Table Of ContentStudies in Computational Intelligence 1018
Fabio Crestani
David E. Losada
Javier Parapar Editors
Early Detection
of Mental
Health Disorders
by Social Media
Monitoring
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Studies in Computational Intelligence
Volume 1018
Series Editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
The series “Studies in Computational Intelligence” (SCI) publishes new develop-
ments and advances in the various areas of computational intelligence—quickly and
with a high quality. The intent is to cover the theory, applications, and design methods
of computational intelligence, as embedded in the fields of engineering, computer
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series contains monographs, lecture notes and edited volumes in computational
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algorithms, evolutionary computation, artificial intelligence, cellular automata, self-
organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems.
Of particular value to both the contributors and the readership are the short publica-
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All books published in the series are submitted for consideration in Web of Science.
· ·
Fabio Crestani David E. Losada Javier Parapar
Editors
Early Detection of Mental
Health Disorders by Social
Media Monitoring
The First Five Years of the eRisk Project
Editors
Fabio Crestani David E. Losada
Faculty of Informatics Centro Singular de Investigación en
Università della Svizzera Italiana (USI) Tecnoloxías Intelixentes (CiTIUS)
Lugano, Switzerland Universidade de Santiago de Compostela
Santiago de Compostela, Spain
Javier Parapar
Centro de Investigación en Tecnoloxías da
Información e as Comunicacións (CITIC)
Universidade da Coruña
A Coruña, Spain
ISSN 1860-949X ISSN 1860-9503 (electronic)
Studies in Computational Intelligence
ISBN 978-3-031-04430-4 ISBN 978-3-031-04431-1 (eBook)
https://doi.org/10.1007/978-3-031-04431-1
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Foreword
Early in 2013, my wife Rebecca and I were out on a “date night” having food and wine
at a restaurant named—pragmatically, but uninspiringly—Food And Wine. We found
ourselves discussing some work being done recently by my tribe, computational
linguists, that might potentially be interesting to her tribe, clinical psychologists. By
the time dessert arrived we had hatched the idea of a workshop that would bring
the two tribes together. Not too long afterward I pitched the idea to Meg Mitchell,
who had been involved in a number of interesting studies on language analysis in
connection with mental health conditions like autism and cognitive impairment with
Brian Roark, Emily Prud’hommeaux, and others.1 The three of us led the organization
of the first Workshop on Computational Linguistics and Clinical Psychology, at the
2014 ACL conference. CLPsych, as it is known, has taken place every year since
then, except for what I sincerely hope will be just a single break during a global
pandemic.2
We organized CLPsych because we could see that there were an increasing
number of people interested in bringing the tools of natural language processing
and machine learning to the problems of mental health and well-being, but they were
1 See for example:
(cid:129) Emily T. Prud’hommeaux, Margaret Mitchell, and Brian Roark. 2011. Using patterns of narra-
tive recall for improved detection of mild cognitive impairment. International Conference on
Technology and Aging (ICTA) 2011, Toronto, Ontario.
(cid:129) B. Roark, M. Mitchell, J.-P. Hosom, K. Hollingshead and J. Kaye, “Spoken Language Derived
Measures for Detecting Mild Cognitive Impairment,” in IEEE Transactions on Audio, Speech,
and Language Processing, vol. 19, no. 7, pp. 2081-2090, Sept. 2011.
(cid:129) Van Santen, J. P., Prud’Hommeaux, E. T., Black, L. M., & Mitchell, M. (2010). Computational
prosodic markers for autism. Autism, 14(3), 215-236.
(cid:129) Roark, B., Mitchell, M., & Hollingshead, K. (2007, June). Syntactic complexity measures for
detecting mild cognitive impairment. In Proceedings of the ACL 2007 Workshop on Biomedical
Natural Language Processing (BioNLP), Prague, pp. 1-8, June 2007.
2 Proceedings can be found at CLPsych.org.
v
vi Foreword
scattered, presenting their work in a wide range of different journals, conferences, and
workshops. There was critical mass, and it was time to start building a community.
The book you’re holding (or, these days, viewing, or perhaps listening to), emerges
from a similar recognition—a recognition of real-world needs, of a range of capable
people with the right skills to tackle those needs, and of an opportunity to bring
that body of intelligence and enthusiasm and energy together. The organizers of
eRisk identified a common thread running through a range of concerns for human
well-being: the value of identifying risks and threats sooner rather than later. This has
given rise to the concept of early risk detection systems as a coherent, interdisciplinary
research area.
