Depression Detection from Social Media Profiles

2020 ◽  
pp. 181-194 ◽  
Author(s):  
Maxim Stankevich ◽  
Ivan Smirnov ◽  
Natalia Kiselnikova ◽  
Anastasia Ushakova
Keyword(s):  
Social Media ◽  
Depression Detection

Depression Detection on Social Media with Reinforcement Learning

2019 ◽  
pp. 613-624
Author(s):  
Tao Gui ◽  
Qi Zhang ◽  
Liang Zhu ◽  
Xu Zhou ◽  
Minlong Peng ◽  
...  
Keyword(s):  
Social Media ◽  
Reinforcement Learning ◽  
Depression Detection

scholarly journals Automatic Construction of a Depression-Domain Lexicon Based on Microblogs: Text Mining Study

10.2196/17650 ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. e17650
Author(s):  
Genghao Li ◽  
Bing Li ◽  
Langlin Huang ◽  
Sibing Hou
Keyword(s):  
Social Media ◽  
Social Networking Sites ◽  
Label Propagation ◽  
World Health ◽  
Support Vector ◽  
Depression Detection ◽  
Text Information ◽  
The Rich ◽  
Health Organization ◽  
Future Work

Background According to a World Health Organization report in 2017, there was almost one patient with depression among every 20 people in China. However, the diagnosis of depression is usually difficult in terms of clinical detection owing to slow observation, high cost, and patient resistance. Meanwhile, with the rapid emergence of social networking sites, people tend to share their daily life and disclose inner feelings online frequently, making it possible to effectively identify mental conditions using the rich text information. There are many achievements regarding an English web-based corpus, but for research in China so far, the extraction of language features from web-related depression signals is still in a relatively primary stage. Objective The purpose of this study was to propose an effective approach for constructing a depression-domain lexicon. This lexicon will contain language features that could help identify social media users who potentially have depression. Our study also compared the performance of detection with and without our lexicon. Methods We autoconstructed a depression-domain lexicon using Word2Vec, a semantic relationship graph, and the label propagation algorithm. These two methods combined performed well in a specific corpus during construction. The lexicon was obtained based on 111,052 Weibo microblogs from 1868 users who were depressed or nondepressed. During depression detection, we considered six features, and we used five classification methods to test the detection performance. Results The experiment results showed that in terms of the F1 value, our autoconstruction method performed 1% to 6% better than baseline approaches and was more effective and steadier. When applied to detection models like logistic regression and support vector machine, our lexicon helped the models outperform by 2% to 9% and was able to improve the final accuracy of potential depression detection. Conclusions Our depression-domain lexicon was proven to be a meaningful input for classification algorithms, providing linguistic insights on the depressive status of test subjects. We believe that this lexicon will enhance early depression detection in people on social media. Future work will need to be carried out on a larger corpus and with more complex methods.

scholarly journals On Text Preprocessing for Early Detection of Depression on Social Media

2020 ◽  
Author(s):  
José Figueredo ◽  
Rodrigo Calumby
Keyword(s):  
Public Health ◽  
Social Media ◽  
Text Data ◽  
Evaluation Measures ◽  
Emotion Words ◽  
Depression Detection ◽  
Text Preprocessing ◽  
The Impact ◽  
Detection Of Depression ◽  
Support Research

Depression is a serious challenge to public health. Many of those who suffer from this disease use social media for information or relief. The text data produced by these users can be used to support research in this field. However, this raw information is not always suitable for use directly in machine learning. Hence, a comparative analysis was performed between different preprocessing techniques to verify the impact on the effectiveness of early depression detection on social media. The results show that the preprocessing contributes to an increase in the prediction effectiveness. Moreover, the mapping of emoticons to real emotion words was decisive to improve not only model’s effectiveness, but also to keep the balance between different evaluation measures.

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

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
José de Jesús Titla-Tlatelpa ◽  
Rosa María Ortega-Mendoza ◽  
Manuel Montes-y-Gómez ◽  
Luis Villaseñor-Pineda
Keyword(s):  
Social Media ◽  
Main Idea ◽  
Text Representation ◽  
Computational Tools ◽  
New Approach ◽  
Linguistic Markers ◽  
Depression Detection ◽  
Benchmark Datasets ◽  
Discriminative Value ◽  
Severe Mental Health Problem

AbstractDepression is a severe mental health problem. Due to its relevance, the development of computational tools for its detection has attracted increasing attention in recent years. In this context, several research works have addressed the problem using word-based approaches (e.g., a bag of words). This type of representation has shown to be useful, indicating that words act as linguistic markers of depression. However, we believe that in addition to words, their contexts contain implicitly valuable information that could be inferred and exploited to enhance the detection of signs of depression. Specifically, we explore the use of user’s characteristics and the expressed sentiments in the messages as context insights. The main idea is that the words’ discriminative value depends on the characteristics of the person who is writing and on the polarity of the messages where they occur. Hence, this paper introduces a new approach based on specializing the framework of classification to profiles of users (e.g., males or women) and considering the sentiments expressed in the messages through a new text representation that captures their polarity (e.g., positive or negative). The proposed approach was evaluated on benchmark datasets from social media; the results achieved are encouraging, since they outperform those of state-of-the-art corresponding to computationally more expensive methods.

