Dataset Description: Suicidal Tweet Detection - This dataset provides a collection of tweets along with an annotation indicating whether each tweet is related to suicide or not. The primary objective of this dataset is to facilitate the development and evaluation of machine learning models for the classification of tweets as either expressing suicidal sentiments or not. This dataset has been internally generated by our team members specifically for our NLP project.

Columns: 1. Tweet: This column contains the text content of the tweets obtained from various sources. The tweets cover a wide range of topics, emotions, and expressions. 2. Suicide: This column provides annotations indicating the classification of the tweets. The possible values are: - Not Suicide post: This label is assigned to tweets that do not express any suicidal sentiments or intentions. - Potential Suicide post: This label is assigned to tweets that exhibit indications of suicidal thoughts, feelings, or intentions.

Usage: This dataset can be used for various natural language processing (NLP) and sentiment analysis tasks. It is particularly suitable for training and evaluating machine learning models that can automatically classify tweets as either non-suicidal or potentially suicidal. Researchers, data scientists, and developers can use this dataset to develop systems that can identify and flag concerning content on social media platforms, potentially contributing to early intervention and support for individuals in distress.

Potential Applications: - Suicidal Ideation Detection: The dataset can be used to train models to automatically detect and flag tweets containing potential suicidal content, enabling platforms to take appropriate actions. - Mental Health Support: Insights from this dataset can be used to develop tools that offer mental health resources or interventions to users who express signs of distress. - Sentiment Analysis Research: Researchers can analyze the linguistic patterns and sentiment of both non-suicidal and potentially suicidal tweets to gain insights into the language used by individuals in different emotional states. - Public Health Awareness: The dataset can be used to raise awareness about mental health issues and the importance of responsible social media usage.

[N.B.: Please note that the annotations provided in the "Suicide" column are based on indicators present in the tweet text. However, the dataset does not provide any personal or identifying information about the users who posted the tweets. Researchers and developers should handle this data responsibly and ethically while considering potential user privacy concerns.]

In 2024, it was estimated that around ** percent of men aged 18-25 years seriously considered committing suicide at some point in the past year. This statistic displays the percentage of U.S. men who had serious thoughts of suicide in the past year in 2023 and 2024, by age.

From 2022 to 2024, around ** percent of college and university students who received mental health services in the United States had seriously considered suicide. This statistic shows the percentage of college and university students in the U.S. who received mental health services and had seriously considered attempting suicide from 2010 to 2024. Post-secondary students and mental health Although often an exciting time, transitioning to college or university can present youth with new pressures and stress due to increased responsibilities, freedom, and academic demands within different social surroundings while adjusting to a new environment. This can unfortunately lead to mental health challenges for some students, especially for those living with pre-existing mental health challenges – for example, in 2021, around ********* of college students reported having an anxiety disorder while *********** had depression or another mood disorder. Moreover, nearly *********** of college and university students in the U.S. reported non-suicidal self-harm behaviors and around ******** percent reported having suicidal ideation. Suicide prevention strategies In order to help increase students’ mental health and wellbeing, many campuses offer different types of support, such as peer support groups, awareness campaigns, and professional services. In 2021, ************ of U.S. students reported knowing where they could go for on-campus professional mental health resources. Families and friends of post-secondary students who are struggling can help through maintaining supportive contact, engaging in conversations about mental health struggles and self-care strategies, and seeking out the on-campus resources available.

This dataset provides a snapshot of global suicide rates by country, gender, and year. It offers insights into the prevalence of suicide across different regions and demographics. By analyzing this data, researchers and policymakers can identify trends, potential risk factors, and areas where interventions may be most effective. This information is crucial for developing targeted suicide prevention strategies and promoting mental health awareness.

1. I failed to find any current public datasets while I was thinking about developing a text classifier to identify suicide ideation. I hope this will save time and be helpful to anyone searching for suicide detection datasets.
2. This dataset is a collection of posts from the Reddit online platform. The posts are collected using PRAW. PRAW, an acronym for "Python Reddit API Wrapper", is a Python package that allows for simple access to Reddit's API.
3. Suicidal texts have been collected from the following Reddit online community:
    SuicideWatch
4. Non-suicidal texts have been collected from the following Reddit online communities:
    CasualConversation  BenignExistence and  CongratsLikeImFive
5. There are a total of 15477 records and 3 attributes in this dataset, and the data were collected from June 1, 2023, to November 13, 2023.
6. All posts collected from suicidal texts are labeled as suicidal, while posts collected from non-suicidal texts are labeled as non-suicidal.

