Impact of Social Media on Suicide Rates: Produced Results

Overview

This dataset explores the impact of social media usage on suicide rates, presenting an analysis based on social media platform data and WHO suicide rate statistics. It is an insightful resource for researchers, data scientists, and analysts looking to understand the correlation between increased social media activity and suicide rates across different regions and demographics.

Content

The dataset includes the following key sources:

WHO Suicide Rate Data (SDGSUICIDE): Retrieved from WHO data export, which tracks global suicide rates. Social Media Usage Data: Information from major social media platforms, sourced from Kaggle, supplemented with data from:

Facebook: Statista

Twitter: Twitter Investor Relations

Instagram: Facebook Investor Relations

Acknowledgements

We would like to acknowledge:

World Health Organization (WHO): For providing global suicide rate data, accessible under their data policy (WHO Data Policy). Kaggle Dataset Contributors: For social media usage data that played a crucial role in the analysis.

Usage

This dataset is useful for studying the potential social factors contributing to suicide rates, especially the role of social media. Analysts can explore correlations using time-series analysis, regression models, or other statistical tools to derive meaningful insights. Please ensure compliance with the Creative Commons Attribution Non-Commercial Share Alike 4.0 International License (CC BY-NC-SA 4.0).

Data Files

Impact-of-social-media-on-suicide-rates-results-1.1.0.zip (90.9 kB) Contains processed results and supplementary data.

Citations

If you use this dataset in your work, please cite:

Martin Winkler. (2021). Impact of social media on suicide rates: produced results (1.1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4701587 https://zenodo.org/records/4701587

License

This dataset is released under the Creative Commons Attribution Non-Commercial Share Alike 4.0 International (CC BY-NC-SA 4.0) license. You are free to share and adapt the material, provided proper attribution is given, it's not used for commercial purposes, and any derivatives are distributed under the same license.

Columns

Year: The year of the recorded data. Sex: Demographic indicator (e.g., male, female). Suicide Rate % Change Since 2010: Percentage change in suicide rates compared to the year 2010. Twitter User Count % Change Since 2010: Percentage change in Twitter user counts compared to the year 2010. Facebook User Count % Change Since 2010: Percentage change in Facebook user counts compared to the year 2010.

Data Bins

The dataset includes categorized data ranges, allowing for analysis of trends within specified intervals. For example, ranges for suicide rates, Twitter user counts, and Facebook user counts are represented in bins for better granularity.

Count Summary

The dataset summarizes counts for various intervals, enabling researchers to identify trends and patterns over time, highlighting periods of significant change or stability in both suicide rates and social media usage.

Use Cases

This dataset can be used for:

Statistical analysis to understand correlations between social media usage and mental health outcomes. Academic research focused on public health, psychology, or sociology. Policy-making discussions aimed at addressing mental health concerns linked to social media.

Cautions

The dataset contains sensitive information regarding suicide rates. Users should handle this data with care and sensitivity, considering ethical implications when presenting findings.

Data from Heuer (1979) on suicide rates in West Germany classified by age, sex, and method of suicide.
A data frame with 306 observations and 6 variables.

Introduction Suicide is still one of the world's most important public health issues, with the World Health Organization (WHO) claiming that over 700,000 people die by suicide annually. Suicide is one of the main causes of death, with far-reaching consequences for people, families, and society. Understanding the global patterns and trends in suicide rates is critical for creating effective prevention methods and providing the required support to at-risk individuals. The purpose of this report is to visualize global data on suicides using the WHO dataset (who_suicide_statistics.csv). This dataset has statistics on the number of suicides in various countries, years, age categories, and sexes. By analyzing this data, it will guide us to learn about demographic and temporal patterns of suicide, show high-risk groups, and highlight regions facing significant challenges. The visualizations will employ various techniques such as graphs, charts, and maps to effectively convey the information and guide the viewer through the findings. Through these visualizations and insights, I suggested key points and recommendations needed to minimize suicide incidents in future. Description of the Dataset The dataset (who_suicide_statistics.csv) has extensive data on global suicide statistics collected by the World Health Organization. This dataset is an invaluable resource for analyzing the patterns and trends in suicide rates across countries, years, age groups, and genders. Below is a detailed description of the columns in the dataset and the kind of information each one provides. Columns in the Dataset • country: Description: The name of the country where the data was collected. Type: Categorical Example Values: 'United States', 'Japan', 'Germany' • year: Description: The year the data was recorded. Type: Numerical Example Values: 2000, 2005, 2010 - age: Description: The age group of the individuals whose suicide data is recorded. Type: Categorical Example Values: '15-24', '25-34', '35-44', '45-54', '55-64', '65-74', '75+' • sex: Description: The sex of the individuals whose suicide data is recorded. Type: Categorical Example Values: 'male', 'female' • suicide_no: Description: The number of suicide cases recorded for the specified country, year, age and sex. Type: Numerical Example Values: 15, 42, 108 • population: Description: The population of the specified age group and sex in the country for that year. Type: Numerical Example Values: 345633, 785042, 3356435 Additional Information • Suicide Rate Calculation: Using the suicide_no and population columns, we can calculate the suicide rate per 100,000 population, which normalizes the data and allows for fair comparisons across different countries and demographic groups. Formula: suicides_rate = (suicide_no / population) * 100000

