 Online Social Networks have become an important medium for communication among people who suffer from mental disorders to share moments of hardship and to seek support. Here we analyze how Reddit discussions can help improve the health conditions of its users. Using  emotional tone of user publications as a proxy for his emotional state, we uncover relationships between state changes and interactions he has in a given community. We observe that authors of negative posts often write more positive comments after engaging in discussions  . Second, we build models based on state-of-the-art embedding techniques and RNNs to predict shifts in emotional tone. We show that it is possible to predict with good accuracy the reaction of users of mental disorder online communities to the interactions experienced in these platforms  . Our models could assist in interventions promoted by health care professionals to provide support to people suffering from mental health illnesses.
In recent years, Online Social Networks have become an important medium for people who suffer from mental disorders to share moments of hardship , and receive emotional and informational support. In this work, we analyze how discussions in Reddit communities related to mental disorders can help improve the health conditions of their users. Using the emotional tone of users' writing as a proxy for emotional state, we uncover relationships between user interactions and state changes. First, we observe that authors of negative posts often write rosier comments after engaging in discussions , indicating that users' emotional state can improve due to social support . Second, we build models based on SOTA text embedding techniques and RNNs to predict shifts in emotional tone. This differs from most of related work, which focuses primarily on detecting mental disorders from user activity. We demonstrate the feasibility of accurately predicting the users' reactions to the interactions experienced in these platforms , and present some examples which illustrate that the models are correctly capturing the effects of comments on the author's emotional tone . Our models hold promising implications for interventions to provide support for people struggling with mental illnesses.
A
In recent years,
D
communication among
R
and to seek support. Here
, and receive emotional and informational support. In this work,
R
Reddit discussions
discussions in Reddit communities related to mental disorders
R
its
their
A
the
R
user publications
users' writing
D
his
R
state changes and interactions he has in a given community. We
user interactions and state changes. First, we
R
more positive
rosier
A
, indicating that users' emotional state can improve due to social support
R
state-of-the-art
SOTA text
R
We show that it is possible to predict with good accuracy the reaction of users of mental disorder online communities
This differs from most of related work, which focuses primarily on detecting mental disorders from user activity. We demonstrate the feasibility of accurately predicting the users' reactions
A
, and present some examples which illustrate that the models are correctly capturing the effects of comments on the author's emotional tone
R
could assist in interventions promoted by health care professionals
hold promising implications for interventions
R
to people suffering from mental health
for people struggling with mental