36,417 research outputs found
Triaging Content Severity in Online Mental Health Forums
Mental health forums are online communities where people express their issues
and seek help from moderators and other users. In such forums, there are often
posts with severe content indicating that the user is in acute distress and
there is a risk of attempted self-harm. Moderators need to respond to these
severe posts in a timely manner to prevent potential self-harm. However, the
large volume of daily posted content makes it difficult for the moderators to
locate and respond to these critical posts. We present a framework for triaging
user content into four severity categories which are defined based on
indications of self-harm ideation. Our models are based on a feature-rich
classification framework which includes lexical, psycholinguistic, contextual
and topic modeling features. Our approaches improve the state of the art in
triaging the content severity in mental health forums by large margins (up to
17% improvement over the F-1 scores). Using the proposed model, we analyze the
mental state of users and we show that overall, long-term users of the forum
demonstrate a decreased severity of risk over time. Our analysis on the
interaction of the moderators with the users further indicates that without an
automatic way to identify critical content, it is indeed challenging for the
moderators to provide timely response to the users in need.Comment: Accepted for publication in Journal of the Association for
Information Science and Technology (2017
Lightme: Analysing Language in Internet Support Groups for Mental Health
Background: Assisting moderators to triage harmful posts in Internet Support
Groups is relevant to ensure its safe use. Automated text classification
methods analysing the language expressed in posts of online forums is a
promising solution. Methods: Natural Language Processing and Machine Learning
technologies were used to build a triage post classifier using a dataset from
Reachout mental health forum for young people. Results: When comparing with the
state-of-the-art, a solution mainly based on features from lexical resources,
received the best classification performance for the crisis posts (52%), which
is the most severe class. Six salient linguistic characteristics were found
when analysing the crisis post; 1) posts expressing hopelessness, 2) short
posts expressing concise negative emotional responses, 3) long posts expressing
variations of emotions, 4) posts expressing dissatisfaction with available
health services, 5) posts utilising storytelling, and 6) posts expressing users
seeking advice from peers during a crisis. Conclusion: It is possible to build
a competitive triage classifier using features derived only from the textual
content of the post. Further research needs to be done in order to translate
our quantitative and qualitative findings into features, as it may improve
overall performance
Análisis del lenguaje en grupos de apoyo en Internet de salud mental
Assisting moderators to triage critical posts in Internet Support Groups is relevant to ensure its safe use. Automated text classification methods analysing the language expressed in posts of online forums is a promising solution. Natural Language Processing and Machine Learning technologies were used to build a triage post classifier using a dataset from Reachout.com mental health forum. When comparing with the state-of-the-art, our solution achieved the best classification performance for the crisis posts (52%), which is the most severe class.Dar asistencia a los moderadores de Grupos de Ayuda en Internet es importante para asegurar su uso de forma segura. Métodos de clasificación de textos que analizan el lenguaje utilizado en estos forums es una de las posibles soluciones. Esta investigación trata de utilizar tecnologías del procesamiento del lenguaje natural y el aprendizaje automático para construir un sistema de clasificación de triaje usando datos del forum de salud mental Reachout.com. Al comparar con el estado de la cuestión, nuestra propuesta alcanza el mejor rendimiento para la clase crisis (52%), siendo ésta la de mayor importancia
Depression and Self-Harm Risk Assessment in Online Forums
Users suffering from mental health conditions often turn to online resources
for support, including specialized online support communities or general
communities such as Twitter and Reddit. In this work, we present a neural
framework for supporting and studying users in both types of communities. We
propose methods for identifying posts in support communities that may indicate
a risk of self-harm, and demonstrate that our approach outperforms strong
previously proposed methods for identifying such posts. Self-harm is closely
related to depression, which makes identifying depressed users on general
forums a crucial related task. We introduce a large-scale general forum dataset
("RSDD") consisting of users with self-reported depression diagnoses matched
with control users. We show how our method can be applied to effectively
identify depressed users from their use of language alone. We demonstrate that
our method outperforms strong baselines on this general forum dataset.Comment: Expanded version of EMNLP17 paper. Added sections 6.1, 6.2, 6.4,
FastText baseline, and CNN-
Improving mental health using machine learning to assist humans in the moderation of forum posts
This work investigates the potential for the application of machine learning and natural language processing technology in an online application designed to help teenagers talk about their mental health issues. Specifically, we investigate whether automatic classification methods can be applied with sufficient accuracy to assist humans in the moderation of posts and replies to an online forum. Using real data from an existing application, we outline the specific problems of lack of data, class imbalance and multiple rejection reasons. We investigate a number of machine learning architectures including a state-of-the-art transfer learning architecture, BERT, which has performed well elsewhere despite limited training data, due to its use of pre-training on a very large general corpus. Evaluating on real data, we demonstrate that further large performance gains can be made through the use of automatic data augmentation techniques (synonym replacement, synonym insertion, random swap and random deletion). Using a combination of data augmentation and transfer learning, performance of the automatic classification rivals human performance at the task, thus demonstrating the feasibility of deploying these techniques in a live system
Mental distress detection and triage in forum posts: the LT3 CLPsych 2016 shared task system
This paper describes the contribution of LT3 for the CLPsych 2016 Shared Task on automatic triage of mental health forum posts. Our systems use multiclass Support Vector Machines (SVM), cascaded binary SVMs and ensembles with a rich feature set. The best systems obtain macro-averaged F-scores of 40% on the full task and 80% on the green versus alarming distinction. Multiclass SVMs with all features score best in terms of F-score, whereas feature filtering with bi-normal separation and classifier ensembling are found to improve recall of alarming posts
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