Age-adjustment mortality rates are rates of deaths that are computed using a statistical method to create a metric based on the true death rate so that it can be compared over time for a single population (i.e. comparing 2006-2008 to 2010-2012), as well as enable comparisons across different populations with possibly different age distributions in their populations (i.e. comparing Hispanic residents to Asian residents).
Age adjustment methods applied to Montgomery County rates are consistent with US Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS) as well as Maryland Department of Health and Mental Hygiene’s Vital Statistics Administration (DHMH VSA). PHS Planning and Epidemiology receives an annual data file of Montgomery County resident deaths registered with Maryland Department of Health and Mental Hygiene’s Vital Statistics Administration (DHMH VSA).
Using SAS analytic software, MCDHHS standardizes, aggregates, and calculates age-adjusted rates for each of the leading causes of death category consistent with state and national methods and by subgroups based on age, gender, race, and ethnicity combinations. Data are released in compliance with Data Use Agreements between DHMH VSA and MCDHHS. This dataset will be updated Annually.

This table contains the number of victims of suicide arranged by marital status, method, motives, age and sex. They represent the number deaths by suicide in the resident population of the Netherlands.

The figures in this table are equal to the suicide figures in the causes of death statistics, because they are based on the same files. The causes of death statistics do not contain information on the motive of suicide. For the years 1950-1995, this information is obtained from a historical data file on suicides. For the years 1996-now the motive is tasks from the external causes of death. Before the 9th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD), i.e. for the years 1950-1978, it was not possible to code “jumping in front of train/metro”. For these years 1950-1978 “jumping in front of train/metro” has been left empty, and it has been counted in the group “other method”.

Relative figures have been calculated per 100000 of the corresponding population group. The figures are calculated based on the average population of the corresponding year.

Data available from: 1950

Status of the figures: The figures up to and including 2022 are final.

Changes as of January 25th 2024: The provisional figures for 2022 have been made final unchanged.

Changes as of August 29th 2023: The provisional figures for 2022 have been added. Some final figures of 2021 were incorrect and have been revised. A small adjustment was made in the number of deceased women from 60 to 69 years.

When will new figures be published: In the third quarter of 2024 the provisional figures for 2023 will be published.

Number of suicides and suicide rates by sex and age in England and Wales. Includes information on conclusion type, the proportion of suicides by method, and the median registration delay.

Crude death rate from suicide and intentional self-harm per 100 000 people, by age group. Suicide registration methods vary between countries and over time. Figures do not include deaths from events of undetermined intent (part of which should be considered as suicides) and attempted suicides which did not result in death.

Methods of suicide/self-inflicted injuries for Santa Clara County residents. The methods of injury for suicide deaths are provided for the total county population and by race/ethnicity. Data for emergency department utilization and hospital discharges are summarized only for total county population. Data are presented for pooled years combined. Missing data are not included in the analysis. Source: Santa Clara County Public Health Department, VRBIS, 2007-2016. Data as of 05/26/2017; Office of Statewide Planning and Development, 2007-2014 Emergency Department Data; Office of Statewide Planning and Development, 2007-2014 Patient Discharge Data.METADATA:Notes (String): Lists table title, notes and sourceYear (String): Year of eventData element (String): Lists data represents deaths, hospital discharges or emergency department visitsCategory (String): Lists the category representing the data. Suicide death data are presented as: Santa Clara County is for total population, sex: Male and Female, and race/ethnicity: African American, Asian/Pacific Islander, Latino and White (non-Hispanic White only). Suicide attempt/ideation data are presented as: Santa Clara County is for total population.Means of injury (String): Methods are categorized as: Poisoning, Suffocation, Firearms, Fall, Cut/pierce, Fire/flame and other.Percentage (Numeric): Percentage

This dataset includes the raw data from Google Trends, averaged data, the construct of (S)ARIMA models, and cross-correlation coefficients. Three sets of data are due to sensitivity analyses performed in 3 different time spans. The monthly rates of suicide by 3 differents means in the USA are also included.

