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.

Number of suicides, suicide rates and median registration delays, by local authority in England and Wales.

There is 1 dataset made up of 3 different sheets Life expectancy contains information about life expectancy for men and for women, happiness score, and fertility rate. Suicides by country contains information about the suicide rate of each country GDP per capita contains the GDP per capita of each country.

All sources come from 2019 data seen below https://apps.who.int/gho/data/view.main.MHSUICIDEASDRv https://www.who.int/data/gho/data/themes/mental-health/suicide-rates World database for life expectancy tables

There're 2 datasets:

1. Life expectancy contains information about life expectancy for men and for women, happiness score and fertility rate.
2. Suicide Rate contains information about the suicide rate and GDP per capita on each country.

India IN: Suicide Mortality Rate: Male data was reported at 17.800 NA in 2016. This records a decrease from the previous number of 18.000 NA for 2015. India IN: Suicide Mortality Rate: Male data is updated yearly, averaging 18.000 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 18.600 NA in 2000 and a record low of 17.700 NA in 2010. India IN: Suicide Mortality Rate: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Health Statistics. Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

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

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

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 project provides comprehensive information on the total number of suicides in Mexico from 1990 to 2024, categorized by sex and state. It includes the main dataset along with a Python script and supporting files that enable users to analyze suicide rates and trends across the country.The dataset follows the official government methodology, using year of registration and state of residence of the deceased as key variables. It includes:Total male and female populationsSuicide counts for males and femalesSuicide rates for each sexData SourcesSuicide Data: Extracted from the INEGI database of registered deathshttps://www.inegi.org.mx/programas/edr/#microdatosPopulation Data: Derived from Mexican government population projections for 2020–2070https://datos.gob.mx/dataset/proyecciones-de-poblacion/resource/de522924-f4d8-4523-a6fd-6b2efe73f3afIncluded Filesscript.py – Python script to generate choropleth maps of suicide rates by state for a selected yearrequirements.txt – Required Python packages to run the scriptmexico.json – GeoJSON file containing administrative boundaries of Mexico by stateSample Chart (2024) – Example visualization featuring suicide rates for 2024This project can be used by researchers, public health professionals, policymakers, journalists, and students interested in understanding suicide trends in Mexico. It allows users to explore long-term and state-level patterns, compare differences between males and females, generate spatial visualizations, and incorporate the data into broader statistical, geographic, or public health analyses.

For a summary of the case study, please go to "Portfolio Project".

Context

This data analysis was meant to show that men have their own issues in society that are being ignored. The mental health has been declining especially for men. This decline worldwide maybe due to a multitude of other variables that may correlate such as: internet usage/social media usage, social belonging, work hours, dating apps, and physical health. This data analysis was meant to show that men have their own issues in society that are being ignored. This decline worldwide maybe due to a multitude of other variables that may correlate such as: internet usage/social media usage, social belonging, work hours, dating apps, and physical health. These variables may require a separate dataset going into more detail about them.

A space dedicated just for men and another just for women to speak about their problems with help and constructive criticism for growth and for social belonging maybe required to improve the mental health of society (among other variables). This does not mean that the struggles of women are nonexistent. There are already a multitude of datasets and articles dedicated to some of the possible struggles of women from MSNBC, CNN, NBC, BBC, Netflix movies, and even popular secular music like recent songs WAP from Megan Thee Stallion, God is a Women by Arianna Grande, etc. This dataset's objective was not made to continue to light a flame between the already hostile relationships that modern men and women have with each other. Awareness without bias is the goal.

For the results, please read the portfolio project and leave comments.

Content

Where the data were obtained:

1. The first excel file was obtained from https://data.world/vizzup/mental-health-depression-disorder-data/workspace/file?filename=Mental+health+Depression+disorder+Data.xlsx

2. The second excel file was obtained from https://ourworldindata.org/grapher/male-vs-female-suicide

3. The third excel file was obtained from https://ourworldindata.org/suicide

4. The fourth excel file was obtained from https://ourworldindata.org/drug-use

Inspiration

I want to be the best data analyst ever, so criticism (regardless of the harshness), it will be greatly appreciated. What would you have added/improved on? Was it easy to understand? What else do you want me to make a dataset on?

