As of February 2025, 50.2 percent of social media users in the United States were women, and 49.8 percent of users were men. In 2024, there were an estimated 304 million social media users in the country.

As of February 2025, X/Twitter was the social network with the highest share of male users, who accounted for 63.7 percent of global users. Overall, social media platforms were had more male users than female users.

Regional use of social media has a significant effect on the male and female social media statistics.

Here are a few use cases for this project:

Use Case 1: Gender-Based Retail Analytics By analyzing customer demographics in retail stores, the "man vrouw dataset 1" can help retailers understand the gender distribution of their shoppers, empowering them to make informed decisions on store layout, marketing strategies, and product placements.

Use Case 2: Crowd Monitoring and Event Management This model can help enhance safety and optimize visitor experience at crowded events, such as concerts or festivals, by identifying the gender distribution of attendees, enabling promoters to customize services, restrooms allocation, and security measures accordingly.

Use Case 3: Digital Advertising and Marketing Using the "man vrouw dataset 1" model, businesses can better target their digital advertisements by understanding the key demographic visiting specific websites or engaging with specific content, allowing for tailored ad campaigns designed to target male or female audiences.

Use Case 4: Smart Surveillance and Security Systems The model can be used in surveillance and security systems to help identify and track people by their HU classes (man or vrouw) in premises like airports or corporate buildings, allowing security teams to analyze patterns and prevent potential threats.

Use Case 5: Social Media Image Analysis The "man vrouw dataset 1" model can be used to analyze the gender composition of social media images, providing insights into trends, preferences, and behaviors of different gender groups on social platforms. This information can then be used for targeted marketing or social research purposes.

This database is comprised of 951 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 509 males (54%) and 442 females (46%). Their ages ranged from 12 to 16 years (M = 13.69, SD = 0.72). Seven participants did not report their age. The majority were born in Australia (N = 849, 89%). The next most common countries of birth were China (N = 24, 2.5%), the UK (N = 23, 2.4%), and the USA (N = 9, 0.9%). Data were drawn from students at five Australian independent secondary schools. The data contains item responses for the Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. The Social media question asked about frequency of use with the question “How often do you use social media?”. The response options ranged from constantly to once a week or less. Items measuring Fear of Missing Out were included and incorporated the following five questions based on the APS Stress and Wellbeing in Australia Survey (APS, 2015). These were “When I have a good time it is important for me to share the details online; I am afraid that I will miss out on something if I don’t stay connected to my online social networks; I feel worried and uncomfortable when I can’t access my social media accounts; I find it difficult to relax or sleep after spending time on social networking sites; I feel my brain burnout with the constant connectivity of social media. Internal consistency for this measure was α = .81. Self compassion was measured using the 12-item short-form of the Self-Compassion Scale (SCS-SF; Raes et al., 2011). The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels. References: Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4 Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. https://doi.org/10.1002/cpp.702 Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. https://doi.org/10.1016/S0005-7967(98)00034-5

As of January 2024, Instagram was slightly more popular with men than women, with men accounting for 50.6 percent of the platform’s global users. Additionally, the social media app was most popular amongst younger audiences, with almost 32 percent of users aged between 18 and 24 years.

              Instagram’s Global Audience

              As of January 2024, Instagram was the fourth most popular social media platform globally, reaching two billion monthly active users (MAU). This number is projected to keep growing with no signs of slowing down, which is not a surprise as the global online social penetration rate across all regions is constantly increasing.
              As of January 2024, the country with the largest Instagram audience was India with 362.9 million users, followed by the United States with 169.7 million users.

              Who is winning over the generations?

              Even though Instagram’s audience is almost twice the size of TikTok’s on a global scale, TikTok has shown itself to be a fierce competitor, particularly amongst younger audiences. TikTok was the most downloaded mobile app globally in 2022, generating 672 million downloads. As of 2022, Generation Z in the United States spent more time on TikTok than on Instagram monthly.

