31,631 research outputs found
Machine Learning-based Approach for Depression Detection in Twitter Using Content and Activity Features
Social media channels, such as Facebook, Twitter, and Instagram, have altered
our world forever. People are now increasingly connected than ever and reveal a
sort of digital persona. Although social media certainly has several remarkable
features, the demerits are undeniable as well. Recent studies have indicated a
correlation between high usage of social media sites and increased depression.
The present study aims to exploit machine learning techniques for detecting a
probable depressed Twitter user based on both, his/her network behavior and
tweets. For this purpose, we trained and tested classifiers to distinguish
whether a user is depressed or not using features extracted from his/ her
activities in the network and tweets. The results showed that the more features
are used, the higher are the accuracy and F-measure scores in detecting
depressed users. This method is a data-driven, predictive approach for early
detection of depression or other mental illnesses. This study's main
contribution is the exploration part of the features and its impact on
detecting the depression level.Comment: 16 pages, 7 figures, journal articl
Anxious Depression Prediction in Real-time Social Data
Mental well-being and social media have been closely related domains of
study. In this research a novel model, AD prediction model, for anxious
depression prediction in real-time tweets is proposed. This mixed
anxiety-depressive disorder is a predominantly associated with erratic thought
process, restlessness and sleeplessness. Based on the linguistic cues and user
posting patterns, the feature set is defined using a 5-tuple vector <word,
timing, frequency, sentiment, contrast>. An anxiety-related lexicon is built to
detect the presence of anxiety indicators. Time and frequency of tweet is
analyzed for irregularities and opinion polarity analytics is done to find
inconsistencies in posting behaviour. The model is trained using three
classifiers (multinomial na\"ive bayes, gradient boosting, and random forest)
and majority voting using an ensemble voting classifier is done. Preliminary
results are evaluated for tweets of sampled 100 users and the proposed model
achieves a classification accuracy of 85.09%
Depression Severity Estimation from Multiple Modalities
Depression is a major debilitating disorder which can affect people from all
ages. With a continuous increase in the number of annual cases of depression,
there is a need to develop automatic techniques for the detection of the
presence and extent of depression. In this AVEC challenge we explore different
modalities (speech, language and visual features extracted from face) to design
and develop automatic methods for the detection of depression. In psychology
literature, the PHQ-8 questionnaire is well established as a tool for measuring
the severity of depression. In this paper we aim to automatically predict the
PHQ-8 scores from features extracted from the different modalities. We show
that visual features extracted from facial landmarks obtain the best
performance in terms of estimating the PHQ-8 results with a mean absolute error
(MAE) of 4.66 on the development set. Behavioral characteristics from speech
provide an MAE of 4.73. Language features yield a slightly higher MAE of 5.17.
When switching to the test set, our Turn Features derived from audio
transcriptions achieve the best performance, scoring an MAE of 4.11
(corresponding to an RMSE of 4.94), which makes our system the winner of the
AVEC 2017 depression sub-challenge.Comment: 8 pages, 1 figur
Knowledge Transferring via Model Aggregation for Online Social Care
The Internet and the Web are being increasingly used in proactive social care
to provide people, especially the vulnerable, with a better life and services,
and their derived social services generate enormous data. However, the strict
protection of privacy makes user's data become an isolated island and limits
the predictive performance of standalone clients. To enable effective proactive
social care and knowledge sharing within intelligent agents, this paper
develops a knowledge transferring framework via model aggregation. Under this
framework, distributed clients perform on-device training, and a third-party
server integrates multiple clients' models and redistributes to clients for
knowledge transferring among users. To improve the generalizability of the
knowledge sharing, we further propose a novel model aggregation algorithm,
namely the average difference descent aggregation (AvgDiffAgg for short). In
particular, to evaluate the effectiveness of the learning algorithm, we use a
case study on the early detection and prevention of suicidal ideation, and the
experiment results on four datasets derived from social communities demonstrate
the effectiveness of the proposed learning method
Using Social Media to Predict the Future: A Systematic Literature Review
Social media (SM) data provides a vast record of humanity's everyday
thoughts, feelings, and actions at a resolution previously unimaginable.
Because user behavior on SM is a reflection of events in the real world,
researchers have realized they can use SM in order to forecast, making
predictions about the future. The advantage of SM data is its relative ease of
acquisition, large quantity, and ability to capture socially relevant
information, which may be difficult to gather from other data sources.
Promising results exist across a wide variety of domains, but one will find
little consensus regarding best practices in either methodology or evaluation.