Download data on suicides in Massachusetts by demographics and year. This page also includes reporting on military & veteran suicide, and suicides during COVID-19.

Suicide Sentiment Analysis Dataset

Description

This dataset, named Suicide Sentiment Analysis Dataset, is a comprehensive collection of textual data labeled for sentiment analysis related to suicide and depression. It was utilized in the research titled "Enhanced Feature Engineering Approach for Suicidal Risk Identification (EFASRI)" published in the Egyptian Informatics Journal.

The dataset is designed to support the development of machine learning models for identifying suicidal risk levels in social media posts. It contains labeled data that can be used for various natural language processing (NLP) tasks, including sentiment analysis, text classification, and mental health research.

Research Paper

The dataset was used in the following research publication: - Title: Enhanced Feature Engineering Approach for Suicidal Risk Identification (EFASRI) - Authors: Umar Hajam, Syed Tanzeel Rabani, Akib Mohi Ud Din Khanday, Qamar Rayees Khan, Ali Shariq Imran, Zenun Kastrati. - Publication: Egyptian Informatics Journal - Date: April 19, 2023 - Link: Enhanced Feature Engineering Approach for Suicidal Risk Identification (EFASRI)

Contents

The dataset is organized into the following structure:

• Negative_Neutral_Depression (Directory):

  • Depression.csv: Contains textual data labeled as "Depression".
  • Neutral.csv: Contains textual data labeled as "Neutral".
  • Suicidal_tendencies_data.csv: Contains textual data labeled with suicidal tendencies.

• Pos_Neut_Neg.csv (File):

  • Contains textual data labeled with Positive, Neutral, or Negative sentiments.

Usage

This dataset is ideal for: - Training and evaluating models related to suicide risk detection. - Sentiment analysis in social media posts. - Mental health research focusing on identifying signs of depression and suicidal tendencies.

Researchers and developers are encouraged to utilize this dataset to advance the state of technology in mental health and sentiment analysis, contributing to the development of better tools and resources for identifying and mitigating suicidal behavior.

License

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. You are free to use, share, and adapt the data, provided appropriate credit is given.

How to Cite

If you use this dataset in your research, please cite the following paper: - Title: Enhanced Feature Engineering Approach for Suicidal Risk Identification (EFASRI) - Journal: Egyptian Informatics Journal - Year: 2023

Feel free to explore the dataset and contribute to the advancement of mental health technology!

Mental health characteristics and suicidal thoughts, by age group and sex, Canada (excluding territories) and provinces.

In 2024, it was estimated that 5.5 percent of men in the U.S. had serious thoughts of suicide in the past year. This statistic shows the percentage of U.S. men who had serious thoughts of suicide in the past year from 2008 to 2024.

This table contains 126720 series, with data for years 2000 - 2000 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Age group (12 items: Total; 15 years and over;20 to 34 years;20 to 24 years;15 to 19 years ...), Sex (3 items: Both sexes; Females; Males ...), Suicidal thoughts and attempts (5 items: Total; suicidal thoughts and attempts; Suicide; considered in past 12 months; Suicide; attempted in past 12 months; Suicide; never contemplated ...), Characteristics (8 items: Number of persons; Low 95% confidence interval; number of persons; Coefficient of variation for number of persons; High 95% confidence interval; number of persons ...).

This short report uses 2008 to 2010 National Survey on Drug Use and Health (NSDUH) to assess past year serious thoughts of suicide, suicide plans, or suicide attempts among adults aged 18 or older living in 33 metropolitan statistical areas.

In 2024, around 28 percent of adults in the United States with a major depressive episode had serious thoughts of suicide within the preceding year. This statistic shows suicidal thoughts, plans, and attempts among U.S. adults as of 2024, broken down by major depressive episode.

