Understanding mental health conversation dynamics is crucial, yet prior studies often overlooked the intricate interplay of social interactions. This paper introduces a unique conversation-level dataset and investigates the impact of conversational context in detecting Moments of Change (MoC) in individual emotions and classifying Mental Health (MH) topics in discourse. In this study, we diferentiate between analyzing individual posts and studying entire conversations, using sequential and graph-based models to encode the complex conversation dynamics. Further, we incorporate emotion and sentiment dynamics with social interactions using a graph multiplex model driven by Graph Convolution Networks (GCN). Comparative evaluations consistently highlight the enhanced performance of the multiplex network, especially when combining reply, emotion, and sentiment network layers. This underscores the importance of understanding the intricate interplay between social interactions, emotional expressions, and sentiment patterns in conversations, especially within online mental health discussions. We are sharing our new dataset (ConversationMoC) and codes with the broader research community to facilitate further research1.
has yielded superior outcomes in tasks such as ques- • A new Reddit dataset, augmented with convertion answering [13, 14] and personalized recommenda- sational context and carefully annotated for use tion [15, 16]. Additionally, graph-based representations in Moments of Change (MoC) and Mental Health have proven beneficial in other conversation tasks such (MH) discourse classification, is now publicly as dialogue act recognition [11, 17], intent detection [18], available for the first time. This dataset introand topic modeling [19], contributing to improved perfor- duces an important development in identifying mance across these domains. These findings highlight the MoC using a valence and arousal space. potential of utilizing network structures to improve the • This study extensively compares suitable baseunderstanding and performance of diverse conversation- line models over the new MoC dataset. Further, related tasks. to encode the complex conversation dynamics, a
Inspired by prior research, this study explores the po- multiplex network structure is introduced, captential of leveraging social and meta-interaction informa- turing the intricate interplay between social intion for mental health tasks, including identifying MoC teractions, emotional expressions, and sentiment in an individual’s mood and classifying MH discourse. patterns within conversations, emphasizing the Notably, no existing datasets specifically address MoC uniqueness of this research. detection with a full conversation context, underscoring • A comprehensive exploration of the multiplex the novelty and importance of this study. To facilitate layers, determining the significance of each layer our investigation, we have curated a new dataset com- for conversational MoC and MH classification prising 967 conversations covering 15 MH topics sourced tasks. from the Reddit social media platform (explained in Section 3.2). This dataset ofers insights into the intricate The rest of the paper is organized as follows: Section 2 interplay between language use and social interactions. provides an overview of related work. Section 3 discusses Further, to encode the complex conversation dynamics, in detail the dataset curation. Section 4 discusses the we utilize a multiplex network representation of conver- experiment designs. Section 5 presents the experimental sations, wherein each layer captures diferent aspects of results and discussion, and finally, the study concludes the conversation, such as emotion, sentiment, and reply in Section 6. interactions (refer Section 3 for detailed discussion). By introducing a novel dataset and highlighting the signifi- 2. Related studies cance of representing conversation context via multiplex networks, this study aims to uncover hidden emotional dynamics and understand the impact of social interac- 2.1. Moment of change detection tions on individual mood shifts. Throughout the paper, Various studies have investigated the connection between the individual who starts the conversation is referred to changes in user language on social media platforms and as the target user, and other participants as non-target their mental health, specifically identifying significant users. transitions or shifts in sentiment and/or emotion states.
A comprehensive evaluation is performed to assess Work includes exploring language changes to establish a
the efectiveness of the proposed study in detecting MoC foundation for detecting the MoC by analyzing
sequenand identifying types of MH topics in discourse. Using tial textual content [20, 21]. The CLPsych Shared Task
suitable sequential and graph-based baseline models, the 2022 [
In a similar direction concerning mental health-related search, particularly a study conducted by Low et al. [
This section presents a detailed overview of the dataset utilized in our study, which has been collected from the Reddit social media platform using the Pushshift API3. This dataset has been curated to facilitate research in the ifeld of classifying mental health discourse and temporal moment of change (MoC) detection. For ease of reference, we named this dataset as ConversationMoC. In the following subsections, we will delve into the dataset’s composition, the data collection process, and the unique attributes that make it a valuable resource for investigating mental health-related conversational dynamics.
