2023 Information Processing Society of Japan Kansai Branch Conference

C-09

NeedToTalk: An Inverse Social Network For Mental Health
Improvement and Well Being
Chouman Hussein†

1.

Tomokazu Matsui†

Yuki Matsuda†

Introduction

Mental health disorders present signiﬁcant challenges
in today’s society, aﬀecting a vast number of individuals. According to global statistics, over 322 million
people experienced depression in 2017 [11]. Accurate
diagnosis is, therefore, crucial for eﬀective interventions
and well-being improvement [2]. To explore ways of enhancing mental health diagnosis and proposing better
treatments, the Person Speciﬁc Network (PSN)method
and DATA-IN approach were initiated. PSN constructs
real-time networks capturing an individual’s psychological experiences using ESM data, while DATA-IN generates personalized treatment recommendations basedon person-speciﬁc dynamic network models [12, 16].
Social media platforms oﬀer potential beneﬁts interms of simplifying social interactions, promoting engagement and retention in services, and facilitating access to peer support networks, which can contributeto improving mental well-being [10]. However, existingsocial media and its usage has been linked to negative psychological outcomes such as loneliness, cyberbullying, anxiety, and depression [10, 18, 7, 6]. On theother hand, chat-based hot-lines using instant messaging platforms are a preferred and eﬀective method forproviding health promotion support and diverse healthadvice [5, 10]. Furthermore, well-managed anonymity
can facilitate self-disclosure and thus improve mental
well-being [9, 4, 17, 3].
Moreover, a study in the ﬁeld of artiﬁcial intelligence and mental health improvement has found that
AI-powered conversational agents using reinforcement
learning show potential for personalized mental healthsupport [13]. Considering these factors, we aim to introduce a novel social media platform called “NeedToTalk” - an inverse social network. This platform will
facilitate the anonymous exchange of ideas, information, and content without fostering personal relationships or interactions. Our approach will leverage reinforcement learning and incremental learning to safeguard users’ mental well-being. NeedToTalk will curate
and deliver only beneﬁcial content tailored to users’
speciﬁc needs and preferences. Through ongoing adaptation, the platform intends to enhance user experiences and promote a healthier online environment.
This paper presents an inverse social network andhighlights the signiﬁcance of integrating innovative technologies to enhance healing and improve outcomes inthe ﬁeld of mental health within the environment of
NeedToTalk.
†

Nara Institute of Science and Technology

2.

Hirohiko Suwa†

Keiichi Yasumoto†

Related Work and Challenges

This section surveys existing studies related to our
proposed SNS platform, NeedToTalk.
2.1

Mental Health and Existing Diagnosis

Mental health issues aﬀect millions worldwide [11].
To address misdiagnosis challenges, Person-Speciﬁc Networks (PSN) oﬀer personalized statistical models forpsychotherapy exploration[5]. The DATA-IN approach,based on PSN, captures individual symptom patterns
and dynamics to generate personalized treatment recommendations [16, 12]. However, PSN and DATA-IN
have limitations, including data requirements, limited
generalization, interpretability challenges, ethical concerns about data privacy, potential bias, and a focus on
personalized strategies rather than comprehensive solutions. On the other hand, sharing recovery stories has
become a central practice in mental health care, promoting empathy, understanding, and peer support[8].
Emotions play a signiﬁcant role in shaping users’ behaviors and content interactions[15]. Understanding
users’ predominant emotions can guide the oﬀering of
relevant and beneﬁcial content.
2.2

Social Media and Mental HealthSocial media platforms are crucial in providing social interaction and support for young adults with mental illness, oﬀering access to peer support networks andopportunities for self-disclosure [5]. However, excessive social media usage, driven by features like inﬁnite
scrolling, has been linked to adverse psychological effects, including “Facebook depression” and correlations
with depression, social comparisons, and body image
concerns on platforms like Instagram [18, 7]. Additionally, platforms like Snapchat have been associatedwith lower levels of mental well-being and cyberbullying incidents [6, 1]. Addressing these issues is crucial for fostering a safer and healthier online environment [10, 18, 7, 6, 1].
2.3

Digital Anonymity and Self-Disclosure for Improved Well-being

A study about anonymity in social media foundthat:• There are more online disclosure when anonymous with social ties,• Intimacy regulation present in all conditions, stronger
with real social ties,• Negative emotions reduce disclosure in real-nameconditions,• No expected disclosure increase due to negative
emotions in anonymity.