Gender statistics on the numbers of women and men in key decision-making positions across a number of different life domains. The domains covered include: politics; public administration; judiciary; business and finance; social partners and NGOs; environment and climate change; and media.

Data on decision-making are collected for 35 European countries - the 28 EU Member States, 4 candidate countries (Montenegro, the former Yugoslav Republic of Macedonia, Serbia and Turkey) and the remaining EEA countries (Iceland, Liechtenstein and Norway).

Figures are available at international, European, national, regional and local level. Most data are updated annually, but some key data are updated more frequently. In particular, data on national and European politics are updated quarterly, and data on large companies biannually, in order to ensure that the information is always right up to date.

The results of which gender uses which platforms are in.

The act of identifying interest in scholarly articles and research papers has long been difficult to quantify, let alone gather. In the past few years, altmetric data has allowed both researchers and the general public to access a wealth of information that was once difficult to collect. The ability to analyze public interest on scholarly articles and research papers allows for the writers themselves to identify general interest in a quantifiable manner. With such a large wealth of accessible information, general trends in regards to viewership can be extrapolated. Through social media, research articles are discussed, and their popularity is recorded into Altmetric’s database. A major disparity plaguing most STEM fields currently is the lack of women in comparison to men in the STEM workforce. Thus, this study attempts to identify what scientific fields most interest each gender. To accomplish this, names and subjects were pulled from altmetric data. The names were input into a script to identify the gender of each name. The articles that a person has commented on has that person’s name associated with its related scientific fields. The resulting data was combined and placed into various graphs to clearly visualize the disparity between different subjects and views by gender. The information was then analyzed. It was discovered that in terms of social media, more females viewed scholarly articles compared to men in most fields. However, it was found that papers relating to social sciences were viewed by more females compared to articles relating to material sciences, which garnered more male viewers.

This site is part of a network of digital infrastructure built by Code for Africa (CfA) as a free open source software for use by human rights defending organisations. Reuse it to empower your own communities. CfA is Africa's largest non-profit civic technology and open data catalyst, with labs across the continent. CfA content on this site is released under a Creative Commons 4.0 International License. Refer to our attributions page for attributions of other work on the site.

As of April 2024, around 16.5 percent of global active Instagram users were men between the ages of 18 and 24 years. More than half of the global Instagram population worldwide was aged 34 years or younger.

              Teens and social media

              As one of the biggest social networks worldwide, Instagram is especially popular with teenagers. As of fall 2020, the photo-sharing app ranked third in terms of preferred social network among teenagers in the United States, second to Snapchat and TikTok. Instagram was one of the most influential advertising channels among female Gen Z users when making purchasing decisions. Teens report feeling more confident, popular, and better about themselves when using social media, and less lonely, depressed and anxious.
              Social media can have negative effects on teens, which is also much more pronounced on those with low emotional well-being. It was found that 35 percent of teenagers with low social-emotional well-being reported to have experienced cyber bullying when using social media, while in comparison only five percent of teenagers with high social-emotional well-being stated the same. As such, social media can have a big impact on already fragile states of mind.

Gen Z and Millennials are the biggest social media users of all age groups.

Facebook is one of the most popular social networks in the United States and as of January 2025, **** percent of U.S. Facebook users were women. Facebook usage in the United StatesThanks to its wide reach and vast range of products including Facebook Messenger, Instagram and WhatsApp, many internet users would find it hard to imagine an online experience without the company that arguably made social media mainstream. In 2021, ** percent of the U.S. population were aware of Facebook, an all-time high and still improving on years of consistently high ranking in this area. In May 2024, Facebook had over *** million unique visitors from the United States, making it the seventh most popular multi-platform web property in the United States. Facebook usage concernsDespite near universal Facebook awareness and wide-ranking adoption, many consumers are wary of the social network’s influence on their digital experience and life. In 2018, the company was plagued by scandals, ranging from being a tool in the alleged foreign influence of the U.S. elections in 2016, to being utilized in spreading misinformation by bad actors due to lax content policies, to the mishandling of user data. During a March 2018 survey, ** percent of internet users in the United States stated that large digital platforms such as Facebook (and also Google and Twitter) should be regulated.

