Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media

Yazdavar, Amir Hossein; Al-Olimat, Hussein S.; Ebrahimi, Monireh; Bajaj, Goonmeet; Banerjee, Tanvi; Thirunarayan, Krishnaprasad; Pathak, Jyotishman; Sheth, Amit P.

doi:10.1145/3110025.3123028

Cited by 122 publications

(106 citation statements)

References 27 publications

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“…13 Lexicon-based approaches are known to have low recall particularly for social media data since social media expressions are often non-standard and contain misspellings. 14,15 Therefore, instead of searching the tweets for exact expressions from the tweets, we performed inexact matching using a string similarity metric. Specifically, for every symptom in the lexicon, we searched windows of sequences of characters in each tweet that had similarity values above a specific threshold.…”

Section: Symptom Discovery From User Postsmentioning

confidence: 99%

Self-reported COVID-19 symptoms on Twitter: An analysis and a research resource

Sarker

Lakamana

Hogg-Bremer

et al. 2020

Preprint

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ObjectiveTo mine Twitter to quantitatively analyze COVID-19 symptoms self-reported by users, compare symptom distributions against clinical studies, and create a symptom lexicon for the research community. Materials and methodsWe retrieved tweets using COVID-19-related keywords, and performed several layers of semi-automatic filtering to curate self-reports of positive-tested users. We extracted COVID-19-related symptoms mentioned by the users, mapped them to standard IDs, and compared the distributions with multiple studies conducted in clinical settings. ResultsWe identified 203 positive-tested users who reported 932 symptoms using 598 unique expressions. The most frequently-reported symptoms were fever/pyrexia (65%), cough (56%), body aches/pain (40%), headache (35%), fatigue (35%), and dyspnea (34%) amongst users who reported at least 1 symptom. Mild symptoms, such as anosmia (26%) and ageusia (24%) were frequently reported on Twitter, but not in clinical studies. ConclusionThe spectrum of COVID-19 symptoms identified from Twitter may complement those identified in clinical settings.

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Section: Symptom Discovery From User Postsmentioning

confidence: 99%

Self-reported COVID-19 symptoms on Twitter: An analysis and a research resource

Sarker

Lakamana

Hogg-Bremer

et al. 2020

Preprint

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“…Previous efforts highlight diverse modes of mental health self-disclosures on social media [12]. Self-disclosure clues have been extensively utilized for creating ground-truth data for numerous social media analytic studies such as predicting users' demographics [54], and depressive behavior [8]. For instance, vulnerable individuals may employ depressive-indicative terms in their Twitter profile descriptions.…”

Section: Datasetmentioning

confidence: 99%

“…Other individuals may share their age and gender, e.g., "16 year old suicidal girl". We employed a large dataset of 45,000 Twitter users with self-reported depressive symptoms introduced initially in [8]. All information was obtained using advanced search API [71].…”

Section: Datasetmentioning

confidence: 99%

“…Recent years have witnessed rapid growth in the analysis of social media for studying a wide range of health problems from detecting the influenza epidemic [5] and cardiac arrest [6] to studying mood and mental health conditions [7,8]. The widespread adoption of social media where people voluntarily and publicly express their thoughts, moods, emotions, and feelings, and share their daily struggles with mental health has not been adequately tapped into studying mental illnesses, such as depression.…”

Section: Introductionmentioning

confidence: 99%

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Multimodal mental health analysis in social media

et al. 2020

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Depression is a major public health concern in the U.S. and globally. While successful early identification and treatment can lead to many positive health and behavioral outcomes, depression, remains undiagnosed, untreated or undertreated due to several reasons, including denial of the illness as well as cultural and social stigma. With the ubiquity of social media platforms, millions of people are now sharing their online persona by expressing their thoughts, moods, emotions, and even their daily struggles with mental health on social media. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of depressive symptoms from tweets obtained, unobtrusively. Particularly, we examine and exploit multimodal big (social) data to discern depressive behaviors using a wide variety of features including individuallevel demographics. By developing a multimodal framework and employing statistical techniques to fuse heterogeneous sets of features obtained through the processing of visual, textual, and user interaction data, we significantly enhance the current state-of-the-art approaches for identifying depressed individuals on Twitter (improving the average F1-Score by 5 percent) as well as facilitate demographic inferences from social media. Besides providing insights into the relationship between demographics and mental health, our research assists in the design of a new breed of demographic-aware health interventions.

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“…Twitter data can be employed to shed light on many healthcare and diseaserelated aspects of contemporary interest, ranging from Alzheimer and dementia progression [74] to eating disorders [75] and mental health problems [76,77]. We focus on applications to glean depression in individuals or at a community level using self-reports about these conditions, their consequences, and patient experiences on Twitter.…”

Section: Healthcarementioning

confidence: 99%

Predictive Analysis on Twitter: Techniques and Applications

Kursuncu

Gaur

Lokala

et al. 2018

Lecture Notes in Social Networks

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Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories.

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Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media

Cited by 122 publications

References 27 publications

Self-reported COVID-19 symptoms on Twitter: An analysis and a research resource

Self-reported COVID-19 symptoms on Twitter: An analysis and a research resource

Multimodal mental health analysis in social media

Predictive Analysis on Twitter: Techniques and Applications

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