• Self-disclosure is rewarding and promotes humanconnection.

User Privacy and Data Security in Digital
Platforms:
Ensuring user privacy and data security on digital• Self-disclosure has therapeutic beneﬁts.
platforms is crucial for building trust and encouraging
These ﬁndings underscore the importance of self-disclosure individuals to seek mental health support online.
Promoting a Positive Social Media Environfor human connectedness and its positive impact on
ment:
well-being. Consequently, social media platforms should
Addressing the adverse psychological outcomes ofconsider accommodating anonymity preferences to ensocial media usage, such as loneliness, cyberbullying,hance users’ overall well-being [9].
anxiety, and depression, is vital. Creating supportive
However, it is essential to recognize that anonymity
and positive online environments is critical to promoton speciﬁc social media platforms has also resulted ining mental well-being.
detrimental eﬀects. The perceived security of anonymity
Balancing Anonymity and Responsible Usmay lead users to engage in self-disclosure, potentiallyage:
revealing negative aspects of themselves, contributing
Striking a balance between enabling anonymity for
to issues such as cyberbullying, oﬀensive content, andself-expression and preventing misuse or harmful bea toxic culture [3, 4, 17].
haviors is necessary to harness the beneﬁts of online
2.4 AI-powered Conversational Agents for Mental
support systems.
Health Support
Reﬁnement and Ethical Considerations of AISun et al. propose a novel approach to using converPowered Conversational Agents:
sational agents (CAs) for behavioral intervention and
Further reﬁnement of AI-powered conversational agentsmental healthcare. They advocate for CAs with genfor mental health support is needed. Ensuring their reerative models to deliver more accessible, scalable, andliability, empathy, and ethical usage, including privacy
personalized interventions, reducing the stigma assoand conﬁdentiality, is crucial.
ciated with seeking help. Existing CAs often rely on
Eﬀectiveness of Existing Methods and Interpre-scripted conversations and rules, limiting their efventions:fectiveness and ﬂexibility.
Research gaps exist in optimizing current mental
To overcome this limitation, the authors propose
health interventions and methods to achieve better outintegrating generative models and reinforcement learncomes for users.
ing techniques. This enables continuous improvement
Addressing these challenges and research gaps willof the generative model and the agent based on huadvance mental health, social media, and existing inman feedback from clients and therapists during interterventions, improving support systems for individuals
actions. Techniques like few-shot generation, promptand society.
engineering, and reinforcement learning from human
feedback (RLHF) are used in the Human-in-the-Loop
3. Proposalinteraction.
3.1 Introduction
This approach holds signiﬁcant potential for senIn today’s digital age, social media platforms havesitive ﬁelds such as mental healthcare and behavioral
become ubiquitous, facilitating personal connections andintervention. Further research should be conducted tointeractions. However, the growing concerns regarding
explore the application of conversational agents withprivacy, cyberbullying, and mental well-being have led
generative models backed by reinforcement learning into the emergence of new approaches to social networkthe ﬁeld of mental health and well-being [13].
ing. This proposal introduces the concept of an “In2.5 Challenges and Research Gaps
verse Social Network” (ISN), a digital platform that
Despite existing research and advancements, sevenables the anonymous exchange of ideas, information,eral signiﬁcant challenges and research gaps persist inand content without forming personal relationships or
the ﬁelds of mental health and social media, emphasizinteractions. The primary goal of ISN is to prioritize
ing the need for further exploration and innovation touser privacy and mental well-being by focusing on conimprove interventions and support systems.
tent rather than personal connections.
Diagnostic Accuracy and Eﬃciency:
Traditional mental health diagnosis methods often
lack accuracy and eﬃciency, resulting in delays in providing timely interventions. Developing more precise
and eﬃcient diagnostic tools is crucial to enhance theaccuracy of mental health diagnoses and improve theoverall quality of care.
Diagnostic Accuracy and Eﬃciency:
Improving the precision and speed of mental healthdiagnostic tools is essential to provide timely interventions and enhance overall care quality.