The results might surprise you when looking at internet users that are active on social media in each country.

Coding category:

Q1_Gender
1. Male 2. Female

Q2_Living_Area
1. Urban 2. Rural

Q3_Maritial_Status
1. No 2. Yes

SMAQ1 – SMAQ10
1. Never 2. Rarely 3. Sometimes 4. Often 5. Always

SMA_Scale_value

less than 20 to more than 40.

SMA_Scale (Class_Lebel) 1. Low addiction: Total score equal & less than 20 2. Moderate addiction: Total score in between 20 and 40 3. High addiction: Total score equal & greater than 40

This Dataset analyzed Social Media Addiction data from Daffodil International University (DIU) to classify their levels of Addiction into five categories: 1 (Never), 2 (Rarely), 3 (Sometimes), 4 (Often), and 5 (Always). The dataset used was included information on 1,030 participants from various Departments of DIU. The dataset contained 10 main attributes, comprising ten questions each for Social Media Addiction. The target SMA_Scale (Class_Lebel) was categorized as 1 (Low Addiction), 2 (Moderate Addiction), 3 (High Addiction). The distribution of instances for Class_Lebel was 728 (Low Addicted), 259 ((Moderate Addicted), 42 (High Addicted).

56.8% of the world’s total population is active on social media.

Ethical Clearance: N/A The research title being the Reputation Management of Social Media: The case study of emerging Hip Hop artists. I am embarking on a pragmatism research philosophy, with the research question as the most important determinant. My research question for this study is what are the textual representations of femininity in Instagram posts, of South African (SA) Hip hop artists? I think it is important to research on gendered discourses and practices because inequality still exists between male and female hip hop artists, as well as over-sexualization of women based on their images on social media. The study is qualitative and therefore will follow a conceptual framework to identify themes and patterns. I am hoping to investigate human experiences through the social constructs of gender and sexuality on social media, how the masculinity and femininity discourse continues to influence society in both positive and negative ways. These social media stereotype images and views are shaping sexual behavior of youth. It is important to interpret these text at a much deeper level to document and make sense of what is being communicated digitally in order to understand current gendered culture.

