Mental health issues widely vary across individuals - the manifestations of
signs and symptoms can be fairly heterogeneous. Recently, language-based
depression and anxiety assessments have shown promise for capturing this
heterogeneous nature by evaluating a patient's own language, but such
approaches require a large sample of words per person to be accurate. In this
work, we introduce adaptive language-based assessment - the task of iteratively
estimating an individual's psychological score based on limited language
responses to questions that the model also decides to ask. To this end, we
explore two statistical learning-based approaches for measurement/scoring:
classical test theory (CTT) and item response theory (IRT). We find that using
adaptive testing in general can significantly reduce the number of questions
required to achieve high validity (r ~ 0.7) with standardized tests, bringing
down from 11 total questions down to 3 for depression and 5 for anxiety. Given
the combinatorial nature of the problem, we empirically evaluate multiple
strategies for both the ordering and scoring objectives, introducing two new
methods: a semi-supervised item response theory based method (ALIRT), and a
supervised actor-critic based model. While both of the models achieve
significant improvements over random and fixed orderings, we find ALIRT to be a
scalable model that achieves the highest accuracy with lower numbers of
questions (e.g. achieves Pearson r ~ 0.93 after only 3 questions versus asking
all 11 questions). Overall, ALIRT allows prompting a reduced number of
questions without compromising accuracy or overhead computational costs.

通过有限的语言回答问题进行迭代估计个体心理得分的自适应语言评估方法（ALIRT）可显著降低问题数量，同时不影响准确性和计算成本。

基于自适应语言的心理健康评估和项目反应理论

Adaptive Language-based Mental Health Assessment with Item-Response  Theory

Compared to physical health, population mental health measurement in the U.S.
is very coarse-grained. Currently, in the largest population surveys, such as
those carried out by the Centers for Disease Control or Gallup, mental health
is only broadly captured through "mentally unhealthy days" or "sadness", and
limited to relatively infrequent state or metropolitan estimates. Through the
large scale analysis of social media data, robust estimation of population
mental health is feasible at much higher resolutions, up to weekly estimates
for counties. In the present work, we validate a pipeline that uses a sample of
1.2 billion Tweets from 2 million geo-located users to estimate mental health
changes for the two leading mental health conditions, depression and anxiety.
We find moderate to large associations between the language-based mental health
assessments and survey scores from Gallup for multiple levels of granularity,
down to the county-week (fixed effects $\beta = .25$ to $1.58$; $p<.001$).
Language-based assessment allows for the cost-effective and scalable monitoring
of population mental health at weekly time scales. Such spatially fine-grained
time series are well suited to monitor effects of societal events and policies
as well as enable quasi-experimental study designs in population health and
other disciplines. Beyond mental health in the U.S., this method generalizes to
a broad set of psychological outcomes and allows for community measurement in
under-resourced settings where no traditional survey measures - but social
media data - are available.

本研究旨在通过对社交媒体数据进行大规模分析，使用 1.2 亿条推文来估计美国各个县的抑郁症和焦虑症情况，从而实现以更高的分辨率监测人口心理健康状况的可行性，在精确度方面要优于 ad-hoc 调查。