3.2

Motivation

The motivation behind this proposal lies in addressing the limitations of traditional social media platforms
and traditional health diagnosis. Common social media platforms often require users to share personal information and form direct connections, which can leadto privacy issues and cyberbullying. Additionally, theconstant engagement and deep interactions on theseplatforms can negatively impact mental well-being.

ISN, represented by the proposed platform “NeedToTalk,” aims to provide users with a safe and supportive space for anonymous sharing and discussions. Byemphasizing content-based interactions and employing
well-managed anonymity, NeedToTalk seeks to foster apositive environment for mental well-being.
3.3

Comparison of ISN and Common Social MediaAn inverse social network refers to digital platforms
that allow anonymous exchange of ideas, information,and content without forming personal relationships orinteractions. They prioritize user privacy and prevent
identiﬁable proﬁles or direct connections.
To highlight the key diﬀerences between ISN andcommon social media, we introduce the FRIENDe
method:Word : Interactivity”Interactivity” refers to user engagement and interaction on all digital platforms.
Abbreviation: SNI (Social Network Interactivity)”Social Network Interactivity” (SNI) refers to whatdeﬁne interactivity online.
Acronym: FRIENDeFRIENDe is the method that is used to highlightthe key diﬀerence between interactivity on comon social
media and interactivity on inverse social network.
Table 1: Comparison of Common Social Media andInverse Social Media
FeatureFriendship/Content
Moderation
Relationship/Data
Privacy andSecurity
Interactions

Engagement

Networking

DigitalEnvironments
PurposeCommon Social Media
Users are identiﬁed by
proﬁles. Moderation may
be manual or automated.
Bonds form via Shared Interest and data privacy
varies.
Users engage through various interactivity, privatemeans.
Deep interactions involve
conversations and repeated exchanges.
Relationships
buildthrough groups, events,
and direct outreach.
Platform context signiﬁcantly inﬂuences user interactions
To create and maintain social connections, share life
updates, and engage withothersInverse Social Media
Users remain anonymous,
moderation based onuser’s proﬁling
Focus on content; privacy
and security emphasized

3.5

Well-Managed Anonymity and Personalization

Well-managed anonymity, as utilized in NeedToTalk,
goes beyond traditional anonymity by fostering a safeand respectful online environment. The platform tailors content and user interactions to individual needs
and interests, providing a supportive space free fromharmful or undesirable content. By prioritizing user
privacy and mental well-being, NeedToTalk promotes
positive personal growth and meaningful interactions.
This brings us to the diﬀerence between NeedToTalkand existing platforms shown in the Table below.
Table 2: Diﬀerence between NeedToTalk and existing
platforms
Platform

Anonymity
Level

Anonymity
featuresNo
Proﬁles,
Location-based
posts

Health Support Focus

YikYak [4]3Whisper [17]3

Share Secret, No
personal info

X

Gravity [14]

3

Total
Anonymityon the platform

4chan [3]3

Total
Anonymity

3

Well-managed
Anonymity,
Impossibility
to link user s
post to users.
AI
poweredanonymous chatInteraction
prioritizes
content over speciﬁc users
Users engage through content contribution and reactions
Minimal personal connections, content topics prioritizedPrioritizes anonymity andcontent over personal interactions.
To share and discuss content anonymously, without forming direct social
connections

Table 1 compares “Common Social Media” and “Inverse Social Media” based on features such as user identiﬁcation, content moderation, privacy emphasis, userinteractions, engagement style, networking approach,digital environments, and platform purposes.
3.4

We classify anonymity into three levels:
Level 1: No anonymity - Real names and information accessible.
Level 2: Pseudonyms - Basic anonymity, personal
info may be linked.
Level 3: Content-based anonymity - No identiﬁable info shared, focus on message content.
The proposed ISN platform, NeedToTalk, oﬀers wellmanaged anonymity (Level 3), ensuring user safety anda positive online experience. It employs AI-powered
anonymous chat to facilitate interactions while maintaining content-based anonymity.

