Instagram photos reveal predictive markers of depression

@article{Reece2017InstagramPR,
  title={Instagram photos reveal predictive markers of depression},
  author={Andrew G. Reece and Christopher M. Danforth},
  journal={EPJ Data Science},
  year={2017},
  volume={6},
  pages={1-12}
}
Using Instagram data from 166 individuals, we applied machine learning tools to successfully identify markers of depression. Statistical features were computationally extracted from 43,950 participant Instagram photos, using color analysis, metadata components, and algorithmic face detection. Resulting models outperformed general practitioners’ average unassisted diagnostic success rate for depression. These results held even when the analysis was restricted to posts made before depressed… 
View on Springer
doi.org
326 Citations
Highly Influential Citations
26
Background Citations
132
Methods Citations
52
Results Citations
9

326 Citations

Predicting Depression with Social Media Images

The different machine learning algorithms were trained on the objects and color features to retrieve object features that determine the presence of 80 different object classes on users’ images and achieved up to 65.5% F1-score for the task of revealing depressed users.

Predicting Depression with Text, Image, and Profile Data from Social Media

This study processed over 60,000 images, 95,000 posts, and 9,000 subscription items related to 619 user profiles on the VKontakte social media network and retrieved 6 different text based feature sets, images, and general profile data.

Identifying signals associated with psychiatric illness utilizing language and images posted to Facebook

Machine-learning algorithms are capable of differentiating SSD and MD using Facebook activity alone over a year in advance of hospitalization, and Integrating Facebook data with clinical information could one day serve to inform clinical decision-making.

Utilizing Instagram Data to Identify Usage Patterns Associated With Schizophrenia Spectrum Disorders

Differences in uploaded images and user activity on Instagram were identified in individuals with schizophrenia spectrum disorders, highlighting potential digital biomarkers of SSD from Instagram data.

Multimodal depression detection on instagram considering time interval of posts

A depression dictionary for automatically collecting data of depressive and non-depressive users is created and a multimodal system, which utilizes image, text and behavior features to predict the aggregated depression score of each post on Instagram is constructed.

Erratum to: Instagram photos reveal predictive markers of depression

It was noticed that Figure  contained an error and the horizontal bars for the ‘likes’ row were incorrectly shown as blue, and this error has now been acknowledged and corrected in this erratum.

Facial Landmark Activity Features for Depression Screening

This study found several facial landmark activity features with significant differences between healthy volunteers and depressed patients and successfully built machine learning models for automatic depression severity prediction.

Predicting Depression Tendency based on Image, Text and Behavior Data from Instagram

The proposed model recognizes users who have depression tendency with an F-1 score of 82.3% and is currently developing a tool for screening and detecting depression in an early stage.

Screening for Depressed Individuals by Using Multimodal Social Media Data

This work shows that the depression identification task through social media is better modeled as a Multiple Instance Learning (MIL) problem that can exploit the temporal dependencies between posts.

Automatic detection of depression symptoms in twitter using multimodal analysis

An automated approach to collect and evaluate tweets based on self-reported statements and present a novel multimodal framework to predict depression symptoms from user profiles to help identify user’s psychological states is provided.
...

References

SHOWING 1-10 OF 66 REFERENCES

Depression-related Imagery on Instagram

This study examined depression-related images on Instagram along with their accompanying captions to better understand the role of photo sharing in the lives of people who suffer from depression or who frame their experience as such and how social computing systems can be designed to support such interactions.

Predicting Depression via Social Media

It is found that social media contains useful signals for characterizing the onset of depression in individuals, as measured through decrease in social activity, raised negative affect, highly clustered egonetworks, heightened relational and medicinal concerns, and greater expression of religious involvement.

Forecasting the onset and course of mental illness with Twitter data

We developed computational models to predict the emergence of depression and Post-Traumatic Stress Disorder in Twitter users. Twitter data and details of depression history were collected from 204…

Characterizing and predicting postpartum depression from shared facebook data

It is found that increased social isolation and lowered availability of social capital on Facebook, are the best predictors of PPD in mothers.

Predicting postpartum changes in emotion and behavior via social media

The opportunity to use social media to identify mothers at risk of postpartum depression, an underreported health concern among large populations, and to inform the design of low-cost, privacy-sensitive early-warning systems and intervention programs aimed at promoting wellness post partum is motivated by the opportunity.

Measuring Post Traumatic Stress Disorder in Twitter

PTSD is considered, a serious condition that affects millions worldwide, with especially high rates in military veterans, and its utility is demonstrated by examining differences in language use between PTSD and random individuals, building classifiers to separate these two groups and by detecting elevated rates of PTSD at and around U.S. military bases using classifiers.

Instagram #Instasad?: Exploring Associations Among Instagram Use, Depressive Symptoms, Negative Social Comparison, and Strangers Followed

A theoretically grounded moderated meditation model of the association between Instagram use and depressive symptoms through the mechanism of negative social comparison, and moderation by amount of strangers one follows suggests that more frequent Instagram use has negative associations for people who follow more strangers, but positive associations forPeople who follow fewer strangers, with social comparison and depressive Symptoms.

Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media

This paper develops a statistical methodology to infer which individuals could undergo transitions from mental health discourse to suicidal ideation, and utilizes semi-anonymous support communities on Reddit as unobtrusive data sources to infer the likelihood of these shifts.

Feeling bad on Facebook: depression disclosures by college students on a social networking site

These findings suggest that those who receive online reinforcement from their friends are more likely to discuss their depressive symptoms publicly on Facebook, and social networking sites could be an innovative avenue for combating stigma surrounding mental health conditions or for identifying students at risk for depression.

Color sensitivity and mood disorders: biology or metaphor?

...