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How Do I Start studying? For this research, we removed users labeled with suicide indication, giving a total of users with labels: Supportive, Suicide Ideation, Suicide Behaviors, and Suicide Attempt see Table 1 for statistics on annotated data.
Suicide Indication IN category separates users using at-risk language from those actively experiencing general or acute symptoms. Users might express a history of divorce , chronic illness , death in the family , or suicide of a loved one , which are risk indicators on the C-SSRS, but would do so relating in empathy to users who expressed ideation or behavior, rather than expressing a personal desire for self-harm.
In this case, it was deemed appropriate to flag such users as IN because while they expressed known risk factors that could be monitored they would also count as false positives if they were accepted as individuals experiencing active ideation or behavior. The users labeled as suicide indication in Reddit user dataset were removed because of high disagreement between annotators during post-level annotation.
Four practicing psychiatrists have annotated the dataset with a substantial pairwise inter-rater agreement of 0. The created dataset allows Time-invariant suicide risk assessment of an individual on Reddit, ignoring time-based ordering of posts.
For Time-Variant suicide risk assessment, the posts needed to be ordered concerning time and be independently annotated. Following the annotation process highlighted in Gaur et al. The annotated dataset of users comprises supportive throwaway account: , Non-throwaway account: and uninformative throwaway account: , Non-throwaway account: posts.
For throwaway accounts, the dataset had 37 supportive users S , 63 users with suicide ideation I , 23 users with suicide behavior B , and 17 users had past experience with suicide attempt A. User distribution within non-throwaway accounts is as follows: 85 S users, I users, 76 B users, and 33 A users. A,B,C,and D are mental healthcare providers as annotators.
We explain two competing methodologies: TvarM and TinvM, for suicide risk severity prediction. Prior literature has shown the effectiveness of sequential models e. Moreover, it has been investigated through experimentation that sentences formed by an individual express their mental state. Hence, these inherent textual features linguistic use of nouns, pronouns, etc. Motivated by prior findings suggests that LSTM selectively filters irrelevant information while maintaining temporal relations; we incorporated them for our Time-variant framework [ 76 , 77 ].
It is messed up. I dont even go to the exams, but I tell my parents that this time I might pass those exams and will be able to graduate. And parents get super excited and proud of me. It is like Im playing some kind of a Illness joke on my poor family. LSTMs learn a representation of a sequence.
Our LSTM model predicts the likelihood of each suicidal severity category of a Reddit post, taking into account its sequence of words. However, the representation of a post is learned independently; hence patterns across multiple posts are not recognized. We require a model which engineers features across multiple posts from a user. Convolutional neural networks CNNs are state of the art for such tasks [ 79 ].
It comprises of an LSTM model to generate probabilities of a post p 0 i , which is a sequence of word embeddings. Considers learning over all the posts made by a user to provide a user-level suicidality prediction.
For this methodology, we put together all the posts made by the user irrespective of time in SuicideWatch and other mental-health related subreddits.
TinvM possesses the capability to learn rich and complex feature representation of the sentences utilizing a deep CNN. Our implementation of CNN is well described in Gaur et al. The model takes embeddings of user posts as input and classifies them into one of the suicide risk severity levels. We concatenate embeddings of posts for each user and pass them into the model. Evaluations are performed using the formulations described by Gaur et al.
The italicized text are phrases which contributed to the representation of the post. These phrases had similarity to the concepts in medical knowledge bases. The italicized text are phrases which contributed to the representation of each post. We present an evaluation of the two methodologies: TinvM and TvarM, in a cross-validation framework using data from users.
We then obtained key insights into throwaway accounts, supportive posts, and uninformative posts. Through an ablation study using different user-types and content-types, we compare TinvM and TvarM models in the user-level suicide risk severity prediction.
We began our ablation studies with the TinvM setting, as shown in Table 5a. As can be seen from the table, experiment S1, which includes throwaway accounts, uninformative posts, and supportive posts, achieved the best performance. The modest improvement in precision and recall in suicidality prediction of throwaway accounts is because of verbosity in content compared to non-throwaway accounts. While throwaway accounts have largely been ignored in the previous studies, we noticed useful information on suicidality in their content see Table 6 [ 82 ].