This database is comprised of 603 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 208 males (34%) and 395 females (66%). Their ages ranged from 12 to 15 years. Their age in years at baseline is provided. The majority were born in Australia. Data were drawn from students at two Australian independent secondary schools. The data contains total responses for the following scales: The Intolerance of Uncertainty Scale (IUS-12; Short form; Carleton et al, 2007) is a 12-item scale measuring two dimensions of Prospective and Inhibitory intolerance of uncertainty. Two subscales of the Children’s Automatic Thoughts Scale (CATS; Schniering & Rapee, 2002) were administered. The Peronalising and Social Threat were each composed of 10 items. UPPS Impulsive Behaviour Scale (Whiteside & Lynam, 2001) which is comprised of 12 items. Dispositional Envy Scale (DES; Smith et al, 1999) which is comprised of 8 items. Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. Three subscales totals included were the GAD subscale (labelled SCAS_GAD), the OCD subscale (labelled SCAS_OCD) and the Social Anxiety subscale (labelled SCAS_SA). Each subscale was comprised of 6 items. Avoidance and Fusion Questionnaire for Youth (AFQ-Y; Greco et al., 2008) which is comprised of 17 items. Distress Disclosure Index (DDI; Kahn & Hessling, 2001) which is comprised of 12 items. Repetitive Thinking Questionnaire-10 (RTQ-10; McEvoy et al., 2014) which is comprised of 10 items. The Brief Fear of Negative Evaluation Scale, Straightforward Items (BFNE-S; Rodebaugh et al., 2004) which is comprised of 8 items. Short Mood and Feelings Questionnaire (SMFQ; Angold et al., 1995) which is comprised by 13 items. The Self-Compassion Scale Short Form (SCS-SF; Raes et al., 2011) which is comprised by 12 items. The subscales include Self Kindness, Self Judgment, Social Media subscales - These subscale scores were based on social media questions composed for this project and also drawn from three separate scales as indicated in the table below. The original scales assessed whether participants experience discomfort and a fear of missing out when disconnected from social media (taken from the Australian Psychological Society Stress and Wellbeing Survey; Australian Psychological Society, 2015a), style of social media use (Tandoc et al., 2015b) and Fear of Missing Out (Przybylski et al., 2013c). The items in each subscale are listed below. Pub_Share Public Sharing When I have a good time it is important for me to share the details onlinec On social media how often do you write a status updateb On social media how often do you post photosb Surveillance_SM On social media how often do you read the newsfeed On social media how often do you read a friend’s status updateb On social media how often do you view a friend’s photob On social media how often do you browse a friend’s timelineb Upset Share On social media how often do you go online to share things that have upset you? Text private On social media how often do you Text friends privately to share things that have upset you? Insight_SM Social Media Reduction I use social media less now because it often made me feel inadequate FOMO I am afraid that I will miss out on something if I don’t stay connected to my online social networksa. I feel worried and uncomfortable when I can’t access my social media accountsa. Neg Eff of SM I find it difficult to relax or sleep after spending time on social networking sitesa. I feel my brain ‘burnout’ with the constant connectivity of social mediaa. I notice I feel envy when I use social media.
I can easily detach from the envy that appears following the use of social media (reverse scored) DES_SM Envy Mean acts online Feeling envious about another person has led me to post a comment online about another person to make them laugh Feeling envious has led me to post a photo online without someone’s permission to make them angry or to make fun of them Feeling envious has prompted me to keep another student out of things on purpose, excluding her from my group of friends or ignoring them. Substance Use: Two items measuring peer influence on alcohol consumption were adapted from the SHAHRP “Patterns of Alcohol Use” measure (McBride, Farringdon & Midford, 2000). These items were “When I am with friends I am quite likely to drink too much alcohol” and “Substances (alcohol, drugs, medication) are the immediate way I respond to my thoughts about a situation when I feel distressed or upset. Angold, A., Costello, E. J., Messer, S. C., & Pickles, A. (1995). Development of a short questionnaire for use in epidemiological studies of depression in children and adolescents. International Journal of Methods in Psychiatric Research, 5(4), 237–249. Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4 Greco, L.A., Lambert, W. & Baer., R.A. (2008) Psychological inflexibility in childhood and adolescence: Development and evaluation of the Avoidance and Fusion Questionnaire for Youth. Psychological Assessment, 20, 93-102. https://doi.org/10.1037/1040-3590.20.2.9 Kahn, J. H., & Hessling, R. M. (2001). Measuring the tendency to conceal versus disclose psychological distress. Journal of Social and Clinical Psychology, 20(1), 41–65. https://doi.org/10.1521/jscp.20.1.41.22254 McBride, N., Farringdon, F. & Midford, R. (2000) What harms do young Australians experience in alcohol use situations. Australian and New Zealand Journal of Public Health, 24, 54–60 https://doi.org/10.1111/j.1467-842x.2000.tb00723.x McEvoy, P.M., Thibodeau, M.A., Asmundson, G.J.G. (2014) Trait Repetitive Negative Thinking: A brief transdiagnostic assessment. Journal of Experimental Psychopathology, 5, 1-17. Doi. 10.5127/jep.037813 Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in human behavior, 29(4), 1841-1848. https://doi.org/10.1016/j.chb.2013.02.014 Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. https://doi.org/10.1002/cpp.702 Rodebaugh, T. L., Woods, C. M., Thissen, D. M., Heimberg, R. G., Chambless, D. L., & Rapee, R. M. (2004). More information from fewer questions: the factor structure and item properties of the original and brief fear of negative evaluation scale. Psychological assessment, 16(2), 169. https://doi.org/10.1037/10403590.16.2.169 Schniering, C. A., & Rapee, R. M. (2002). Development and validation of a measure of children’s automatic thoughts: the children’s automatic thoughts scale. Behaviour Research and Therapy, 40(9), 1091-1109. . https://doi.org/10.1016/S0005-7967(02)00022-0 Smith, R. H., Parrott, W. G., Diener, E. F., Hoyle, R. H., & Kim, S. H. (1999). Dispositional envy. Personality and Social Psychology Bulletin, 25(8), 1007-1020. https://doi.org/10.1177/01461672992511008 Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. https://doi.org/10.1016/S0005-7967(98)00034-5 Tandoc, E. C., Ferrucci, P., & Duffy, M. (2015). Facebook use, envy, and depression among college students: Is facebooking depressing? Computers in Human Behavior, 43, 139–146. https://doi.org/10.1016/j.chb.2014.10.053 Whiteside, S.P. & Lynam, D.R. (2001) The five factor model and impulsivity: using a structural model of personality to understand impulsivity. Personality and Individual Differences 30,669-689. https://doi.org/10.1016/S0191-8869(00)00064-7 The data was collected by Dr Danielle A Einstein, Dr Madeleine Fraser, Dr Anne McMaugh, Prof Peter McEvoy, Prof Ron Rapee, Assoc/Prof Maree Abbott, Prof Warren Mansell and Dr Eyal Karin as part of the Insights Project. The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels.

