Generating a Mental Health Curve for Monitoring Depression in Real Time by Incorporating Multimodal Feature Analysis Through Social Media Interactions

Abstract

The coronavirus pandemic has led to a dramatic increase in depression cases worldwide. Several people are utilizing social media to share their depression or suicidal thoughts. Thus, the major goal of the proposed study is to examine Twitter posts by users and identify features that may indicate depressed symptoms among online users. A numerical metric for each user is proposed based on the sentiment value of their tweets, and it is demonstrated that this feature can detect depression with good accuracy by using several machine learning classifiers. The paper proposes a novel method for measuring the mental health index of an individual by combining the sentiment score with multimodal features extracted from his online activities. A real-time curve is generated using this index that can monitor a person's mental health in real time and offer real-time information about his state. The proposed model shows an accuracy of 89% using SVM, and proper feature selection is very essential for obtaining good performance.

Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches

“…In addition, researchers found individuals with depression, suicidal intent, and schizophrenia exhibiting a pronounced expression of negative emotions compared to healthy controls. This observation is substantiated by various features, including the frequency of negative words [ 20 , 118 , 123 , 204 , 220 ] and negative emoticons [ 130 , 188 ], as well as negative sentiment scores of overall sentences [ 121 ].…”

“…Specifically, Chatterjee et al [ 188 ] uncovered that 56% of suicidal samples demonstrated the association between low agreeableness and high neuroticism scores with increased suicide ideation, compared to most healthy controls with high agreeableness and optimism scores. Another study [ 130 ] also found that individuals with depressive symptoms generally have higher neuroticism and lower optimism scores.…”

“…Research attempts have demonstrated a significant association between time spent on social media platforms [ 116 ] and depressive symptoms. This claim is supported by compelling evidence indicating that a substantial proportion of individuals with depression (76% [ 130 ]) and suicidal intent (73% [ 188 ]) engaged in more active posting activities on various social media platforms, including Instagram [ 125 ], Twitter [ 20 , 118 ], Reddit [ 130 , 188 ], and Weibo [ 189 ], particularly at midnight. Some authors [ 20 , 118 , 134 ] intuited this behavior as potentially linked to sleeping problems or insomnia.…”

“…Another study [130] also found that individuals with depressive symptoms generally have higher neuroticism and lower optimism scores.…”

“… Features Tools Studies Feature Category Count of words: general, condition-specific (depressed, suicidal, eating disorder-related) keywords, emojis N/A [ 20 , 104 , 109 , 123 , 126 , 127 , 130 , 133 , 134 , 137 , 145 , 146 , 187 , 188 , 218 , 219 ] Linguistic Words referring to social processes (e.g., reference to family, friends, social affiliation), and psychological states (e.g., negative/positive emotions) Linguistic Inquiry and Word Count (LIWC) [ 278 ], LIWC 2007 Spanish dictionary [ 369 ], Chinese Suicide Dictionary [ 370 ], Chinese LIWC [ 371 ], TextMind [ 372 ], Suite of Automatic Linguistic Analysis Tools (SALAT) [ 279 ]—Simple Natural Language Processing (SiNLP) [ 373 ] [ 20 , 79 , 109 , 118 , 121 , 128 , 186 , 194 , 196 , ...…”