We hypothesize that this is because users are more open to express their emotions and feelings when they can remain anonymous. In another ablation study of TvarM for predicting the suicidality of throwaway accounts, we note a significant decline in false negatives compared to TinvM. We found supportive posts to be more important in determining user-level suicidality S11 in Table 5c compared to uninformative posts.
This is because contents from a supportive user include past suicidal experiences, which could be higher in suicide severity, causing the TinvM model to predict false positives. The dense content structure of throwaway accounts at each time step improved the averaged recall in experiment S9 TvarM compared to S1 TinvM.
Thus, the time-variant modeling is akin to a hypothetical bi-weekly diagnostic interview between a patient and a clinician conducted in a clinical setting. The reduction in false positives and false negatives is due to sequence-preserving representations of time-ordered content, capturing local information about suicidality, and keeping important characteristic features across multiple posts through a max-pooled CNN.
In the TinvM context, irrespective of user-type, all types of content are required for high precision and high recall in predicting user-level suicidality. Lengthy posts expressing mental health conditions are often made by TA a , which resulted in high precision compared to Non-TA b. However, in the TvarM, seldom supportive behavior of suicidal users is important for assessing their suicidality c.
For Non-TA, there is a trade-off between precision and recall concerning uninformative posts. For clinical-grounding based assessment, we recorded the results in Table 7. We characterize the capability of TinvM and TvarM frameworks to predict possible suicide risk severity levels and, subsequently, discuss the functioning of each framework qualitatively. According to Table 7 , for people who are showing signs of suicidal ideation, the time-variant model is better than the time-invariant model a 5.
On the other hand, suicidal behavior is better captured by the time-invariant model a Apart from these categories, there are users on Reddit supporting others by sharing their experiences, who confound suicide risk analysis [ 13 ].
We found that TinvM is relatively more susceptible to misclassifying supportive users as suicidal compared to TvarM a 6. However, TinvM had difficulty separating supportive users from suicide attempters, contributing to increases in false negatives see Table 7. The false positives occur when users with suicidal ideation use future tense to express suicide behavior.
For example, in the following sentence, For not able to make anything right, getting abused, I would buy a gun and burn my brain , the user used a future tense to describe ideations, signaling a false positive. We notice that TvarM right is effective in detecting supportive and ideation users. TinvM left is capable of detecting behavior and attempt users.
We also record that a hybrid of TinvM and TvarM is required for detecting users with suicidal behaviors. Supportive users on MH-Reddit account for the high false positives in the prediction of suicide assessment because of the substantial overlap in the content with users having ideation, behavior, and attempts. The time-variant methodology discreetly identifies semantic and linguistic markers which separate supportive users from users with a high risk of suicide.
However, it is overridden by high-frequency suicide-related terms in TinvM, leading to high false positives [ 84 ]. Through our quantitative and qualitative experiments, we posit that there is a significant influence of User Types and Content Types on TinvM and TvarM models for suicide-risk assessment. We investigate the influence of throwaway accounts and supportive posts on TinvM and TvarM in estimating the suicide risk of individuals. Reddit users with anonymous throwaway accounts post on stigmatizing topics concerning mental health [ 85 ].
Their content is substantial in terms of context and length of posts Table 6. We highlight the following key takeaways from an ablation study on throwaway accounts: 1 More often, through such accounts, Reddit users seek support as opposed to being support providers. Yang et al. Supportive users as defined by our domain experts are either users with their own personal history of suicide-related ideations and behaviors with or without other mental health disorder comorbidities or MHPs acting in a professional capacity.
Identification of these users types has been of paramount significance in conceptualizing precision and recall see Table 5a—5d. From the results in Table 5a—5d , we notice a substantial decline in recall after removing supportive posts which also removes supportive users.
Removing supporting content makes it harder for the model to distinguish supportive users from non-supportive suicide ideation, behavior, and attempt users, causing an increase in false negatives. The aggregation of all helpful posts misclassifies a supportive user as a user with a prior history of suicide-related behaviors or suicide attempts.