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

This dataset shows the Canadian Armed Forces (CAF) rate for suicide per 100,000 for Regular Force males. As the number of events was less than 20 in most years, rates were not calculated annually as these would not have been statistically reliable. Regular Force female rates were not calculated because female suicides were uncommon. This dataset is taken from the yearly Report on Suicide Mortality in the Canadian Armed Forces released on the Canada.ca platform at the homepage link provided down below.

This dataset contains annual suicide rates from 2000 to 2019 for various countries and regions around the world. The data was collected from the World Health Organization (WHO) Mortality Database, which provides information on deaths and mortality rates from various causes, including suicide. The dataset includes information on the country, location, year, sex, and suicide rates (with upper and lower bounds) for each year.

Variables:

ParentLocation: The name of the parent location, such as a region or subregion. Location: The name of the location, such as a country or territory. Period: The year the suicide rate was recorded. Sex: The gender of the individual (male or female). FactValueNumeric: The age-standardized suicide rate (per 100,000 population) for the given sex, age, and year. FactValueNumericLow: The lower bound of the confidence interval for the suicide rate. FactValueNumericHigh: The upper bound of the confidence interval for the suicide rate.

Potential uses:

This dataset can be used to explore patterns and trends in suicide rates over time and across different regions of the world. Researchers and policymakers can use this data to identify risk factors and develop interventions to prevent suicides. The dataset can also be used to investigate the impact of economic, social, and cultural factors on suicide rates.

Caveats:

It is important to note that suicide is a sensitive and complex issue, and the data in this dataset may be subject to reporting biases, cultural differences in suicide rates, and other limitations. Additionally, the dataset does not include information on the causes or circumstances surrounding the suicides. Therefore, any analyses based on this dataset should be interpreted with caution and with an understanding of the limitations of the data.

This group of datasets describe the suicides in Scotland for the period 1982-2009. There are 4 separate datasets: All Suicides/Male Suicides/Female Suicides/All Suicide Rate (expressed per 100,000 people). The data is broken down into Local Authority Areas making it easier to investigate any spatial disparity in the suicide figures. A couple of points are worth noting are that it is unclear if the suicide data shows all suicides or just those of Adults. A recent Scottish Government report(http://www.scotland.gov.uk/Publications/2007/03/01145422/20) used deaths of people over 15 years old. Differences in the rates between this data and the results presented in the Scottish Government report may also be due to different population datasets being used. Suicide data sources form the Scottish Public Health Observatory (http://www.scotpho.org.uk/home/Healthwell-beinganddisease/suicide/suicide_data/suicide_la.asp) and the population data used to calculate the rates was sourced from ShareGeo Open (http://hdl.handle.net/10672/95) which uses mid-year estimates downloaded from Nomis (www.nomisweb.co.uk/. Datasets were joined to Local Authority (district, unitary authority and borough) boundaries downloaded from Ordnance Survey OpenData Boundary Line dataset. All spatial analysis was carried out in ArcGIS. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2011-01-13 and migrated to Edinburgh DataShare on 2017-02-21.

This dataset contains data about obesity, suicides and unemployment segregated by Country. The sources of data are wikipedia tables as updated on 11/04/2022. More information can be found in project's github: https://github.com/martinsanc/wikipedia_scraper

Países (List of countries by population (United Nations) - Wikipedia)

Country

UN continental region

UN statistical subregion

Population 1 July 2018

Population 1 July 2019

Change

Desempleo (List of countries by unemployment rate - Wikipedia)

Unemployment Rate

Sourcedate of information

Suicidios (List of countries by suicide rate - Wikipedia)

All

Male

Female

Tasa de obesidad por país (List of countries by suicide rate - Wikipedia)

Rank

Obesity rate

In 2022, there were more than ** thousand female deaths due to suicides in India, while the incidents were more than *** thousand for males. Some of the causes for suicides in the country were due to professional problems, abuse, violence, family problems, financial loss, sense of isolation and mental disorders.

