EVENT REPORT

Report on the 8th International Workshop on
Mining Actionable Insights from SocialNetworks (MAISoN’22) - Special Edition on
Mental Health and Social Mediaat TheWebConf 2022
Ebrahim Bagheri

Diana Inkpen

Christopher C. Yang

Toronto Metropolitan University
CanadaUniversity of Ottawa
CanadaDrexel University
Pennsylvania

bagheri@ryerson.ca

diana.inkpen@uottawa.ca

chris.yang@drexel.edu

Fattane Zarrinkalam
University of Guelph
Canadafzarrink@uoguelph.ca

Abstract
The eighth edition of the workshop on Mining Actionable Insights from Social Networks(MAISoN 2022) took place virtually on April 26th, 2022, co-located with the ACM Web
Conference 2022 (TheWebConf 2022). This year, we organized a special edition with focus
on mental health and social media. The aim of this edition was to bring together researchers
from different disciplines to discuss research that goes beyond descriptive analysis of social
media data and instead investigate different techniques that use social media data for building diagnostic, predictive and prescriptive analysis models for mental health applications.
This topic attracted a lot of interest from the community especially because of all the considerations surrounding the impact of social media during the COVID-19 pandemic which
has impacted on people’s mental health issues.
Date: 26 April 2022.
Website: https://2022.maisonworkshop.org/.

1

Introduction

With the emergence and growing popularity of social media such as blogging systems, wikis, social
bookmarking, social networks and microblogging services, many users are extensively engaged in
at least some of these applications to express their feelings and views about a wide variety of social
topics as they happen in real time by commenting, tagging, joining, sharing, liking, and publishing
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posts. According to Statista, there are over 3.6 billion people were using social media worldwide
in 2020, a number projected to increase to almost 4.41 billion in 2025.1 . This has resulted in an
ocean of data which presents an interesting opportunity for performing data mining and knowledge
discovery in many domains including mental health. The recent highly impressive advances inmachine learning and natural language processing present exciting opportunities for developing
automatic methods for the collection, extraction, representation, analysis, and validation of social
media data for health applications. These methods should be able to simultaneously address
the unique challenges of processing social media data and timely discover meaningful patterns
identifying emerging mental health threats.

2Workshop Theme and Topics

Traditional research on healthcare social analytics mainly focuses on descriptive methods suchas tracking health trends on social media and tracking infectious disease spread. The main distinguishing focus of this workshop will be the use of social media data for building diagnostic,
predictive and prescriptive analysis models for mental health analysis and applications such as
analysing how social media can impact on people’s mental issue, and predicting users’ mental
health status form social media data and recommending solutions to prevent the risk of committing unfortunate actions such as suicide.
The topics of interest of this workshop included but were not limited to the following:• Social media mining for automatic mental health monitoring and surveillance.
• Predicting user’s mental health status on social media.
• Association of social media Use with mental health• User behavior analysis and susceptibility prediction with regard to health-related data onsocial media.
• Predictive models for early detection of future mental illness from social media.
• Explainable AI for healthcare social media analytics.
• Ethics, bias, and fairness in analyzing social media for mental health analysis.
• New datasets and evaluation methodologies to help mental health analysis on social media.

3

Workshop Contributions

3.1

Keynotes

The workshop had two invited keynote speakers who shared their perspectives on social media
mining for mental health research and applications.
The first keynote speaker, Dr. Munmun De Choudhury is an Associate Professor in the Schoolof Interactive Computing at Georgia Tech. She presented her talk on “Employing Social Media toImprove Mental Health: Pitfalls, Lessons Learned, and the Next Frontier” to highlight a criticalanalytic perspective on the pitfalls of social media signals of mental health, especially when they1

https://www.statista.com/statistics/278414/number-of-worldwide-social-network-users/