The study elucidated 3 Google search terms whose search volume trends precede trends in means-specific suicide rate in the United States.

State-wise Suicides by Profession in India

Investigating National Level Trends and Patterns

By Rajanand Ilangovan [source]

About this dataset

This dataset contains data on suicides in India by state, year, profession and gender. Through this dataset, we can gain an understanding of the factors that influence suicide rates across different states, professions and genders. By examining this data we can better understand how to reduce these tragedies in India which are of great concern to citizens, families and the government alike. The columns include the State in India where the suicides occurred; Year in which the suicides occurred; Type_code of the profession of the person who committed suicide; Gender of the person who committed suicide; Age_group of such person; and Total number of suicides for a given State-Year-Typecode-Type-Gender-Agegroup combination. With this insightful data set at our disposal, we can gather valuable insights into why certain types people are more likely to take their own lives than others and look for solutions which would have meaningful implications for society at large

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How to use the dataset

This dataset contains information about the number of suicides in India by state, year, type of profession, gender, and age group. It is an important resource for understanding the trends and patterns in suicides in India. This guide will explain how to use this dataset to gain insights into suicide rates across India.

Exploring the Data

The first step to exploring this data is to examine its structure. There are 8 columns that contain information about each suicide: State (the Indian state where the suicide occurred), Year (the year of occurrence), Type_code (the code for the type of profession or activity engaged in at time of death), Gender (male or female), Age_group (groups based on age-range), Total (total number of suicides for given state/year/type_code/type/gender/age group). In addition, there are other useful descriptive stats such as aggregate totals by year and aggregate totals by state as well as null values indicating missing data points that should be accounted for during analysis.

Analyzing Trends

Once you have a good understanding of the data structure, you can begin analyzing it for patterns and trends. You can look at overall trends across all states or focus on individual states to see if certain decades witness higher suicide rates than others due to specific socioeconomic factors within those states. Similarly, you may identify distinct patterns when examining activity related causes across genders or age groups both generally and within individual states – e.g., self-immolation witnessed significantly more amongst females than males within a given decade etc.. Alternatively you could find out what types occupations had higher incidences during certain years thus ruling out otherwise unlikely ways people chose ‘suicide’!

Finally it may also be useful window shop; use this data set as research material before further framing hypotheses related too changes over time i historical events that directly caused shifts in societal norms like wars / pandemics etc.. And then corroborate results against timelines ascertained through secondary sources such newspapers / anecdotal reports or primary sources like census records summaries published by official agencies etc.. As a index towards which other activities were attempted within scope!

Overall these analyses can help policy makers understand better how best resources can be allocated while developing interventions aimed at reducing suicidal tendencies amongst different demographic segments including males & females , adolescents & elderly people respectively!

Research Ideas

• Analyzing trends in suicides across different states in India over time to identify regional disparities and support the implementation of targeted policies and interventions.
• Mapping out the suicide hotspots across age groups, genders, and profession types to better target prevention efforts in those areas.
• Examining differences by profession type among populations with higher suicide rates in order to suggest preventative measures or resources tailored specifically for such populations

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: Suicides_in_India.csv | Column name | Description ...

This dataset provides model-based provisional estimates of the weekly numbers of drug overdose, suicide, and transportation-related deaths using “nowcasting” methods to account for the normal lag between the occurrence and reporting of these deaths. Estimates less than 10 are suppressed. These early model-based provisional estimates were generated using a multi-stage hierarchical Bayesian modeling process to generate smoothed estimates of the weekly numbers of death, accounting for reporting lags. These estimates are based on several assumptions about how the reporting lags have changed in recent months across different jurisdictions, and the resulting estimates differ from other sources of provisional mortality data. For now, these estimates should be considered highly uncertain until further evaluations can be done to determine the validity of these assumptions about timeliness. The true patterns in reporting lags will not be known until data are finalized, typically 11–12 months after the end of the calendar year. Importantly, these estimates are not a replacement for monthly provisional drug overdose death counts, or quarterly provisional mortality estimates. For more detail about the nowcasting methods and models, see: Rossen LM, Hedegaard H, Warner M, Ahmad FB, Sutton PD. Early provisional estimates of drug overdose, suicide, and transportation-related deaths: Nowcasting methods to account for reporting lags. Vital Statistics Rapid Release; no 11. Hyattsville, MD: National Center for Health Statistics. February 2021. DOI: https://doi.org/10.15620/ cdc:101132