This short report uses data on drug-related emergency department (ED) visits from the Drug Abuse Warning Network (DAWN) to examine the trends and characteristics of ED visits involving drug-related suicide attempts among ED patients aged 45-64 in 2011. The report discusses the patterns for male and female patients, the drugs most frequently involved in the suicide attempt-related ED visits, and the outcome of the visits. Findings from 2011 are compared with 2005 data. The report notes that current suicide prevention public health efforts are directed at primarily young people and the elderly, but that the findings of this analysis--the increase in drug-related suicide attempts among adults ages 45-64--underscore the importance of understanding risk factors and developing appropriate prevention strategies for this age group.

Abstract Objective: to perform spatiotemporal analysis of suicide mortality in the elderly in Brazil. Methods: a mixed ecological study was carried out in which deaths from suicide among the elderly were analyzed using data from the Mortality Information System (MIS) and socio-demographic variables, from 2000 to 2014, with a trend analysis of this period. Univariate and bivariate spatial analysis was performed using the Moran Global and Moran Map index to evaluate the intensity and significance of spatial clusters. Results: there were 19,806 deaths due to suicide among the elderly in Brazil between 2000 and 2014. The ratio of male and female mortality rates was 4:1, with increasing trends for both genders (R2>0.8), but with greater intensity among men (p=0.0293). There was a moderate autocorrelation for men (I>0.40), with clusters forming for both genders in the south of Brazil. Bivariate analysis showed the formation of clusters in the southern region with the Human Development Index and aging variables and in the north and northeast regions based on dependence and illiteracy ratio. Conclusions: mortality due to suicide among the elderly has a tendency to increase and is unequally distributed in Brazil.

Over *** thousand deaths due to suicides were recorded in India in 2022. Furthermore, majority of suicides were reported in the state of Tamil Nadu, followed by Rajasthan. The number of suicides that year had increased from the previous year. Some of the causes for suicides in the country were due to professional problems, abuse, violence, family problems, financial loss, sense of isolation and mental disorders. Depressive disorders and suicide As of 2015, over ****** million people worldwide suffered from some kind of depressive disorder. Furthermore, over ** percent of the total population in India suffer from different forms of mental disorders as of 2017. There exists a positive correlation between the number of suicide mortality rates and people with select mental disorders as opposed to those without. Risk factors for mental disorders Every ******* person in India suffers from some form of mental disorder. Today, depressive disorders are regarded as the leading contributor not only to disease burden and morbidity worldwide, but even suicide if not addressed. In 2022, the leading cause for suicide deaths in India was due to family problems. The second leading cause was due to illness. Some of the risk factors, relative to developing mental disorders including depressive and anxiety disorders, include bullying victimization, poverty, unemployment, childhood sexual abuse and intimate partner violence.

Abstract Purpose To assess the clinical characteristics of subjects with gender dysphoria (GD). Method A cross-sectional study of adults with GD. Symptoms of anxiety and depression were measured using the Hospital Anxiety and Depression Scale (HADS). Sociodemographic data, clinical data and life habits were recorded. Results Total of 44 subjects participated in the study: 36 (82%) trans women and 8 (18%) trans men. Forty-three (98%) of the GD patients had anxiety (36 [100%] trans women and 7 [87.5%] trans men), and 36 (82%) had depression (29 [80.5%] trans women and 7 [87.5%] trans men). Suicide had been attempted by 32 (73%) subjects. The rates of depression were lower among the subjects living with partners, parents, or other people than among those living alone (p = 0.03), and it was also lower among the subjects who were married compared to those who were dating or single (p = 0.03). Conclusion Improving the relationship status may reduce the prevalence of depressive symptoms in GD patients. There was a high rate of attempted suicide in this sample.

Linear regression estimation results for suicidal rate among urban men and urban women.

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.