Predicting Depression with Social Media Images

Stankevich, Maxim; Ignatiev, N. A.; Smirnov, Ivan V.

doi:10.5220/0009168602350240

Cited by 7 publications

(6 citation statements)

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Section: A Machine Learning-based Depression Detectionmentioning

confidence: 99%

“…De Choudhury et al [5] have investigated to predict depression for social media users based on Twitter. To get the ground truth of users' suffered depression history, De Choudhury et al [5] adopted crowdsourcing to collect Twitter users who have been diagnosed with clinical major depressive disorder (MDD) based on the Center for Epidemiologic Studies Depression Scale (CES-D2) screening test. Then, to link the depression symptoms with the social media data, they extracted several measures, such as user engagement and emotion, egocentric social graph, linguistic style, depressive language user, and the mentions of antidepressant medications from users' social media history for one year.…”

Section: A Machine Learning-based Depression Detectionmentioning

confidence: 99%

“…NSW has a large population of around 8.1 million people according to the census in September 2019 from the Australian Bureau of Statistics. 5 The NSW's capital city Sydney is Australia's most populated city with a population of over 5.3 million people. The LGAs are the third tier of government in the Australian state (the three tiers are federal, state, and local government).…”

Section: Data a Study Locationmentioning

confidence: 99%

“…TF-IDF is a numerical statistic metric to reflect the importance of a word in a list or corpus, which is widely studied in relevant work. De Choudhury et al [5] applied TF-IDF to words in Wikipedia to remove extremely frequent terms and then used the top words with high TF-IDF. The approach helped them to assess the frequency of uses of depression terms that appear on each user's Twitter posts on a given day.…”

Section: Tf-idfmentioning

confidence: 99%

“…Hence, many research works have been done to extract features, such as user's social activity behaviors, user profiles, and texts from their social media posts for depression detection using machine learning techniques [5]- [9]. For example, De Choudhury et al [5] proposed to predict depression for social media users based on Twitter using a support vector machine (SVM) for prediction based on manually labeled training data.…”

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confidence: 99%

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Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia

Zhou

Zogan

Yang

et al. 2021

IEEE Trans. Comput. Soc. Syst.

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The recent Coronavirus Infectious Disease 2019 (COVID-19) pandemic has caused an unprecedented impact across the globe. We have also witnessed millions of people with increased mental health issues, such as depression, stress, worry, fear, disgust, sadness, and anxiety, which have become one of the major public health concerns during this severe health crisis. Depression can cause serious emotional, behavioral, and physical health problems with significant consequences, both personal and social costs included. This article studies community depression dynamics due to the COVID-19 pandemic through user-generated content on Twitter. A new approach based on multimodal features from tweets and term frequency-inverse document frequency (TF-IDF) is proposed to build depression classification models. Multimodal features capture depression cues from emotion, topic, and domain-specific perspectives. We study the problem using recently scraped tweets from Twitter users emanating from the state of New South Wales in Australia. Our novel classification model is capable of extracting depression polarities that may be affected by COVID-19 and related events during the COVID-19 period. The results found that people became more depressed after the outbreak of COVID-19. The measures implemented by the government, such as the state lockdown, also increased depression levels.

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Section: A Machine Learning-based Depression Detectionmentioning

confidence: 99%

Section: A Machine Learning-based Depression Detectionmentioning

confidence: 99%

Section: Data a Study Locationmentioning

confidence: 99%

Section: Tf-idfmentioning

confidence: 99%

mentioning

confidence: 99%

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Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia

Zhou

Zogan

Yang

et al. 2021

IEEE Trans. Comput. Soc. Syst.

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Tracing the Emotional Roadmap of Depressive Users on Social Media Through Sequential Pattern Mining

et al. 2021

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Depression is one of the most growing health disorders, generating social and economic problems globally. The affective computing models focus on analyzing unique user posts, not observing temporal behavior patterns, which are essential to track changes and the evolution of emotional behavior and user context, that involves the persistent analysis of feelings and characteristics over time. This article proposes the TROAD framework for longitudinal recognition of sequential patterns from depressive users on social media. The framework identifies the best interval to analyze every user activity, extracts emotional and contextual features from user data, and models the features into time windows to recognize sequential patterns from depressive user behavior. The main characteristics of the users found in the top-10 rules are negative emotions: violence, pain, shame, depression, sadness, and silence. We obtained strong sequence patterns with a minimum of 70% of support, 81% of confidence, and 69% sequential confidence, considering periods of silence between users' posts. Without considering silent periods, the rules showed 70%, 86%, and 38% of support, confidence, and sequential confidence. TROAD computational approach is a promising tool for clinical specialists in human behavior.

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