Anonymity in ISN

Anonymity is a crucial feature of ISN, ensuring user
privacy and protection. In the context of digital platforms, anonymity means being unidentiﬁable, safeguarding an individual’s identity and personal information
during online interactions to prevent linkage to theirreal-life identities.

NeedToTalk

3.6

X

X

X

✓

Limitations
Cyberbullying,
Limited content
moderation
Cyberbullying,
Limited content
moderation
Personal information is collected at registration, Cyberbullying
Oﬀensive content, lack ofmoderation,
toxic cultureNo
predeﬁned
data
because of Total
Anonymity

Experimental Design

The proposed experimental design involves the following steps:
1. Initial Information Gathering:First, we use a conversational agent to collect initial information from anonymous users through a preassessment questionnaire. Next, we understand theircurrent emotional state. We then utilize a generative model to reﬁne the questionnaire based on user
responses, identifying the most prevalent emotion toshape the user’s proﬁle eﬀectively.
2. Connecting Users with ISN:
We connect users in NeedToTalk, ensuring content
and interactions align with their preferences. In essence,for every new state a user enters, there is a history

– Vulnerable Content– Toxic Content

Figure 1: metrics about user stateof their previous interactions with other users or usergenerated content.
This brings us to the formulation outlined below:
Let U = {u0 , u1 , . . . , un−1 } be the set of users.
Let S = {s0 , s1 , . . . , sn−1 } be the set of states ofa user, where the state of a user u is of the form
(xu,0 , xu,1 , . . . , xu,k ), with each xj an observable interaction of ui in NeedToTalk.
Let C = {c0 , c1 , . . . , cl } be the set of consumablecontent in NeedToTalk.
Goal

Analyzing user behavior and their interactions inreal-time allows us to generate more precise user proﬁles. This classiﬁcation enables us to deliver personalized and relevant content, fostering a positive andsupportive environment for mental well-being.
3. Conversational Agent for Emotional Insights:
We employ a conversational agent trained with psychological data to gain insights into the user’s emotions
and feelings, providing better support and understanding.
4. User Classiﬁcation:
We categorize users into Feeling Good (Normal andPotential Healing users) and Feeling Bad (Vulnerable
and Harmful users) based on their emotions and interactions.

Train an ML model that approximates ui ’s proﬁlebased on si to perform user matching (f1 ) and recommending content (f2 ).
• f1 (P1 ) = {ui } ⊆ U − {ui }• f2 (P2 ) = {ci } ⊆ C
P1 -User Proﬁle: P1 represent the proﬁle of a user
based in their interactions and states in NeedToTalk.
Each user’s proﬁle is a summary or representation of
their observable interactions in the form of states, denoted as si
p2 -Content Proﬁle: P2 represents a speciﬁc content
item to a user proﬁle in NeedToTalk. The set C contains all the consumable content items availables on
the paltform and each content item has its own proﬁle,denoted as ci
In order to eﬀectively train our model, we need toclassify both users and their content. Our approach
involves categorizing all users into two main groups:• Feeling Good Users:

Figure 2: User’s classiﬁcation
5. Content Classiﬁcation:
We classify shared content into Good (Normal andInspirational) and Bad (Sad/Vulnerable and Toxic) basedon their emotional impact.

– Potential Healing Users– Normal Users• Feeling Bad Users:– Vulnerable Users– Harmful Users
Similarly, we organize the content shared by users
into two main categories:• Good Content:– Inspirational Content– Entertaining Content• Bad Content:

Figure 3: Content classiﬁcation

We diﬀerentiate the content and the user as every
kind of content and the user has speciﬁc emotions asshown in Fig. refﬁg:system.

Figure 4: Emotions behind users and their content
6. User Satisfaction and Reinforcement Learning:
To assess the progress of the user’s state, we propose employing reinforcement learning to deﬁne user
satisfaction as a reward. By doing so, we can eﬀectively gauge the evolution of the user’s state for the
better by using the following equation.