We collected data focusing on the posts that initiated conversations to compile our dataset5. Each user’s timeline constitutes a chronological record of their conversations, encompassing their posts and replies from other users. In this study, we use the term post to refer to both user comments and the initiating posts. To ensure meaningful and comprehensive data, we specifically selected conversations in which the target user contributed at least two posts, allowing us to examine the conversation dynamics efectively. Table 1 presents the dataset distribution, which consists of 963 target users participating in 967 conversations – 11,841 users contributed 28,659 posts, 4A subreddit is a thematic community on Reddit that focuses on specific topics. 5We hypothesize that the target user is either sufering or interested to know about the subject.
Target Users
Avg IS
posts
/Convs
with 9,221 posts from the 963 target users. levels change. When the emotion remains unchanged or
neutral throughout a conversation, it is labeled O. The
3.3. Data Annotation use of VA space allows a more structured assessment of
IS and IE and is less subjective than relying on simple
Three annotators with educational backgrounds in Psy- annotator label judgments of mood change as in [
The Valence and Arousal (VA) chart (shown in
Figure 2) is considered to annotate IS and IE, representing 4. Methodology
afective states in a continuous numerical VA space.
According to the Circumplex model [
In contrast, for a switch, both the valence and arousal 6https://en.wikipedia.org/wiki/Cohen’s_kappa
Post-to-post Multiplex graph (A)
Moment of Change (MoC) classification
Softmax(Target user posts)
Mental health
Discourse Classification
Softmax(FlattenedEMB)
Flattening Masked non-target user post embedding
Multi-head attention layer Conversation encoded
post embedding Multiplex graph learning model
Temporal encoded
Post embedding Sequence-to-sequence learning model
Post embedding
Conversationi posts (P)
4.1. Post embedding
relations. Figure 3 shows an overview of this experimen- target user’s moods, we utilize a Bidirectional Long
Shorttal framework, demonstrating how conversation dynam- Term Memory (BiLSTM) model [
ℎ→ = LSTM→(, ℎ→− 1, →− 1) ℎ← = LSTM← (, ℎ←+1, ←+1)
Conversation input posts multiplex network (A) Reply Emotion Sentiment Conversation input posts in temporal order (P) 2-layer GCN Shared weights 2-layer GCN Shared weights 2-layer GCN (Temporal Encoded) Post embedding H(0)
M a x p o o il n g
H oC n v e rs a it o n e n c o d e d p o s t e m b e d d i n g 4.3. Multiplex Graph Representation Social media conversations are inherently non-linear, marked by users responding to earlier and recent posts, potentially influencing the mood or emotion of future posts. Figure 4 shows a conversation’s multiplex network structure representation using a two-layer Graph Convolutional Network (GCN). This approach captures this non-linearity by introducing a multiplex network consisting of reply, sentiment, and emotion network layers.
Specifically, the
reply layer focuses on linking posts
involved in social interactions between users. The emotion
and sentiment layers are constructed by linking posts
with similar emotions and sentiments, classified using
the pre-trained RoBERTa-based emotion and sentiment
models [
Further, by applying max pooling, the GCN allows the
network to capture the most prominent information from
each layer, potentially emphasizing important features
contributing to the overall task. The resulting node
feature matrix () represents the enhanced post-embedding
of the -layer GCN model. In this study, we consider a
2-layer GCN model, where the input (0) represents the
temporal enhanced post-embedding output from the
BiLSTM network and the output (2) represents the final
enhanced post-embedding (H), capturing both temporal
and multiplex network of social and meta-interaction of
the conversation.