scholarly journals Depression Detection via Harvesting Social Media: A Multimodal Dictionary Learning Solution

2017 ◽  
Author(s):  
Guangyao Shen ◽  
Jia Jia ◽  
Liqiang Nie ◽  
Fuli Feng ◽  
Cunjun Zhang ◽  
...  
Keyword(s):  
Social Media ◽  
Dictionary Learning ◽  
Large Scale ◽  
Clinical Depression ◽  
Daily Lives ◽  
Face To Face ◽  
Large Scale Dataset ◽  
Depression Detection ◽  
Series Of Experiments ◽  
Global Burden Of Diseases

Depression is a major contributor to the overall global burden of diseases. Traditionally, doctors diagnose depressed people face to face via referring to clinical depression criteria. However, more than 70% of the patients would not consult doctors at early stages of depression, which leads to further deterioration of their conditions. Meanwhile, people are increasingly relying on social media to disclose emotions and sharing their daily lives, thus social media have successfully been leveraged for helping detect physical and mental diseases. Inspired by these, our work aims to make timely depression detection via harvesting social media data. We construct well-labeled depression and non-depression dataset on Twitter, and extract six depression-related feature groups covering not only the clinical depression criteria, but also online behaviors on social media. With these feature groups, we propose a multimodal depressive dictionary learning model to detect the depressed users on Twitter. A series of experiments are conducted to validate this model, which outperforms (+3% to +10%) several baselines. Finally, we analyze a large-scale dataset on Twitter to reveal the underlying online behaviors between depressed and non-depressed users.

scholarly journals Automatic Construction of a Depression-Domain Lexicon Based on Microblogs: Text Mining Study (Preprint)

2019 ◽  
Author(s):  
Genghao Li ◽  
Bing Li ◽  
Langlin Huang ◽  
Sibing Hou
Keyword(s):  
Social Media ◽  
Social Networking Sites ◽  
Label Propagation ◽  
World Health ◽  
Support Vector ◽  
Depression Detection ◽  
Text Information ◽  
The Rich ◽  
Health Organization ◽  
Future Work

BACKGROUND According to a World Health Organization report in 2017, there was almost one patient with depression among every 20 people in China. However, the diagnosis of depression is usually difficult in terms of clinical detection owing to slow observation, high cost, and patient resistance. Meanwhile, with the rapid emergence of social networking sites, people tend to share their daily life and disclose inner feelings online frequently, making it possible to effectively identify mental conditions using the rich text information. There are many achievements regarding an English web-based corpus, but for research in China so far, the extraction of language features from web-related depression signals is still in a relatively primary stage. OBJECTIVE The purpose of this study was to propose an effective approach for constructing a depression-domain lexicon. This lexicon will contain language features that could help identify social media users who potentially have depression. Our study also compared the performance of detection with and without our lexicon. METHODS We autoconstructed a depression-domain lexicon using Word2Vec, a semantic relationship graph, and the label propagation algorithm. These two methods combined performed well in a specific corpus during construction. The lexicon was obtained based on 111,052 Weibo microblogs from 1868 users who were depressed or nondepressed. During depression detection, we considered six features, and we used five classification methods to test the detection performance. RESULTS The experiment results showed that in terms of the F1 value, our autoconstruction method performed 1% to 6% better than baseline approaches and was more effective and steadier. When applied to detection models like logistic regression and support vector machine, our lexicon helped the models outperform by 2% to 9% and was able to improve the final accuracy of potential depression detection. CONCLUSIONS Our depression-domain lexicon was proven to be a meaningful input for classification algorithms, providing linguistic insights on the depressive status of test subjects. We believe that this lexicon will enhance early depression detection in people on social media. Future work will need to be carried out on a larger corpus and with more complex methods.

scholarly journals Early Detection of Depression Indication from Social Media Analysis

2021 ◽  
Vol 40 ◽  
pp. 03029
Author(s):  
Maharukh Syed ◽  
Meera Narvekar
Keyword(s):  
Social Media ◽  
Early Detection ◽  
Media Analysis ◽  
Social Media Analysis ◽  
The Past ◽  
Course Of Action ◽  
Negative Thoughts ◽  
Depression Detection ◽  
Suicidal Thoughts And Behaviors ◽  
Detection Of Depression

Depression that stems through social media has been steadily growing since the past few years but with the current inclination towards social media reliance it is highly imperative to detect the early signs. Continuous observation of a user's social media interests and activities may highlight suspicious and negative thoughts. This observation can help in understanding their future course of action and also indicate any suicidal thoughts and behaviors. By using the machine learning models, early indications of depression detection can be addressed. This work studies different word embedding techniques for early detection of depression from social media posts. Further, this work develops a model using various NLP processes in order to address the issue of early detection. The recommendations can be useful as a Decision Support System for counselors, psychologist and also can be of good use by the cyber-crime cell department for criminal investigations.

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