A body of literature has demonstrated that users' mental health conditions,
such as depression and anxiety, can be predicted from their social media
language. There is still a gap in the scientific understanding of how
psychological stress is expressed on social media. Stress is one of the primary
underlying causes and correlates of chronic physical illnesses and mental
health conditions. In this paper, we explore the language of psychological
stress with a dataset of 601 social media users, who answered the Perceived
Stress Scale questionnaire and also consented to share their Facebook and
Twitter data. Firstly, we find that stressed users post about exhaustion,
losing control, increased self-focus and physical pain as compared to posts
about breakfast, family-time, and travel by users who are not stressed.
Secondly, we find that Facebook language is more predictive of stress than
Twitter language. Thirdly, we demonstrate how the language based models thus
developed can be adapted and be scaled to measure county-level trends. Since
county-level language is easily available on Twitter using the Streaming API,
we explore multiple domain adaptation algorithms to adapt user-level Facebook
models to Twitter language. We find that domain-adapted and scaled social
media-based measurements of stress outperform sociodemographic variables (age,
gender, race, education, and income), against ground-truth survey-based stress
measurements, both at the user- and the county-level in the U.S. Twitter
language that scores higher in stress is also predictive of poorer health, less
access to facilities and lower socioeconomic status in counties. We conclude
with a discussion of the implications of using social media as a new tool for
monitoring stress levels of both individuals and counties.

本文研究了社交媒体上的心理压力语言，发现社交媒体的语言模型可用于测量用户和地区的压力，具有较高的预测能力和实际效果。

利用社交媒体理解和衡量心理压力

Understanding and Measuring Psychological Stress using Social Media

We propose a Long Short-Term Memory (LSTM) with attention mechanism to
classify psychological stress from self-conducted interview transcriptions. We
apply distant supervision by automatically labeling tweets based on their
hashtag content, which complements and expands the size of our corpus. This
additional data is used to initialize the model parameters, and which it is
fine-tuned using the interview data. This improves the model's robustness,
especially by expanding the vocabulary size. The bidirectional LSTM model with
attention is found to be the best model in terms of accuracy (74.1%) and
f-score (74.3%). Furthermore, we show that distant supervision fine-tuning
enhances the model's performance by 1.6% accuracy and 2.1% f-score. The
attention mechanism helps the model to select informative words.

本研究提出了基于 LSTM 和注意力机制，通过对自我主观评估访谈记录进行分析来评估心理压力。我们利用自动生成标签的方式来远程监督训练集以扩大我们的语料库。 结果表明，双向 LSTM 模型是最佳模型，精度为 74.1％，F 分数为 74.3％。距离监督微调提高了 1.6％的精度和 2.1％的 F 分数。注意力机制帮助模型选择信息化的单词。