The application areas for early risk detection are numerous. These include risks
directly associated with mental health, such as major depression and suicidality,
but they also go further, to include risks from sexual predators, for example, or
cyberbullying, or political radicalization, or addiction.
What makes all this possible is the ever-increasing availability of online evidence
about people’s thoughts and behaviors—what Lazer and colleagues, introducing
the field of computational social science, referred to as “digital traces that can be
compiled into comprehensive pictures”.3
Similarly, Coppersmith and his colleagues talk about the “clinical whitespace” in
the context of mental health; this describes the long periods of time that intervene
in between attention from clinical providers, periods that used to be entirely opaque
to providers of care, but which now hold the potential for yielding valuable or even
crucial signal about people’s mental state and, as such, new opportunities for early
intervention.4
This notion of time is really central, and one of eRisk’s most distinguishing and
innovative features. It’s very easy for us, as technologists, to define “tasks” and
their associated evaluation metrics in whatever way we find intuitively plausible or
easy to execute. But tasks and metrics, if they are to be relevant, are not stand-
alone things. Rather, progress needs to be made using faithful abstractions of the
real-world problems the technology is eventually intended to help address, and that
means thinking about real-world use cases. One obvious example of this is in speech
recognition, where the tasks worked on by the community have progressed, over the
decades, from isolated words, to speech read by broadcasters, to conversations, to
meetings, all with an increased awareness of the need for tolerance in the face of
noise and variation among speakers. Another is information retrieval, where standard
thinking about evaluation in terms of precision and recall has now been updated with
more relevant metrics in the context of how people actually search for information
on a day-to-day basis. What sense does it make to evaluate systems as if we care
about every single relevant document they return, for example, in a world where
3 Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A. L., Brewer, D., ... & Van Alstyne, M.
(2009). Social science. Computational social science. Science (New York, NY), 323(5915), 721–
723.
4 Coppersmith, G., Leary, R., Crutchley, P., and Fine, A. (2018). Natural language processing of
social media as screening for suicide risk. Biomedical Informatics Insights, 10, 117822261879286.
Foreword vii
most searches are done on the web and almost nobody looks past the first ten hits?
Measuring precision-at-10, the number of relevant hits among the first ten returned,
is a better abstraction for assessing the extent to which a system will satisfy that
real-world need.
Correspondingly, in settings where proactive intervention is the ultimate real-
world goal, data don’t arrive all at once. Much of time, datapoints arrive in a stream
over time. The question, therefore, is not just how to do good classification, it’s
about when a system should raise a flag saying that an individual or a situation
merits attention. The eRisk tasks are, therefore, designed from the ground up with
chronological considerations in mind, from an incremental data release strategy to
evaluation metrics that consider not only the accuracy but also the speed with which
choices are made.
As another key thing to consider, better technology is not a goal in and of itself.
Even if a technology genuinely performs well in formal evaluations, that doesn’t
necessarily mean that it can be deployed in a way that actually represents progress.
Most technologists are familiar with the distinction between intrinsic and extrinsic
evaluations, the latter category meaning that what is measured is the performance of
the entire system in which the to-be-evaluated technology is embedded. For example,
you can evaluate a natural language parser intrinsically, by looking at the parse trees
it produces, or you can consider it extrinsically in the context of a machine translation
system, looking at how different parsing approaches improve, or don’t improve, the
overall quality of the translations being produced.5
When it comes to risk, systems like the ones described in this volume are being
evaluated intrinsically in the eRisk tasks, but it’s important to remember that ulti-
mately what matters most is how they will perform extrinsically—embedded not
just in some other automatic system, like machine translation, but in an entire
ecosystem that involves not just technologies but also human beings providing care
or assistance.6 This gives rise to some challenges for the typical ways technologists
gather data for technological development and evaluation; for example, Ernala and
colleagues have pointed out some of the downsides of using online self-reports and
other proxies as a stand-in for clinically valid diagnoses.7 It’s notable and important,
therefore, that eRisk’s criteria go further and require explicit mention of a diagnosis
of a condition like depression, not just a statement about having depression; this is
a credible way of balancing the goal of collecting data at reasonable scale against
5 Resnik, P., and Lin, J. (2013). Evaluation of NLP systems. In Clark, A., Fox, C., & Lappin,
S. (Eds.) (2010). The Handbook of Computational Linguistics and Natural Language Processing.
Wiley-Blackwell.