This report compares estimates of suicidality (i.e., serious thoughts of suicide, suicide plans, suicide attempts, and receipt of medical care for a suicide attempt) generated from the 2008-2012 National Survey on Drug Use and Health (NSDUH) with estimates of similar measures acquired from other national data sources: National Comorbidity Survey Replication (NCS-R), the Youth Risk Behavior Survey (YRBS), the National Hospital Discharge Survey (NHDS), and the Nationwide Inpatient Sample (NIS). Results are shown by gender, race/ethnicity, age, and year data collected.

BackgroundIn Europe, men have lower rates of attempted suicide compared to women and at the same time a higher rate of completed suicides, indicating major gender differences in lethality of suicidal behaviour. The aim of this study was to analyse the extent to which these gender differences in lethality can be explained by factors such as choice of more lethal methods or lethality differences within the same suicide method or age. In addition, we explored gender differences in the intentionality of suicide attempts.Methods and FindingsMethods. Design: Epidemiological study using a combination of self-report and official data. Setting: Mental health care services in four European countries: Germany, Hungary, Ireland, and Portugal. Data basis: Completed suicides derived from official statistics for each country (767 acts, 74.4% male) and assessed suicide attempts excluding habitual intentional self-harm (8,175 acts, 43.2% male).Main Outcome Measures and Data Analysis. We collected data on suicidal acts in eight regions of four European countries participating in the EU-funded “OSPI-Europe”-project (www.ospi-europe.com). We calculated method-specific lethality using the number of completed suicides per method * 100 / (number of completed suicides per method + number of attempted suicides per method). We tested gender differences in the distribution of suicidal acts for significance by using the χ2-test for two-by-two tables. We assessed the effect sizes with phi coefficients (φ). We identified predictors of lethality with a binary logistic regression analysis. Poisson regression analysis examined the contribution of choice of methods and method-specific lethality to gender differences in the lethality of suicidal acts.Findings Main ResultsSuicidal acts (fatal and non-fatal) were 3.4 times more lethal in men than in women (lethality 13.91% (regarding 4106 suicidal acts) versus 4.05% (regarding 4836 suicidal acts)), the difference being significant for the methods hanging, jumping, moving objects, sharp objects and poisoning by substances other than drugs. Median age at time of suicidal behaviour (35–44 years) did not differ between males and females. The overall gender difference in lethality of suicidal behaviour was explained by males choosing more lethal suicide methods (odds ratio (OR) = 2.03; 95% CI = 1.65 to 2.50; p < 0.000001) and additionally, but to a lesser degree, by a higher lethality of suicidal acts for males even within the same method (OR = 1.64; 95% CI = 1.32 to 2.02; p = 0.000005). Results of a regression analysis revealed neither age nor country differences were significant predictors for gender differences in the lethality of suicidal acts. The proportion of serious suicide attempts among all non-fatal suicidal acts with known intentionality (NFSAi) was significantly higher in men (57.1%; 1,207 of 2,115 NFSAi) than in women (48.6%; 1,508 of 3,100 NFSAi) (χ2 = 35.74; p < 0.000001).Main limitations of the studyDue to restrictive data security regulations to ensure anonymity in Ireland, specific ages could not be provided because of the relatively low absolute numbers of suicide in the Irish intervention and control region. Therefore, analyses of the interaction between gender and age could only be conducted for three of the four countries. Attempted suicides were assessed for patients presenting to emergency departments or treated in hospitals. An unknown rate of attempted suicides remained undetected. This may have caused an overestimation of the lethality of certain methods. Moreover, the detection of attempted suicides and the registration of completed suicides might have differed across the four countries. Some suicides might be hidden and misclassified as undetermined deaths.ConclusionsMen more often used highly lethal methods in suicidal behaviour, but there was also a higher method-specific lethality which together explained the large gender differences in the lethality of suicidal acts. Gender differences in the lethality of suicidal acts were fairly consistent across all four European countries examined. Males and females did not differ in age at time of suicidal behaviour. Suicide attempts by males were rated as being more serious independent of the method used, with the exceptions of attempted hanging, suggesting gender differences in intentionality associated with suicidal behaviour. These findings contribute to understanding of the spectrum of reasons for gender differences in the lethality of suicidal behaviour and should inform the development of gender specific strategies for suicide prevention.