Figure 6: User’s matching

Q(s, a) ← (1−α)·Q(s, a)+α·(rt+1 +γ·max Q(st+1 , at+1 ))at+1where: Q(s, a) represents the quality of action a instate s, α is the learning rate, r is the reward received
after taking action a in state s, γ is the discount factor,
and maxa Q(s, a) is the maximum quality of all possible
actions in the next state s′ .
This reinforcement learning approach allows NeedToTalk to continuously analyze users’ interactions,
adapt, and provide personalized support based on user
satisfaction as a reward.
Our approach involves utilizing the metrics illustrated in Fig. 5 below to measure user satisfaction.

Figure 7: Content matching
Figure 5: metrics about user’s satisfaction
If the user’s satisfactionn+1 − satisfactionn is ≤ 0,
no rewards are given.
3.7

Hypothesis

After getting the user state, classifying them andtheir content, we propose some hypothesis according
to how we should match them with other users andcontent :In Fig. 6, the red arrow symbolizes the priority
assigned to the users, while the blue arrow represents
the potential matching between the user’s preferences.
Based on the user’s current state and the emotions behind their interaction, we propose a matching approachthat caters speciﬁcally to their needs.
Fig. 7 shows how we are going to match our diﬀerent users with their diﬀerent content.
Personalized Content:

We match all of the users with inspirational content
and entertaining content according to the emotions behind their interaction and thus their need and interest.
Protective Measure:
We show the inoﬀensive content to the normal user
and potential healing user in order for them to provide
some insight to the user responsible for those post/content.
User Safety Priority:
To maintain the highest level of user safety, we refrain from showing inoﬀensive content to anyone. Ourcommitment is to protect our users from content that
may have a negative impact on their well-being.
With those diﬀerent users and content matching approach, we want to be sure to connect the users to what
they need to see or may be interested in seeing by being
sure that we will not match them to content or usersthat may hurt them.

3.8

Relation to mental health

Figure 8: Graph network from ﬁrst interaction to improved state
Having successfully classiﬁed our users and analyzed their content, we gained valuable insights into
their interactions.
By deﬁning a user satisfaction metric, we were able
to eﬀectively measure the improvements in their overall
experience. We then utilized a graph network as you
can see in the ﬁgure above to visually depict and emphasize the diﬀerent stages of progress from the initialuser state to their enhanced state and eventual recovery.
This approach allow us to gain a comprehensive understanding of the positive transformations our users
undergo through their journey with our platform.

4.

Conclusion

In this paper, we have introduced “NeedToTalk,”an “Inverse Social Network” (ISN) that prioritizes user
privacy and mental well-being. Unlike traditional social media platforms, NeedToTalk focuses on contentbased interactions without personal relationships, fostering a safe online environment through well-managed
anonymity. We proposed an experimental design thatinvolves user and content classiﬁcation, emotional insights using a CA, user satisfaction measurement withreinforcement learning, and personalized content matching. The platform’s emphasis on mental health includes
identifying user emotions, connecting them with relevant content, and visualizing their progress throughgraph networks.
Through our proposed approach, we expect to ﬁnd
several key outcomes. First, we anticipate that NeedToTalk’s emphasis on content-based interactions andanonymity will lead to increased user privacy and reduced negative eﬀects associated with deep personalconnections on traditional social media platforms. Second, by employing reinforcement learning to measureuser satisfaction and connecting users with personalized content, we hope to see enhanced user satisfaction
and positive engagement within the platform. Lastly,visualizing user progress through graph networks canhighlight positive transformations and eventual recovery experienced by users as they interact within NeedToTalk.
There are several avenues for future work and improvements. One area of focus would be reﬁning user
matching algorithms to ensure users are consistently
connected with content and other users that are most
relevant to their needs and interests. Additionally, integrating advanced emotional insights, leveraging natural language processing techniques and emotional AI,
would allow for more nuanced and accurate emotional
analysis of user interactions. Conducting longitudinalstudies to assess the long-term impact of using NeedToTalk on user mental well-being and collaboratingwith mental health professionals to enhance the platform’s support features are also essential steps in making a meaningful impact on the lives of our users.

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