For example, T0 timestamps : I have been in these weird times, having made 30ish cuts on each arm, 60 in total to give myself the pain I gave to my loved ones. I was so much into hurting myself that I plan on killing, but caught hold of hope ; T1: I realized that I was mean to myself, started looking for therapy sessions, and ways to feel concerned about myself ; TinvM classifies this user as having suicidal behavior, whereas TvarM predicts the user as supportive true label.
An important challenge for TinvM and TvarM models is to distinguish supportive users from non-supportive users on Reddit. Since content from supportive users semantically overlaps with content from non-supportive users see Table 8 , temporally learning suicide-specific patterns is more effective than learning them in aggregate. Identifying an individual when they are exhibiting suicide-related ideations or behaviors would provide significant benefit in making timely interventions to include creating an effective suicide prevention safety plan.
The methodology which correctly classifies a user either with suicide-related ideations true positive or higher suicide risk while maintaining high recall is desired Table 8c. Based on our series of experiments, we identified TvarM as efficient for the early detection of suicidal ideation or planning compared to the TinvM approach.
On F1-score, we recorded a 5. From Table 7 , we observe a relatively high recall for TinvM compared to TvarM while detecting users with suicide-related behaviors and suicide attempts. Reddit posts from either suicide attempters or users with suicide-related behaviors are verbose with words expressing the intention of self-harm. Users may make a chain of posts to explain their mental health status. If these posts are studied in time-slices, the model may not identify the user as suicidal.
We see a hybrid model the composition of TinvM and TvarM holding promise for early intervention. Table 8b. From the suicide risk severity based analysis, we infer that time-variant analysis is not applicable across every level of C-SSRS, and time-invariant modeling is required to estimate the risk better.
Further, from human behavior shown in the online conversations, users do not express signals of suicide attempts or suicide-related behavior in all their posts.
Psychiatrists treat a siloed community of patients suffering from mental health disorders, which restricts diversification. Information obtained from EHRs provide time-bounded individual-level insights throughout multiple appointments, often as infrequent as every months. For the MHP, patient monitoring in this traditional structure was, until recently, the only pragmatic option outside of resource-consuming intra-appointment telephone calls or community-partnered wellness visits.
The value of closer-to-real-time patient status updates is realized when specific and appropriate markers activate timely interventions prior to an impending suicide-related behavior or attempt. As compared to prior studies attempting to make similar identifications using Reddit user posts, this work uniquely combines state-of-the-art deep learning models with long-standing, clinically-common metrics of suicide risk. Notably, Reddit communications were treated to special translation into the domain of clinically-established tools and nomenclature to reveal the signal of suicide-related ideations and behaviors—otherwise hopelessly buried under more information than could be sorted by even the most diligent MHP.
Decades of research has supported the notion that early targeted interventions are critical for reducing suicide rates [ 87 — 89 ]. The result of this study is to develop an expert-in-the-loop technology for web-based intervention for a terminal mental health condition, suicide, and perform technology evaluation from the perspective of explainability [ 32 , 92 ].
Through concrete outcomes defining the capability of TinvM and TvarM, it is evident a hybrid model is desired to estimate the likelihood of an individual to exhibit suicide-related ideations, suicide-related behaviors, or suicide attempts for precise intervention. However, there are certain limitations of our current research that also motivates future work. First, both TinvM and TvarM were able to track the changing nature of concepts but ignore causal relationships.
Indeed, the data is not representing individuals suffering from other mental health conditions or comorbidity. Apart from the domain-specific limitations in our current research, we noticed the problem of handling intentional and unintentional biases to be unaddressed. The intentional bias in the form of knowledge also called Knowledge bias is augmented through contextualization and abstraction.
Another form of aggregation bias in both TinvM and TvarM is using the concatenation function to represent a post or a user. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Feb 12 PM.
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Please upload your review as an attachment if it exceeds 20, characters. Reviewer 1: Novel study addressing the knowledge gap pertaining to Time-variant and Time-invariant assessment of suicidality. The manuscript is written in comprehensible form and present possible algorithms to assess suicide risk. Reviewer 2: The purpose of this study is to compare time-variant and time-invariant modeling to predict suicidal risk among Reddit platform users. The time-variant model better identifies supportive users and those with suicidal ideation, while the time-invariant model allows for better identification of suicidal behaviors and attempts.
