Depression Detection from Social Media Profiles

Stankevich, Maxim; Smirnov, Ivan V.; Kiselnikova, Natalia; Ushakova, Anastasia

doi:10.1007/978-3-030-51913-1_12

Cited by 23 publications

(10 citation statements)

References 15 publications

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“…The research of a team of authors led by M. Stankevich [48] is of particular interest because the social network "Vkontakte" was used as a platform for data collection. In this study, as in most of those already mentioned above, the emphasis was placed on finding correspondences between verbal clues in the subjects' posts and psychometric indicators (Beck's depression scale).…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Assessment of a Person's Social Success Through the Characteristics of Interpersonal Relationships in A Virtual Environment

Shevcov,

Ustin,

Popov

et al. 2023

OBM Neurobiol

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The sudden spread of the COVID-19 pandemic has convincingly proved the role of social networks in human life activity as an actor of interpersonal relations. The need for isolation and the limitation of face-to-face communication between people has significantly transformed the system of interpersonal connections through an extensive increase in social contacts in the virtual environment and the growing importance of online social services. In the study, we addressed the problem of diagnosing the indicator of social success of a personality, reflecting characteristics of its interpersonal relations in offline activity through the attributes of its virtual activity in social networks. The research was based on the methods of social network analysis and traditional ways of psychodiagnostics. Social networks were analyzed using social graphs - mathematical models that describe the characteristics of relationships between users in social networks through various metrics (friends, elements, edges, density, closeness centrality, degree centrality, clustering coefficient, etc.). The study aims to prove the predictive validity of social graph indicators as predictors of personality social success through correlations of graph characteristics reflecting the features of interpersonal relations of a social network user in a virtual environment with the socio-psychological hands of traditional psychodiagnostic tools. The study included 601 subjects. Data was collected using psychological questionnaires and personal profiles from the social network VKontakte. The results of the study showed correlations between several characteristics of social graphs (density, clustering coefficient, closeness centrality, etc.) and several indicators of psychodiagnostic techniques (Dominance index in relationships with others, Organizational abilities, Desire for people, Narcissism, etc.). The significant contribution of the research is to expand the methodological apparatus of psychological science and to open new methods of predicting interpersonal relations of personality through its activity in a virtual environment.

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Section: Theoretical Frameworkmentioning

confidence: 99%

Assessment of a Person's Social Success Through the Characteristics of Interpersonal Relationships in A Virtual Environment

Shevcov,

Ustin,

Popov

et al. 2023

OBM Neurobiol

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“…The authors of the paper [10] increased the performance of machine learning (ML) classifiers by utilizing a dataset of 50,000 tweets that were manually tagged to conduct a binary classification after being acquired from a variety of online and news articles using keywords. An automatic depression detection method was developed in [11], where the authors used ML models to analyze a dataset obtained from the Russian social networking platform Vkontakte. However, because these studies used limited datasets, their models did not achieve high accuracy.…”

Section: Of 16mentioning

confidence: 99%

“…For example, Tadesse et al [18] built a combination model using LDA, LIWCA, and MLP, and achieved 90% accuracy. In [9][10][11], the researchers collected data from Twitter using a method similar to that described here and then employed different ML approaches to categorize suicidal ideas.…”

Section: Introductionmentioning

confidence: 99%

Detecting and Analyzing Suicidal Ideation on Social Media Using Deep Learning and Machine Learning Models

Aldhyani

Alsubari

Alshebami

et al. 2022

IJERPH

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Individuals who suffer from suicidal ideation frequently express their views and ideas on social media. Thus, several studies found that people who are contemplating suicide can be identified by analyzing social media posts. However, finding and comprehending patterns of suicidal ideation represent a challenging task. Therefore, it is essential to develop a machine learning system for automated early detection of suicidal ideation or any abrupt changes in a user’s behavior by analyzing his or her posts on social media. In this paper, we propose a methodology based on experimental research for building a suicidal ideation detection system using publicly available Reddit datasets, word-embedding approaches, such as TF-IDF and Word2Vec, for text representation, and hybrid deep learning and machine learning algorithms for classification. A convolutional neural network and Bidirectional long short-term memory (CNN–BiLSTM) model and the machine learning XGBoost model were used to classify social posts as suicidal or non-suicidal using textual and LIWC-22-based features by conducting two experiments. To assess the models’ performance, we used the standard metrics of accuracy, precision, recall, and F1-scores. A comparison of the test results showed that when using textual features, the CNN–BiLSTM model outperformed the XGBoost model, achieving 95% suicidal ideation detection accuracy, compared with the latter’s 91.5% accuracy. Conversely, when using LIWC features, XGBoost showed better performance than CNN–BiLSTM.