ABSTRACT Objective The aim of this study was to use a wavelet technique to determine whether the number of suicides is similar between developed and emerging countries. Methods Annual data were obtained from World Health Organization (WHO) reports from 1986 to 2015. Discrete nondecimated wavelet transform was used for the analysis, and the Daubechies wavelet function was applied with five-level decomposition. Regarding clustering, energy (variance) was used to analyze the clusters and visualize the clustering process. We constructed a dendrogram using the Mahalanobis distance. The number of groups was set using a specific function in the R program. Results The cluster analysis verified the formation of four groups as follows: Japan, the United States and Brazil were distinct and isolated groups, and other countries (Austria, Belgium, Chile, Israel, Mexico, Italy and the Netherlands) constituted a single group. Conclusion The methods utilized in this paper enabled a detailed verification of countries with similar behaviors despite very distinct socioeconomic, geographic and climate characteristics.

Methods for suicide-related behaviors and deaths by suicide.

Number of suicides and suicide rates broken down by sex, age, month, and method in England and Wales, that occurred between 2013 and 2021.

Dataset replaced by: http://data.europa.eu/euodp/data/dataset/CAJrcG2qBzdgHFsUWHFw

This indicator is defined as the crude death rate from suicide and intentional self-harm per 100 000 people, by age group. Figures should be interpreted with care as suicide registration methods vary between countries and over time. Moreover, the figures do not include deaths from events of undetermined intent (part of which should be considered as suicides) and attempted suicides which did not result in death.

Fire Incident data includes all fire incident responses. This includes emergency medical services (EMS) calls, fires, rescue incidents, and all other services handled by the Fire Department.

The source of this data is the City of Cincinnati's computer aided dispatch (CAD) database.

This data is updated daily.

DISCLAIMER: In compliance with privacy laws, all Public Safety datasets are anonymized and appropriately redacted prior to publication on the City of Cincinnati’s Open Data Portal. This means that for all public safety datasets: (1) the last two digits of all addresses have been replaced with “XX,” and in cases where there is a single digit street address, the entire address number is replaced with "X"; and (2) Latitude and Longitude have been randomly skewed to represent values within the same block area (but not the exact location) of the incident.

This dataset provides global suicide mortality rates (per 100,000 population) by country from 2000 to 2021, sourced from the World Bank. It includes 200+ countries with annual data, cleaned to remove missing values. Columns: Country Name, Country Code, Year, Suicide Rate. Ideal for analyzing trends in mental health, public health policy, and socio-economic correlations. Please use this sensitive data ethically and responsibly.

Suicide rates per 100,000 person years (October 1, 2007- December 31, 2018).

This dataset contains individual details who committed suicide in Bangladesh during the Pandemic between February, 2020 to November, 2020. This dataset includes details of every individuals who committed suicide like personal details, family & social life, profession, financial condition, methods of committing suicide, location and weather info. The dataset is freely available. The major fields included in this dataset are: age group, age, gender, profession group, reason, method, suicide date & time, addiction status, mental status, economic condition, marital status, family details, academic qualification, weather. Apart from the above data this dataset also contains a CSV file of a Bengali wordcloud built on social media posts of the suicide victims.

The access to the dataset files is kept restricted. Fill up the below form (link in the References section) to request the data.

This dataset describes drug poisoning deaths at the U.S. and state level by selected demographic characteristics, and includes age-adjusted death rates for drug poisoning. Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent). Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2017 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Drug poisoning death rates may be underestimated in those instances. REFERENCES 1. National Center for Health Statistics. National Vital Statistics System: Mortality data. Available from: http://www.cdc.gov/nchs/deaths.htm. CDC. CDC Wonder: Underlying cause of death 1999–2016. Available from: http://wonder.cdc.gov/wonder/help/ucd.html.