The introduction and the Materials and Methods sections could be more clearly structured. Specific suggestions are made below. In the literature on suicide, there is a lack of consistency in the terms used to report on suicidal thoughts and behaviors. In this manuscript, several different terms are used, and the reporting of suicidal thoughts and behaviors should be standardized throughout the manuscript.
For example, terms such as "successfully die by suicide" abstract or "committing suicide" line 56, p. Where it is necessary, the type of STB could be specified e. Part 2: Suicide-related ideations, communications, and behaviors.
Suicide Life Threat Behav 37 3 Suicide and Life-Threatening Behavior 26 3 The following sentence "Stratifying risk in terms of severity and temporality is important in the risk management of a suicidal patient e.
The statement below could also be further discussed in the manuscript: First, in the Introduction to reinforce the rationale of the study; second, in the Discussion to highlight the relevance of the results. Overall, the introduction could be more concise and the clinical relevance of the development of learning algorithms to assess suicide risk could be further discussed.
To better contextualize the importance of using time-variant models, the limitations of time-invariant models could be discussed in more details. For example, it is reported that "this approach is limited due to poor patient engagement and treatment adherence » line The authors could clarify how poor patient engagement or treatment adherence impact time-invariant models.
The following sentences could be included in the Discussion section when the findings are discussed rather than in the Introduction:. The last paragraph of the introduction line , p. It would be clearer if the information on the description of the dataset was grouped together. The aim of the study should be explicitly reported at the end of the introduction.
However, some aspects of the method could be clarified. At the end of the Introduction section line , p. Then, the first paragraph of the Material and Method section p. It is not clear whether the created dataset of suicidal redditors is drawn from the dataset described in the first paragraph of the Materials and Methods section.
This should be clarified. Moreover, it is mentioned at line p. Then, line p. What explains the distinct number of users? The date or at least the year on which the number of users was observed should be mentioned since the number of users in the SuicideWatch subreddit seems to be increasing.
The article by Gaur et al, is often cited for more information on the method to create the dataset. The description in the article by Gaur et al. The description provided in this manuscript could be revised to include more details from the Gaur et al. For example, Figure 3 in Gaur et al. In the section Annotator Agreement, it is first reported that a dataset of users was used line , p. Then it is mentioned that « the annotated dataset of users Once the dataset is described at the beginning of the method, as suggested, and Figure 2 is presented, it would avoid confusion to always refer to the users that are included in the study.
Hypotheses, such as the one announced at line p. Interpretation of the results should also be in the Discussion e. The study produces interesting results. The discussion could further highlight the clinical implications and implications for future research. More references should be added to the discussion. It would be interesting to further contrast the results with other studies in the field and make hypotheses to explain the results such as the hypothesis included in the results section, line , p.
The last sentence of the Discussion line , p. Cites should be before the period of each sentence e. Line 48, p. Alternatively, the sentence could simply be reworded "There are three User-Types in mental health subreddits MH-Reddits ". CNN should be defined the first time it is used line , p. Figure 4 in the text refers to the fifth figure in the appendix. PLOS authors have the option to publish the peer review history of their article what does this mean?
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To use PACE, you must first register as a user. Registration is free. Please note that Supporting Information files do not need this step. We thank the reviewers for their constructive feedback that greatly improved this manuscript. We have summarized the changes we made below based on these feedback. Response: We appreciate this particular comment from the reviewers. We have addressed the points highlighted, specifically for introduction and the Materials and Methods sections, on clarity, clinical relevance, and broader impact of the study.
We have made necessary changes in blue color a color coding followed for track changes. Response: We have noticed the lack of such consistency that was brought up by the reviewers providing examples. With our resident psychiatrist co-author, we have carefully reviewed the following two papers for consistency on the terminology related to suicide risk: a Silverman MM, Berman AL, Sanddal ND, O'Carroll P W, Joiner TE Rebuilding the tower of Babel: a revised nomenclature for the study of suicide and suicidal behaviors.