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are derived from “proxy” diagnostic indicators, often removed from the real-world context in which
they are likely to be used. Then, to overcome these pitfalls, her talk presented results from two casestudies, where machine learning algorithms to glean mental health insights from social media weredeveloped in a context-sensitive and human-centered way, in collaboration with domain experts
and stakeholders. The first of these case studies, a collaboration with a health provider, focuses on
the individual-perspective, and reveals the ability and implications of using social media data ofconsented schizophrenia patients to forecast relapse and support clinical decision-making. Scaling
up to populations, in collaboration with a federal organization and towards influencing publichealth policy, the second case study seeks to forecast nationwide rates of suicide fatalities usingsocial media signals, in conjunction with health services data. Her talk concluded with discussionsof the path forward, emphasizing the need for a collaborative, multi-disciplinary research agenda
while realizing the potential of social media data and machine learning in mental health – onethat incorporates methodological rigor, ethics, and accountability, all at once.
Our second keynote speaker, Dr. Nazli Goharian, is Clinical Professor of Computer Scienceand Associate Director of the Information Retrieval Lab at Georgetown University. In her talkentitled “NLP Applications in Mental Health”, she shared her collaborative work done at theInformation Retrieval Lab at Georgetown University on detecting mental health concerns in social
media posts. The first application was on a dedicated mental health forum where the users whoregister to share and communicate their thoughts and feelings were suffering from some sort ofmental distress (sadness, depression, and potential of self-harm). The task was to triage theseverity of users’ posts to detect early the potential of self-harm as well as to evaluate the impactof forum activities and conversations on the users during a period of time. In the second typeof platform, i.e., non-dedicated, she focused on the question of whether we could detect if a user
was suffering from any one or more of nine mental health conditions, only using the generallanguage of the user; that is, the posts are not in mental health [sub]forums nor have any mental
health related words. She also explained how they have identified the diagnosed users, and how
carefully selected the controls. Further she showed the results of several baselines to detect theconditions. Finally, to address information reduction for a faster read and processing of users’
posts by moderators/counselors, she talked about her effort to summarize the posts to their shortforms.

3.2

Panel

Our workshop also hosted a panel discussion on “Mental Health and Social media” with 3 panelists,
namely Dr. Ugur Kursuncu from Georgia State University, Dr. Koustuv Saha from Microsoft andDr. Joshua Skorburg from University of Guelph.
The panelists shared their perspectives mainly on two themes:
The first theme was about the societal impact of COVID19 on health, economy and well-being.
For this topic, they argued that among the factors that influence the well-being of individuals, theindirect external factors have been critical drivers. Further, they discussed about their findingson the likely factors that impact the well-being of the populations with different circumstantial
characteristics during the pandemic.
As the second theme, the panelists talked about a number of epistemological and ethical issues
that must be addressed while mining social media data for the detection and treatment of mental
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illness. They highlighted some of these ethical issues including safety/efficacy, informed consent,respect for persons, inequality, and the gap between predictions and interventions.

3.3

Paper Session

All paper submissions were reviewed by at least two members of the program committee, which
consisted of experts from academia and industry alike. In the reviewing process, we ensured that
we maintain high quality standards, while at the same time making sure that less-mature yetinteresting work was also given a chance to be present and discussed at the workshop. Four full
papers were accepted based on the quality of the rigor of analysis, results and presentation, andwe provide a brief description for each contribution below:
I’m always in so much pain and no one will understand” - Detecting Patterns in Suicidal
Ideation on Reddit [Monselise and Yang, 2022]. In this study, the authors have investigated thepatterns in the behaviors of the users struggling with thoughts of suicide particularly on thesocial media platform Reddit. More specifically, they explored how Reddit users move or progress
between subreddits until they express active suicidal ideation. They have also looked at theseusers’ posting pattern in the time leading up to expressing suicidal ideation and the time after.
A large-scale temporal analysis of user lifespan durability on the Reddit social media platform[Nadiri and Takes, 2022]. In this study, the authors aimed to understand what early user activity
patterns fuel an ultimately durable user lifespan. To do so, they have analyzed what behavior
causes potentially durable contributors to abandon their social career at an early stage, despite
their strong start based on a uniquely processed temporal dataset of over 6 billion user interactions
on Reddit covering more than 14 years. They have assessed both user content creation activity
and the way in which this content is perceived based on three dimensions, dealing with a user’scontent a) engagement and perception, b) diversification, and c) contribution.
Supporting People Receiving Substance Use Treatment During COVID-19 Through A Professional Moderated Online Peer Support Group [Liang et al., 2022]. In this study, the authors havepresented a private, professional-moderated OPSC using the Facebook Group platform launchedin September 2020, to study its effects on the mental health wellness of women undergoing substance use treatment. This study was particularly meaningful as the participants were not able
to join in-person treatment sessions due to the COVID-19 pandemic. Their preliminary findings
indicated that study participants reported decreased loneliness and increased online social support three months after initiating the OPSC. They tended to interact with content initiated by aclinical professional more than those generated by peers.
Utilizing Pattern Mining and Classification Algorithms to Identify Risk for Anxiety and Depression in the LGBTQ+ Community During the COVID-19 Pandemic [Bierbaum et al., 2022].
In this study, the authors have examined the results of pattern mining and decision trees applied
to a dataset of survey responses about life for individuals in the LGBTQ+ community during
COVID, which have the potential to be used as a tool to identify those at risk for anxiety anddepression. Their study have used the data during pandemic in 2020 through 2022, to analyze
the impact on anxiety and depression. They have first used the FP-growth algorithm for frequent
pattern mining, which finds groups of items that frequently occur together, and utilized the resulting patterns and measures to determine which features were significant when inspecting anxiety

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and depression. Then, they have trained a decision tree with the selected features to classify if aperson has anxiety or depression.

