What Twitter Profile and Posted Images Reveal about Depression and Anxiety

Guntuku, Sharath Chandra; Preoţiuc-Pietro, Daniel; Eichstaedt, Johannes C.; Ungar, Lyle H.

doi:10.1609/icwsm.v13i01.3225

Cited by 38 publications

(10 citation statements)

References 45 publications

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“…Moreover, machine learning algorithms have been employed to decipher patterns and indicators of depression from visual content shared on these platforms. Such analyses often encompass aspects like colors, objects, scenes, and overall aesthetics [26,30,61].…”

Section: Smartphones and Mental Healthmentioning

confidence: 99%

MoodCapture: Depression Detection using In-the-Wild Smartphone Images

Nepal,

Pillai,

Wang

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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MoodCapture presents a novel approach that assesses depression based on images automatically captured from the front-facing camera of smartphones as people go about their daily lives. We collect over 125,000 photos in the wild from N=177 participants diagnosed with major depressive disorder for 90 days. Images are captured naturalistically while participants respond to the PHQ-8 depression survey question: "I have felt down, depressed, or hopeless". Our analysis explores important image attributes, such as angle, dominant colors, location, objects, and lighting. We show that a random forest trained with face landmarks can classify samples as depressed or non-depressed and predict raw PHQ-8 scores effectively. Our posthoc analysis provides several insights through an ablation study, feature importance analysis, and bias assessment. Importantly, we evaluate user concerns about using MoodCapture to detect depression based on sharing photos, providing critical insights into privacy concerns that inform the future design of in-the-wild image-based mental health assessment tools. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing; • Applied computing → Health informatics.

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Section: Smartphones and Mental Healthmentioning

confidence: 99%

MoodCapture: Depression Detection using In-the-Wild Smartphone Images

Nepal,

Pillai,

Wang

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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“…We used pretrained language models obtained from large-scale data sets to derive the standardized psychological constructs of individuals from their language each month. Specifically, we obtained sentiment, loneliness, 23 anxiety, 24 anger, depression, 25 and positive and negative emotions. These models have been validated both in-sample and out-of-the-sample across multiple populations before and during COVID-19.…”

Section: Linguistic Attributesmentioning

confidence: 99%

Investigating Social Media to Evaluate Emergency Medicine Physicians’ Emotional Well-being During COVID-19

et al. 2023

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ImportanceEmergency medicine (EM) physicians experience tremendous emotional health strain, which has been exacerbated during COVID-19, and many have taken to social media to express themselves.ObjectiveTo analyze social media content from academic EM physicians and resident physicians to investigate changes in content and language as indicators of their emotional well-being.Design, Setting, and ParticipantsThis cross-sectional study used machine learning and natural language processing of Twitter posts from self-described academic EM physicians and resident physicians between March 2018 and March 2022. Participants included academic EM physicians and resident physicians with publicly accessible posts (at least 300 total words across the posts) from the US counties with the top 10 COVID-19 case burdens. Data analysis was performed from June to September 2022.ExposureBeing an EM physician or resident physician who posted on Twitter.Main Outcomes and MeasuresSocial media content themes during the prepandemic period, during the pandemic, and across the phases of the pandemic were analyzed. Psychological constructs evaluated included anxiety, anger, depression, and loneliness. Positive and negative language sentiment within posts was measured.ResultsThis study identified 471 physicians with a total of 198 867 posts (mean [SD], 11 403 [18 998] words across posts; median [IQR], 3445 [1100-11 591] words across posts). The top 5 prepandemic themes included free open-access medical education (Cohen d, 0.44; 95% CI, 0.38-0.50), residency education (Cohen d, 0.43; 95% CI, 0.37-0.49), gun violence (Cohen d, 0.37; 95% CI, 0.32-0.44), quality improvement in health care (Cohen d, 0.33; 95% CI, 0.27-0.39), and professional resident associations (Cohen d, 0.33; 95% CI, 0.27-0.39). During the pandemic, themes were significantly related to healthy behaviors during COVID-19 (Cohen d, 0.83; 95% CI, 0.77-0.90), pandemic response (Cohen d, 0.71; 95% CI, 0.65-0.77), vaccines and vaccination (Cohen d, 0.60; 95% CI, 0.53-0.66), unstable housing and homelessness (Cohen d, 0.40; 95% CI, 0.34-0.47), and emotional support for others (Cohen d, 0.40; 95% CI, 0.34-0.46). Across the phases of the pandemic, thematic content within social media posts changed significantly. Compared with the prepandemic period, there was significantly less positive, and concordantly more negative, language used during COVID-19. Estimates of loneliness, anxiety, anger, and depression also increased significantly during COVID-19.Conclusions and RelevanceIn this cross-sectional study, key thematic shifts and increases in language related to anxiety, anger, depression, and loneliness were identified in the content posted on social media by academic EM physicians and resident physicians during the pandemic. Social media may provide a real-time and evolving landscape to evaluate thematic content and linguistics related to emotions and sentiment for health care workers.

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“…Due to the mental health stigma, most people with psychological problems refuse to present themselves physically before consultants to get advice [23,24]. Based on the literature, many types of research have been conducted based on Facebook [9,[25][26][27][28][29][30], Twitter [18,[31][32][33][34] and Reddit [35][36][37][38][39] for identification of depression from textual content. When it comes to social media platforms and OSF, there is a greater propensity to discuss psychological issues on specific OSF, because people use social media platforms to share information about daily lives, not just about specific mental disorders.…”

Section: How Are Online Mental Support Forums Getting Involved With D...mentioning

confidence: 99%

“…Social media and online peer communities act as digital platforms that provide information, experiential advice, and support [15] to the people with depression. Moreover, numerous studies have been conducted based on social media platforms to identify depression and extracted emotions and feelings expressed by people [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Investigating Machine Learning & Natural Language Processing Techniques Applied for Predicting Depression Disorder from Online Support Forums: A Systematic Literature Review

2021

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Depression is a common mental health disorder that affects an individual’s moods, thought processes and behaviours negatively, and disrupts one’s ability to function optimally. In most cases, people with depression try to hide their symptoms and refrain from obtaining professional help due to the stigma related to mental health. The digital footprint we all leave behind, particularly in online support forums, provides a window for clinicians to observe and assess such behaviour in order to make potential mental health diagnoses. Natural language processing (NLP) and Machine learning (ML) techniques are able to bridge the existing gaps in converting language to a machine-understandable format in order to facilitate this. Our objective is to undertake a systematic review of the literature on NLP and ML approaches used for depression identification on Online Support Forums (OSF). A systematic search was performed to identify articles that examined ML and NLP techniques to identify depression disorder from OSF. Articles were selected according to the PRISMA workflow. For the purpose of the review, 29 articles were selected and analysed. From this systematic review, we further analyse which combination of features extracted from NLP and ML techniques are effective and scalable for state-of-the-art Depression Identification. We conclude by addressing some open issues that currently limit real-world implementation of such systems and point to future directions to this end.

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What Twitter Profile and Posted Images Reveal about Depression and Anxiety

Cited by 38 publications

References 45 publications

MoodCapture: Depression Detection using In-the-Wild Smartphone Images

MoodCapture: Depression Detection using In-the-Wild Smartphone Images

Investigating Social Media to Evaluate Emergency Medicine Physicians’ Emotional Well-being During COVID-19

Investigating Machine Learning & Natural Language Processing Techniques Applied for Predicting Depression Disorder from Online Support Forums: A Systematic Literature Review

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