This report presents estimates for suicidal thoughts, plans, and non-fatal attempts from the 2015 National Survey on Drug Use and Health (NSDUH) for adults aged 18 or older. Estimates are also included of those that had suicidal thoughts, their percentage of illicit drug use, substance use disorders, and major depressive episode. Comparisons are made by sex and by age group. Trends are examined between 2014 and 2014 and in some cases, 2008 and 2015.

In 2025, around 27 percent of college and university students in the United States reported having had non-suicidal self-injurious behaviors in the past year, and two percent reported having attempted suicide. This statistic shows the percentage of postsecondary students with suicidal or self-injurious behavior in the United States in 2025.

stackedBar chart showing Suicidal Ideation vs Completion Rates

This dataset is a manually relabeled subset based on the original Kaggle dataset “Suicide Watch” by nikhileswarkomati.

The original dataset assigns labels purely based on subreddit origin:

• posts from r/SuicideWatch → suicide
• posts from r/teenagers → non-suicide

While this approach is simple and scalable, it introduces systematic label noise. Not all posts in r/SuicideWatch express suicidal intent (e.g. gratitude posts, requests for help for third parties, or general life difficulties), and not all posts in r/teenagers are emotionally neutral or harmless.

Manual Relabeling Approach

To reduce subreddit-based bias and focus on semantic meaning rather than origin, all texts were manually reviewed and relabeled according to the following principles:

• Labels reflect explicit suicidal intent, not emotional tone or attention-seeking behavior.
• If a text contained clearly expressed suicidal intent, it was labeled suicide.
• Ambiguous or emotionally heavy messages without explicit intent were labeled non-suicide, even if they originally came from r/SuicideWatch.
• Subreddit context was intentionally ignored, as a real-world classifier does not have access to it. For example, a message like “I’m tired of all this. This is my last post.” was treated as neutral while it seems pretty dangerous in r/SuicideWatch.

No clinical or psychological expertise is claimed; this dataset is intended for machine learning research, not medical diagnosis.

Additional Data Sources

To increase stylistic diversity and reduce the risk of learning subreddit-specific language patterns, a small number of additional posts were included from:

• r/AskReddit
• r/TrueAskReddit
• r/CasualConversation

This helps ensure that models trained on this dataset learn to distinguish meaning and intent, not subreddit writing style or topic.

Label Verification

After manual annotation, CleanLab was used to review the most uncertain cases and re-evaluate potential labeling errors. This step was applied conservatively as a secondary quality check.

Dataset Statistics

• Total samples: 1,000
• non-suicide: 501
• suicide: 499
• Class balance: ~50/50

Approximately 15–20% of the original “suicide” labels were reassigned to “non-suicide”, reducing subreddit leakage and improving semantic validity.

⚠️ Disclaimer:

This dataset is not intended for clinical use, diagnosis, or real-world decision-making without professional oversight.

This report is the second report under the 2014 NSDUH National First Release Reports. This report presents findings from the 2014 NSDUH on the percentages and numbers of adults aged 18 years old or older in the United States who had serious thoughts of suicide, made a suicide plan, and attempted suicide in the past 12 months. Findings for 2014 are presented for all adults aged 18 or older, young adults aged 18 to 25, adults aged 26 to 49, adults aged 50 or older, and adult males or females aged 18 or older. Trend data for suicidal thoughts and behavior also are presented by comparing estimates in 2014 with estimates in 2008 to 2013. Statistically significant differences are noted among subgroups of adults in 2014 and for differences between estimates in 2014 and those in prior years.

Overall suicide death rate per 100,000 by demographic group, 2024. Notes: Analysis of CDC WONDER underlying cause of death data, 2014 to 2024. Suicide deaths were identified using ICD-10 113 Cause List, Intentional self-harm (U03, X60-X84, Y87.0). Rates are age-adjusted for all demographics except age groups. AIAN = American Indian and Alaska Native. Persons of Hispanic origin may be of any race but are categorized as Hispanic for this analysis; other groups are non-Hispanic. Data were insufficient to allow for analysis of other racial groups.