4.4. Multitask classification
The evaluation framework tackles two tasks
simultaneously: Moment of Change (MoC) detection and Mental
Health (MH) classification. MoC detection focuses on
while MH classification operates at the conversation level
to determine the specific MH topics in discourse. To
improve the MH classification task, we add a multi-head
selfattention layer [
(3) where b is a Boolean vector to mask the non-target users’ posts from H. 4.5. Loss functions The evaluation framework considers the entire conversations to classify the Moments of Change (MoC) of the interaction across various aspects of the conversation. identifying mood shifts of the target user at the post level, () = max ︂( {︁ ︁( − 1/2− 1/2(− 1) ())︁}︁ )︂ (H), resulting in an attention-weighted encoded represen
tation (H). Mathematically, the classification tasks =1 (2)
can be defined as:
1 ∑︁ ∑︁ (︁ · (1 − ) · T(1) · log( ))︁
ℒ = − =1 =1
ℒ = −
target user’s mood, it is essential to mask the posts of
non-target users. To train the model for the MoC
detection task, we apply the Focal Loss Function [
5.1. Detection of Moment of change This section evaluates the performance of the considered baseline models on the ConversationMoC dataset.
Initially, we evaluate these models using two input scenarios: (i) using only the target user’s posts (TU ) and (ii) utilizing the entire conversation (All). Notably, as the TU ∑︁ T(2) · log( ) input lacks social interactions, models like GCN and BiL=1 STM+GCN are not evaluated in this context. Further, we (4) conduct an extensive analysis to understand the impact
of diferent layers within the multiplex network on the where and represent the number of posts and MoC downstream tasks. The experimental results for the MoC class labels in the conversation, represents the weight detection task, achieved through 10-fold cross-validation, factor for the MoC class , and represents the focusing are presented in Table 2. This table includes the mean parameter to control the rate at which the loss decreases F1-scores for each class (IE, IS, O) as well as the macro for well-classified examples. For the MoC classification F1-score, providing a comprehensive view of the overtask, T(1) represents the true MoC label one-hot vector all performance. From the table, it is observed that the of the target user post i in the conversation. While T(2) BiLSTM+GCN model consistently outperforms its stanrepresents the true label of the conversation MH topic. dalone counterparts. In particular, the BiLSTM+GCN models, when incorporating the multiplex graph input 4.6. Comparision of model variants with the Emotion, Sentiment, and Reply (ESR) layers, exhibit the highest macro F1-scores, achieving 0.422 in the The MoC and MH classification tasks can be evaluated single-task setup and 0.438 in the multitask setup. An as single or multitask setups. Moreover, the conversation intriguing observation is that the performance of specific dynamics can be encoded in both setups using a stan- models significantly deviates from the average in a few dalone BiLSTM model, GCN model, or a combination folds, leading to a standard deviation of approximately of both (BiLSTM+GCN). To assess the impact of con- ± 0.02. For a detailed view of these results, please refer versation context, we compare two input scenarios: (i) to the boxplot presented in Appendix Figure 6, which TU, which encompasses solely the target user’s sequence visualizes the F1-score performances of the multitask of posts, and (ii) All, which encompasses the sequence models across all folds. These findings underscore the efof posts interacting with the target user’s posts in the fectiveness and consistency of the proposed framework, conversation. Based on the input type, we evaluate the validating its superior performance in detecting MoC considered models (BiLSTM, GCN, BiLSTM+GCN) over across mental health-related tasks. the MoC dataset using the pretrained post-embedding The results are evident; incorporating an entire conver(discussed in Section 4.1). Hyperparameter details are in sation context notably improves the performance of MoC Appendix Section A.2. detection models compared to using only the target user posts. Furthermore, the multitask setup consistently out4.6.1. Heuristic model for MoC detection performs the single-task setup9. Delving into the performance across diferent classes reveals intriguing insights.