Response: We have addressed this aspect in multiple places. Please see the following lines. In addition, we have clarified the initial two paragraphs of the author summary and made the necessary edits in the revised version. Response: We have made changes that reinforces the above statement and explained its relevance to end-users, who are mental healthcare providers in our study. Please refer to paragraph from line no. Response: We have made significant efforts in tightening the introduction by removing redundancy and focussing more on the clinical relevance of the study.
The introduction also contains the prior research related to this study. Please see Lines. Response: We have moved this paragraph from introduction to discussion Please see Lines Response: We moved all the parts of the dataset description into Materials and Methods section.
Response: We explicitly created a paragraph at the end of the introduction that describes the key contributions of this study. Please see Lines Response: We have revised the description of redditors for clarity. Please see the Lines in introduction and in Materials and Methods.
Also, we have included Figure 3 taken from our previous work Gaur et al. Response: We have updated the number of users in our dataset in the Introduction Line no. We have also added Table 1 on Data Statistics.
Response: We agree that this will provide clarity on the dataset creation. We have included additional summary information and provided reference to the sections in Gaur et al. Response: We have provided clarification on how the dataset of users has been created. Furthermore, Table 1 and Figure 3 now provides more details on the dataset and its creation process.
Response: We have moved the hypothesis to discussion, as suggested by reviewers. Also, we have specifically mentioned relative novelty in this research compared to prior research on suicide and social media Please see lines. The post examples are really useful to better understand the results. Response: We have moved the suggested and similar statements that convey the interpretation of the results to the Discussion section. Response: We have now improved the clinical relevance of study with strength of the research, added a paragraph on limitations and future directions.
Response: We have added Limitations of the study in the Discussion section Please see the line no. Also, we have specifically mentioned relative novelty in this research in comparison with prior research on suicide and social media Please see line no.
Response: We have made the required changes in Line No. Response: We have made the change in line no. Response: We have revised the order of Tables. Though there will be a change in the number as a new table has been added Table 1 on data statistics. Please submit your revised manuscript by Apr 29 PM.
Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.
Reviewer 1: All the reviewer comments have been incorporated in the manuscript by authors. The introduction, Materials and Methods and discussion sections are clearly structured.
Reviewer 2: The authors are very responsive to comments made and have done a good job of improving the rationale of their study. I still have a few minor comments on the revised version of the manuscript. Suicide-related terms are reported much more consistently. However, as mentioned in the O'Carroll paper, the revised term for 'completed suicide' is simply 'suicide' or 'died by suicide'. Efforts are now being made to avoid all terms with "positive" connotations such as "completed" when referring to death by suicide.
I am still not sure why the paragraph from line to is not in the method section. This paragraph seems redundant with the paragraph from line to which is in the method section.
It would be helpful to define the category "suicide indicator" when it is mentioned that this category is excluded from the study line The authors have done a good job of improving the discussion and incorporating the comments made. We thank the reviewers again for their constructive feedback that has greatly improved the quality of this manuscript. We have addressed the changes suggested by the reviewers.
Places where we have made edits, as suggested by reviewers, are colored in brown. Q1: Suicide-related terms are reported much more consistently. Q2: I am still not sure why the paragraph from line to is not in the method section. Response: We thank the reviewer for suggesting the change. We have moved the content from line numbers starting from to in the Introduction section to line numbers to in the Materials and Methods section. Q4: It would be helpful to define the category "suicide indicator" when it is mentioned that this category is excluded from the study line An invoice for payment will follow shortly after the formal acceptance.
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If we can help with anything else, please email us at gro. PLoS One. Published online May Vamsi Aribandi 2 Kno. Amanuel Alambo 2 Kno. Krishnaprasad Thirunarayan 2 Kno. Amit Sheth 2 Kno. Vincenzo De Luca, Editor. Author information Article notes Copyright and License information Disclaimer.
Competing Interests: The authors have declared that no competing interests exist. Received Jul 31; Accepted Apr 6. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Abstract Suicide is the 10 th leading cause of death in the U.
Introduction Social media provides an unobtrusive platform for individuals suffering from mental health disorders to anonymously share their inner thoughts and feelings without fear of stigma [ 1 ].
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