In this systematic review, we examine relevant literature over the past decade,
tabulate mixed results across a number of scientific disciplines, and identify
common pitfalls and best practices. We find that SM forecasting is limited by
data biases, noisy data, lack of generalizable results, a lack of
domain-specific theory, and underlying complexity in many prediction tasks. But
despite these shortcomings, recurring findings and promising results continue
to galvanize researchers and demand continued investigation. Based on the
existing literature, we identify research practices which lead to success,
citing specific examples in each case and making recommendations for best
practices. These recommendations will help researchers take advantage of the
exciting possibilities offered by SM platforms
Cognitive computation of brain disorders based primarily on ocular responses
The present review presents multiple techniques in which ocular assessments
may serve as a noninvasive approach for the early diagnoses of various
cognitive and psychiatric disorders, such as Alzheimer's disease (AD), autism
spectrum disorder (ASD), schizophrenia (SZ), and major depressive disorder
(MDD). Real-time ocular responses are tightly associated with emotional and
cognitive processing within the central nervous system. Patterns seen in
saccades, pupillary responses, and blinking, as well as retinal
microvasculature and morphology visualized via office-based ophthalmic imaging,
are potential biomarkers for the screening and evaluation of cognitive and
psychiatric disorders. Additionally, rapid advances in artificial intelligence
(AI) present a growing opportunity to use machine-learning-based AI, especially
deep-learning neural networks, to shed new light on the field of cognitive
neuroscience, which may lead to novel evaluations and interventions via ocular
approaches for cognitive and psychiatric disorders
Machine Learning pipeline for discovering neuroimaging-based biomarkers in neurology and psychiatry
We consider a problem of diagnostic pattern recognition/classification from
neuroimaging data. We propose a common data analysis pipeline for
neuroimaging-based diagnostic classification problems using various ML
algorithms and processing toolboxes for brain imaging. We illustrate the
pipeline application by discovering new biomarkers for diagnostics of epilepsy
and depression based on clinical and MRI/fMRI data for patients and healthy
volunteers.Comment: 20 pages, 2 figure
Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications
Facial expressions are an important way through which humans interact
socially. Building a system capable of automatically recognizing facial
expressions from images and video has been an intense field of study in recent
years. Interpreting such expressions remains challenging and much research is
needed about the way they relate to human affect. This paper presents a general
overview of automatic RGB, 3D, thermal and multimodal facial expression
analysis. We define a new taxonomy for the field, encompassing all steps from
face detection to facial expression recognition, and describe and classify the
state of the art methods accordingly. We also present the important datasets
and the bench-marking of most influential methods. We conclude with a general
discussion about trends, important questions and future lines of research
Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media
Millions of users share their experiences on social media sites, such as
Twitter, which in turn generate valuable data for public health monitoring,
digital epidemiology, and other analyses of population health at global scale.
The first, critical, task for these applications is classifying whether a
personal health event was mentioned, which we call the (PHM) problem. This task
is challenging for many reasons, including typically short length of social
media posts, inventive spelling and lexicons, and figurative language,
including hyperbole using diseases like "heart attack" or "cancer" for
emphasis, and not as a health self-report. This problem is even more
challenging for rarely reported, or frequent but ambiguously expressed
conditions, such as "stroke". To address this problem, we propose a general,
robust method for detecting PHMs in social media, which we call WESPAD, that
combines lexical, syntactic, word embedding-based, and context-based features.
WESPAD is able to generalize from few examples by automatically distorting the
word embedding space to most effectively detect the true health mentions.
Unlike previously proposed state-of-the-art supervised and deep-learning
techniques, WESPAD requires relatively little training data, which makes it
possible to adapt, with minimal effort, to each new disease and condition. We
evaluate WESPAD on both an established publicly available Flu detection
benchmark, and on a new dataset that we have constructed with mentions of
multiple health conditions. Our experiments show that WESPAD outperforms the
baselines and state-of-the-art methods, especially in cases when the number and
proportion of true health mentions in the training data is small.Comment: WWW 201
Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequences
Depression is ranked as the largest contributor to global disability and is
also a major reason for suicide. Still, many individuals suffering from forms
of depression are not treated for various reasons. Previous studies have shown
that depression also has an effect on language usage and that many depressed
individuals use social media platforms or the internet in general to get
information or discuss their problems. This paper addresses the early detection
of depression using machine learning models based on messages on a social
platform. In particular, a convolutional neural network based on different word
embeddings is evaluated and compared to a classification based on user-level
linguistic metadata. An ensemble of both approaches is shown to achieve
state-of-the-art results in a current early detection task. Furthermore, the
currently popular ERDE score as metric for early detection systems is examined
in detail and its drawbacks in the context of shared tasks are illustrated. A
slightly modified metric is proposed and compared to the original score.
Finally, a new word embedding was trained on a large corpus of the same domain
as the described task and is evaluated as well.Comment: This work has been submitted to the IEEE and has been accepted for
future publication in IEEE Transactions on Knowledge and Data Engineering.
Copyright may be transferred without notice, after which this version may no
longer be accessible. 14 pages, 3 figures, 7 table
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