The GCN model, in the multitask setup, emerges as the
best model, achieving an F1-score of 0.287 for the
escalation (IE) class. On the other hand, for the switch (IS) class,
the BiLSTM+GCN model achieves the best performance,
with an F1-score of 0.169. The single-task BiLSTM model,
which exclusively relies on the posts of the target user,
achieves the highest F1-score of 0.906 for the No MoC (O)
class. It suggests that the posts from the target user alone
Inspired by the Circumplex model [
To delve deeper into the impact of diferent layers within the multiplex network, we conducted a comprehensive 5.2. Mental health classification performance analysis of the BiLSTM+GCN model, as Figure 5 presents a bar chart illustrating the performance detailed in Table 2. The results reveal that the model of various models in classifying mental health (MH) disperforms better when leveraging the multiplex networks course. Rather than relying on traditional topic modthan relying on individual networks. Significantly, when eling techniques, we directly categorize the MH topics we examine the performance of the BiLSTM+GCN model discussed within the conversations using the models conacross the respective graphs, the Reply graph consistently sidered in this study. The evaluation includes single-task outperforms the Emotion and Sentiment graphs. This and multitask setups, using the categorical cross-entropy suggests that social interactions provide more useful in- loss function to train the MH classification task. As seen formation for the tasks we are interested in. In particular, in Figure 5, the performance is notably superior for the the Reply graph contains authentic, ground-truth data multitask models compared to their single-task counterof social interactions. In contrast, the Emotion and Senti- parts. In this study, the most notable performers among ment graphs are constructed based on the emotion and multitask models are the BiLSTM (All) and BiLSTM+GCN sentiment classification of each post using the pretrained (R), both achieving remarkable macro F1-scores of 0.85 RoBERTa classifier, which is susceptible to potential mis- and 0.84, respectively. These results substantiate that classifications, as evidenced by the performance of the incorporating conversation contextual information sigheuristic MoC classification model in handling IE and O nificantly enhances the accuracy of MH classification, classes. Moreover, when incorporating Reply and Senti- particularly when considering only the target user’s posts ment networks, the model’s performance improved even as the input data. This observation highlights the substanfurther, achieving the highest 0.470 F1-score. This in- tial contribution of conversation context information for dicates that the Reply network is practical in capturing enhancing the classification of mental health discourse. changes in the target user’s mood. The interplay be- Delving deeper into the performance across individtween users and the presence of emotionally charged ual MH topics, it becomes apparent that the BiLSTM (sentimental) conversations significantly impacts MoC model, incorporating All posts, excels in 8 MH classes, detection. By incorporating these additional layers, the while the BiLSTM+GCN (Reply) model leads in 7 MH model attains a more comprehensive understanding of classes (results detailed in Appendix Table 5). These re(a) Single-task (b) Multitask sults underscore the importance of conversation context information, with both models demonstrating robust performance across various MH topics. In summary, these ifndings emphasize the advantages of multitask models and highlight that integrating conversation context along with the Reply network significantly enhances the accuracy of MH classification within conversations. The BiLSTM+GCN (All) model emerges as a standout performer, achieving high performance in eight MH categories within this study.
Ethical approval for this study was obtained from the University of Southampton ethics board (submission reference ERGO/FEPS/64959.A1). The research involves the analysis of personal data sourced from the social media platform Reddit. To ensure compliance with ethical guidelines and regulations, we have adhered to the Reddit platform API’s terms and conditions, and our annotated dataset is shared with Reddit IDs only so other researchers can download the original Reddit posts and metadata directly from Reddit. During the annotation process, the annotators were informed about the potential risks of encountering disturbing content. They were encouraged to take regular breaks and time-outs from their annotation work to mitigate emotional overload.
Additionally, a clinically trained psychologist has been actively advising the team to provide expertise and guidance throughout the project. A comprehensive risk assessment has been conducted to identify and address any potential risks associated with this task. Our commitment to ethical considerations and the well-being of the annotators underscores our commitment to conducting responsible and sensitive research in the field of mental health analysis.