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“…Additionally, some authors have widely analysed the brain signals (Sharma et al 2018a , 2021b ; Ay et al 2019 ; Faust et al 2014 ; Acharya et al 2015 ; Bairy et al 2016 ; Liao et al 2017 ; Cai 2018 ; Uyulan et al 2020 ; Qiao et al 2020 ; Thoduparambil et al 2020 ; Saeedi et al 2020 , 2021 ; Xie, et al 2020 ; Qayyum et al 2020 ; Khan et al 2021 ; Seal et al 2021 ; Bai et al 2021 ). Some authors have also scoured and analysed texts from social media such as Twitter, Facebook and Reddit (Thoduparambil 2020 ; Saeedi 2020 ; Xie et al 2020 ; Islam et al 2018 ; Eichstaedt et al 2018 ; Cacheda et al 2019 ; Trotzek et al 2020 ; Owen et al 2020 ; Ramírez-Cifuentes et al 2020 ; Safa et al 2021 ; Tong et al 2022 ; Gupta et al 2022 ; Stankevich et al 2018 , 2020 ; Hussain et al 2019 ; Alsagri and Ykhlef 2020 ). A few authors have explored the combination of audio and textural features (Alhanai et al 2018 ; Park and Moon 2022 ), audio and visual recordings (Yang et al 2017 ; Mallol-Ragolta et al 2020 ; Saidi et al 2020 ) while some others have used unique methods such as a combination of time series signal features (Zhou et al 2015 ), measurement of electrodermal activity (Kim et al 2018 ), magnetic resonance imaging (Kipli et al 2013 ; Yamashita et al 2020 ; Boeke et al 2020 ), kinematic skeleton data (Li et al 2021 ), photo-plethysmogram(PPG) signal features extraction (Khandoker 2017 ), gait characteristics (Wang et al 2021 ) and optical flow visual-based method (Zhu et al 2018 ).…”

Section: Summarised Studiesmentioning

confidence: 99%

Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review

et al. 2022

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Epidemiological studies report high levels of anxiety and depression amongst adolescents. These psychiatric conditions and complex interplays of biological, social and environmental factors are important risk factors for suicidal behaviours and suicide, which show a peak in late adolescence and early adulthood. Although deaths by suicide have fallen globally in recent years, suicide deaths are increasing in some countries, such as the US. Suicide prevention is a challenging global public health problem. Currently, there aren’t any validated clinical biomarkers for suicidal diagnosis, and traditional methods exhibit limitations. Artificial intelligence (AI) is budding in many fields, including in the diagnosis of medical conditions. This review paper summarizes recent studies (past 8 years) that employed AI tools for the automated detection of depression and/or anxiety disorder and discusses the limitations and effects of some modalities. The studies assert that AI tools produce promising results and could overcome the limitations of traditional diagnostic methods. Although using AI tools for suicidal ideation exhibits limitations, these are outweighed by the advantages. Thus, this review article also proposes extracting a fusion of features such as facial images, speech signals, and visual and clinical history features from deep models for the automated detection of depression and/or anxiety disorder in individuals, for future work. This may pave the way for the identification of individuals with suicidal thoughts.

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Depression Detection from Social Media Profiles

Cited by 23 publications

References 15 publications

Assessment of a Person's Social Success Through the Characteristics of Interpersonal Relationships in A Virtual Environment

Assessment of a Person's Social Success Through the Characteristics of Interpersonal Relationships in A Virtual Environment

Detecting and Analyzing Suicidal Ideation on Social Media Using Deep Learning and Machine Learning Models

Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review

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