6 Resnik, P., Foreman, A., Kuchuk, M., Musacchio Schafer, K., and Pinkham, B. (2021). Naturally
occurring language as a source of evidence in suicide prevention. Suicide and Life Threatening
Behavior, 51(1), 88–96.
7 Ernala, S. K., Birnbaum, M. L., Candan, K. A., Rizvi, A. F., Sterling, W. A., Kane, J. M., and
De Choudhury, M. (2019, April). Methodological gaps in predicting mental health states from
social media: Triangulating diagnostic signals. In Proceedings of the 2019 ACM CHI conference
on human factors in computing systems (p. 134).
viii Foreword
the goal of defining ground-truth categories that will actually matter to clinicians or
other relevant subject matter experts.
The chapters in this volume do a great job providing a comprehensive under-
standing of these and other considerations. The technology-oriented reader will find
clear rationales for the choices being made, detailed discussions of the best work so
far in eRisk tasks, and thorough contemplation of findings.
Even more important, though, this volume is a milestone setting the stage for the
next generation of eRisk. In looking at the future of eRisk, the discussion of expanding
the range of problems is exciting, particularly the idea of selecting new challenges
to focus on based on broad social relevance—for example, anxiety disorders are
trending upward as a result of COVID-19 pandemic, and digital evidence may be
one of the most important ways to help clinical professionals handle the mental health
tsunami following in its wake.8 Similarly, the ability to improve early detection of
dementia may have a dramatic impact on well-being for large swaths of the world’s
aging population.
Equally exciting is eRisk’s more recent and continuing push in the direction
of prediction not just for a single class (at-risk or not), but for symptom-level
responses of the kind reported in clinical questionnaires. Particularly for mental
health providers, the yes-or-no question of a diagnosis matters, but it’s far from
the end of the story: finer-grained information is needed in order to really under-
stand what’s going on with patients, and in fact there are efforts underway in the
mental health community to improve the organization, description, and measure-
ment of psychopathology via data-driven, empirically based organization of signs
and symptoms.9 The move toward obtaining ground-truth data from validated ques-
tionnaires, and defining the goals of prediction tasks accordingly, is a move toward
bridging more effectively between the things technologies can provide and the things
clinicians need.
In the end, whether we are talking about mental health risks in particular, or other
risks more generally, that kind of bridging—creating momentum and community
among technologists, combined with relevance for the non-technologists—is the way
real progress is going to be made. The work in this book is an important contribution
to that enterprise.
October 2021 Philip Resnik
Department of Linguistics and Institute
for Advanced Computer Studies
University of Maryland
College Park, Maryland, USA
8 Inkster, B. and Digital Mental Health Data Insights Group (DMHDIG) (2021). Early warning
signs of a mental health tsunami: A coordinated response to gather initial data insights from multiple
digital services providers. Frontiers in Digital Health, 2, 64.
9 Kotov, R., Krueger, R. F., W atson, D., Achenbach, T. M., Althoff, R. R ., Bagby, R. M., ... &
Zimmerman, M. (2017). The Hierarchical Taxonomy of Psychopathology (HiTOP): A dimensional
alternative to traditional nosologies. Journal of Abnormal Psychology, 126(4), 454-477.
Preface
This book presents the best of the first five years of eRisk. eRisk10 , which is a short-
ening of Early Risk Prediction on the Internet, is concerned with the exploration of
techniques for the early detection of certain health problems which manifest in the
way people write and communicate on the internet, in particular in user generated
content. eRisk is also concerned with the evaluation methodology for such tech-
niques, in particular with effectiveness metrics where early detection is an important
issue. The eRisk project was proposed as part of the Conference and Lab of the
Evaluation Forum (CLEF) in 2016 and started as a lab in 2017. Thus, 2020 was the
fifth year of eRisk.
Proud of what we achieved after five years and excited for what the future will
bring to this endeavor, we decided to compile this book to present the best of what
was achieved in these first five years of this interesting and challenging task. We hope
this book will be of interest not just to the many past and future participants of the
lab but also to other researchers and practitioners involved in similarly challenging
endeavors.
Lugano, Switzerland Fabio Crestani
Santiago de Compostela, Spain David E. Losada
A Coruña, Spain Javier Parapar
October 2021
10 See: https://erisk.irlab.org/.
ix