This study introduces a novel publicly accessible dataset (ConversationMoC) tailored to identify the Moments of Change (MoC) and classify Mental Health (MH) discourse within conversational settings. The importance of incorporating conversation information to identify MoC and classify MH discourse is investigated using a combination of BiLSTM and GCN models in single-task and multitask setups. The experimental results evidently show the significance of incorporating conversation information to identify MoC and classify MH discourse. Further, encoding the intricate social interactions, emotional dynamics, and sentiment patterns through multiplex network structure enhances classification performances. More specifically, the Reply network emphasizes the significance of 8. Limitations social interactions and user engagement. Additionally, when combined with the Sentiment and Emotion net- In this study, there are few limitations that warrant conworks, the classification performance further improves, sideration. Firstly, our findings are derived from a single underscoring the influence of emotional conversations Reddit dataset. While we envision the potential for our and overall sentiment. The multiplex networks repre- models to generalize well to analogous conversational sent an exciting new direction for future conversational datasets with a similar social context graph, we have analysis and mental health detection research. yet to conduct experiments on problem datasets beyond Reddit. This limitation arises due to the unavailability of publicly annotated datasets for MoC in this specific domain, underscoring the significance of our contribution in providing a new publicly accessible MoC dataset, ConversationMoC, for prospective research. Additionally, our study does not explore the performance of more recent and larger language models (LLMs) like OpenAI’s GPT3/4, Meta’s LLaMa, Stanford’s Alpaca, and Berkeley’s Gorilla models. While we anticipate potential improvements in performance by leveraging these advanced models, experimental validation of this hypothesis remains pending. Furthermore, from the perspective of the evaluation framework, several limitations and potential solutions to mitigate these challenges are highlighted: • Contextual Understanding in Short Conversations:
Acknowledging that short conversations with limited posts may pose challenges in contextual understanding, integrating LLMs can alleviate this issue by capturing a broader context. • Semantic Consistency in Dynamic Conversations:
Dynamic conversations with rapid emotional shifts due to longer conversations (e.g., 5 posts + 50 replies) present hurdles in maintaining semantic consistency. In this scenario, incorporating an additional attention layer into the framework could serve to weight the influence of diferent posts dynamically and replies within a conversation. Moreover, exploring the integration of guiding loss functions is suggested. These functions would guide the model to focus on the primary conversation topics and emotions, even amidst swift emotional changes. This combined approach could enhance the model’s understanding of key conversation topics, particularly if the conversation is full of changing emotions and dynamics.
Acknowledging the potential for the conversation multiplex network encoding framework to apply to various domains and recognizing the importance of testing it on diverse datasets, our current investigation faced limitations due to the scarcity of datasets with similar characteristics. In the future, we aim to expand our analysis to encompass a more comprehensive range of conversation datasets, thereby demonstrating the broader applicability of our framework beyond the scope of this specific domain. 10. Acknowledgement This work was supported by the Natural Environment Research Council (NE/S015604/1), the Economic and Social Research Council (ES/V011278/1) and the Engineering and Physical Sciences Research Council (EP/V00784X/1).
The authors acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work and the highly valuable insights into the mental health domain from Aynsley Bernard of Kooth Plc. ing, Knowledge and Information Systems (2024). 2098–2110.
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A.1. 15 subreddit topics In this study, we collected data from 15 mental health subreddits encompassing a wide range of topics. The 15 subreddits are Eating Disorder (r/EDAnonymous), Addiction (r/addiction), Alcoholism (r/alcoholism), Attention Deficit Hyperactivity Disorder (ADHD) (r/adhd), Anxiety Hyperparameters
Pretrained model
Sentence-BERT [
Value (r/anxiety), Autism (r/autism), Bipolar Disorder (r/BipolarReddit), Borderline Personality Disorder (BPD) (r/bpd), Depression (r/depression), Health Anxiety (r/healthanxiety), Loneliness (r/lonely), Post-Traumatic Stress Disorder (PTSD) (r/ptsd), Schizophrenia (r/schizophrenia), Social Anxiety (r/socialanxiety), and Suicide (r/SuicideWatch). Considering these diverse mental health topics, we aimed to capture a comprehensive picture of mental health discussions in online communities.
A.2. Hyperparemeters This study considers several hyperparameters to optimize the performance of the proposed model for detecting moments of change and identifying mental health topics in conversations. The detailed hyperparameter settings, including the dimensions of the output representations from pretrained models, are presented in Table 3. A.3. Moment of change classification Figure 6 presents boxplots representing the distribution of F1-scores for the moment of change (MoC) classification across three classes: IE (escalation), IS (switch), and O (No MoC), including the macro F1-score. Each boxplot represents a diferent model considered in this study, with the x-axis representing the models and the y-axis representing the F1-scores. The boxplots show the median (middle line), interquartile range (box), and range of the scores (whiskers), providing a visual representation of the performance distribution for MoC classification.
H TU All E S R ES ER SR ESR
Model
Input type
Entire posts + Emotion and Sentiment multiplex graph
plex graph
tiplex graph
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