4Conclusion, and Future Directions

In this special edition, we gathered researchers and practitioners from across the world and, inparticular, from different disciplines, such as information retrieval, data mining and machinelearning as well as social network analysis, healthcare informatics, network science and complex
networks. As in previous years, the MAISoN 2022 was highly interactive. Many discussions
emerged from the keynotes, panels, and presented papers about delivering technology and solutions
for mining actionable insight from social media data for mental health analysis and applications.
In the meanwhile, the proposal for MAISoN 2023 on Deep Learning Methods for Social Media
Mining has been accepted in 32nd International Joint Conference on Artificial Intelligence (IJCAI2023) which is scheduled to take place as an in-person event on August 21, 2023.

Acknowledgments
We would like to thank the authors of submitted papers to MAISoN 2022 and all workshop
attendees. Further, we thank our keynote speakers and panelists for sharing their insightful ideas
on mental health and social media. We also thank our steering and program committee members:
Steering Committee• Marcelo G. Armentano (ISISTAN (CONICET-UNICEN))• Ebrahim Bagheri (Toronto Metropolitan University)• Frank W. Takes (University of Amsterdam)
Program Committee• Michal Monselise (Drexel University)• Ali Jazayeri (Washington University)• Elad Yom-Tov (Microsoft)• Yelena Mejova (ISI Foundation)• Arkaitz Zubiaga (Queen Mary University of London)• Cheng-Te Li (National Cheng Kung University)• Damiano Spina (RMIT University)
We also express gratitude to TheWebConf 2022 organizers for hosting the workshops andproviding online meeting facilities.

ReferencesJosephine Bierbaum, Melissa Lynn, and Louis Yu. Utilizing pattern mining and classification
algorithms to identify risk for anxiety and depression in the LGBTQ+ community during theCOVID-19 pandemic. In Frédérique Laforest, Raphaël Troncy, Elena Simperl, Deepak Agarwal,ACM SIGIR Forum

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Aristides Gionis, Ivan Herman, and Lionel Médini, editors, Companion of The Web Conference
2022, Virtual Event / Lyon, France, April 25 - 29, 2022, pages 663–672. ACM, 2022. doi:
10.1145/3487553.3524697. URL https://doi.org/10.1145/3487553.3524697.
Ou Stella Liang, Christopher C. Yang, Anne Glenney, Richard E. Pointer, Sharlene Irving, BarbaraSchindler, and David S. Bennett. Supporting people receiving substance use treatment during
COVID-19 through a professional-moderated online peer support group. In Frédérique Laforest,
Raphaël Troncy, Elena Simperl, Deepak Agarwal, Aristides Gionis, Ivan Herman, and Lionel
Médini, editors, Companion of The Web Conference 2022, Virtual Event / Lyon, France, April
25 - 29, 2022, pages 673–676. ACM, 2022. doi: 10.1145/3487553.3524698. URL https://doi.org/10.1145/3487553.3524698.
Michal Monselise and Christopher C. Yang. ”i’m always in so much pain and no one willunderstand” - detecting patterns in suicidal ideation on reddit. In Frédérique Laforest,
Raphaël Troncy, Elena Simperl, Deepak Agarwal, Aristides Gionis, Ivan Herman, and Lionel Médini, editors, Companion of The Web Conference 2022, Virtual Event / Lyon, France,April 25 - 29, 2022, pages 686–691. ACM, 2022. doi: 10.1145/3487553.3524700. URL
https://doi.org/10.1145/3487553.3524700.
Amirhossein Nadiri and Frank W. Takes. A large-scale temporal analysis of user lifespan durabilityon the reddit social media platform. In Frédérique Laforest, Raphaël Troncy, Elena Simperl,
Deepak Agarwal, Aristides Gionis, Ivan Herman, and Lionel Médini, editors, Companion of The
Web Conference 2022, Virtual Event / Lyon, France, April 25 - 29, 2022, pages 677–685. ACM,2022. doi: 10.1145/3487553.3524699. URL https://doi.org/10.1145/3487553.3524699.

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