTERNET RESEARCH (2019)

Article Computer Science, Artificial Intelligence

A text classification framework for simple and effective early depression detection over social media streams

Sergio G. Burdisso et al.

EXPERT SYSTEMS WITH APPLICATIONS (2019)

Article Information Science & Library Science

Novel insights into views towards H1N1 during the 2009 Pandemic: a thematic analysis of Twitter data

Wasim Ahmed et al.

HEALTH INFORMATION AND LIBRARIES JOURNAL (2019)

Proceedings Paper Computer Science, Information Systems

Keyword-Driven Depressive Tendency Model for Social Media Posts

Hsiao-Wei Hu et al.

BUSINESS INFORMATION SYSTEMS, BIS 2019, PT II (2019)

Proceedings Paper Engineering, Electrical & Electronic

Predicting Social Network Users with Depression from Simulated Temporal Data

Akkapon Wongkoblap et al.

PROCEEDINGS OF 18TH INTERNATIONAL CONFERENCE ON SMART TECHNOLOGIES (IEEE EUROCON 2019) (2019)

Article Computer Science, Information Systems

Detection of Depression-Related Posts in Reddit Social Media Forum

Michael M. Tadesse et al.

IEEE ACCESS (2019)

Article Clinical Neurology

Detecting depression stigma on social media: A linguistic analysis

Ang Li et al.

JOURNAL OF AFFECTIVE DISORDERS (2018)

Article Health Care Sciences & Services

Exploring the Utility of Community-Generated Social Media Content for Detecting Depression: An Analytical Study on Instagram

Benjamin J. Ricard et al.

JOURNAL OF MEDICAL INTERNET RESEARCH (2018)

Proceedings Paper Computer Science, Artificial Intelligence

Measuring the Latency of Depression Detection in Social Media

Farig Sadeque et al.

WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (2018)

Article

SOS-DR: a social warning system for detecting users at high risk of depression

Chih-Hua Tai et al.

Personal and Ubiquitous Computing (2017)

Article Computer Science, Artificial Intelligence

Natural language processing in mental health applications using non-clinical texts

Rafael A. Calvo et al.

NATURAL LANGUAGE ENGINEERING (2017)

Article Mathematics, Interdisciplinary Applications

Instagram photos reveal predictive markers of depression

Andrew G. Reece et al.

EPJ DATA SCIENCE (2017)

Proceedings Paper Computer Science, Information Systems

Towards Suicide Prevention: Early Detection of Depression on Social Media

Victor Leiva et al.

INTERNET SCIENCE (2017)

Proceedings Paper Biochemical Research Methods

Extracting Depression Symptoms from Social Networks and Web Blogs via Text Mining

Long Ma et al.

BIOINFORMATICS RESEARCH AND APPLICATIONS (ISBRA 2017) (2017)

Review Behavioral Sciences

Detecting depression and mental illness on social media: an integrative review

Sharath Chandra Guntuku et al.

CURRENT OPINION IN BEHAVIORAL SCIENCES (2017)

Article Computer Science, Artificial Intelligence

Sentiment analysis on social campaign Swachh Bharat Abhiyan'' using unigram method

Devendra K. Tayal et al.

AI & SOCIETY (2017)

Article Psychology, Multidisciplinary

Unfolding the notes from the walls: Adolescents' depression manifestations on Facebook

Yaakov Ophir et al.

COMPUTERS IN HUMAN BEHAVIOR (2017)

Article Public, Environmental & Occupational Health

Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter

Abeed Sarker et al.

DRUG SAFETY (2016)

Article Social Sciences, Interdisciplinary

How the machine 'thinks': Understanding opacity in machine learning algorithms

Jenna Burrell

BIG DATA & SOCIETY (2016)

Review Psychology, Multidisciplinary

Social media, big data, and mental health: current advances and ethical implications

Mike Conway et al.

CURRENT OPINION IN PSYCHOLOGY (2016)

Article Public, Environmental & Occupational Health

Detecting themes of public concern: A text mining analysis of the Centers for Disease Control and Prevention's Ebola live Twitter chat

Allison J. Lazard et al.

AMERICAN JOURNAL OF INFECTION CONTROL (2015)

Article Public, Environmental & Occupational Health

What can we learn about the Ebola outbreak from tweets?

Michelle Odlum et al.

AMERICAN JOURNAL OF INFECTION CONTROL (2015)

Article Biochemical Research Methods

Supervised, semi-supervised and unsupervised inference of gene regulatory networks

Stefan R. Maetschke et al.

BRIEFINGS IN BIOINFORMATICS (2014)

Proceedings Paper Computer Science, Artificial Intelligence

Sentiment analysis: towards a tool for analysing real-time students feedback

Nabeela Altrabsheh et al.

2014 IEEE 26TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI) (2014)

Article Psychiatry

DEPRESSIVE SYMPTOMS AND THEIR SOCIAL CONTEXTS: A QUALITATIVE SYSTEMATIC LITERATURE REVIEW OF CONTEXTUAL INTERVENTIONS

Laura Gottlieb et al.

INTERNATIONAL JOURNAL OF SOCIAL PSYCHIATRY (2011)

Review Medicine, General & Internal

Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement

David Moher et al.

PLOS MEDICINE (2009)

Article Computer Science, Software Engineering

Lessons from applying the systematic literature review process within the software engineering domain

Pearl Brereton et al.

JOURNAL OF SYSTEMS AND SOFTWARE (2007)