This dataset extends NAMEXACT by including words that can be used as names, but may not exclusively be used as names in every context.

Dataset Details Dataset Description

Unlike NAMEXACT, this datasets contains words that are mostly used as names, but may also be used in other contexts, such as

Christian (believer in Christianity) Drew (simple past of the verb to draw) Florence (an Italian city) Henry (the SI unit of inductance) Mercedes (a car brand)

In addition, names with ambiguous gender are included - once for each gender. For instance, Skyler is included as female (F) name with a probability of 37.3%, and as male (M) name with a probability of 62.7%.

Dataset Sources [optional]

Repository: github.com/aieng-lab/gradiend

Original Dataset: Gender by Name

Dataset Structure

name: the name gender: the gender of the name (M for male and F for female) count: the count value of this name (raw value from the original dataset) probability: the probability of this name (raw value from original dataset; not normalized to this dataset!) gender_agreement: a value describing the certainty that this name has an unambiguous gender computed as the maximum probability of that name across both genders, e.g., $max(37.7%, 62.7%)=62.7%$ for Skyler. For names with a unique gender in this dataset, this value is 1.0 primary_gender: is equal to gender for names with a unique gender in this dataset, and equals otherwise the gender of that name with higher probability genders: label B if both genders are contained for this name in this dataset, otherwise equal to gender prob_F: the probability of that name being used as a female name (i.e., 0.0 or 1.0 if genders != B) prob_M: the probability of that name being used as a male name

Dataset Creation Source Data

The data is created by filtering Gender by Name.

Data Collection and Processing

The original data is filtered to contain only names with a count of at least 100 to remove very rare names. This threshold reduces the total number of names by $72%, from 133910 to 37425.

Bias, Risks, and Limitations

The original dataset provides counts of names (with their gender) for male and female babies from open-source government authorities in the US (1880-2019), UK (2011-2018), Canada (2011-2018), and Australia (1944-2019) in these periods

📊 Social Media vs Productivity — Realistic Behavioral Dataset (30,000 Users)

This dataset explores how daily digital habits — including social media usage, screen time, and notification exposure — relate to individual productivity, stress, and well-being.

🔍 What’s Inside?

The dataset contains 30,000 real-world-style records simulating behavioral patterns of people with various jobs, social habits, and lifestyle choices. The goal is to understand how different digital behaviors correlate with perceived and actual productivity.

🧠 Why This Dataset is Valuable

• ✅ Designed for real-world ML workflows
  Includes missing values, noise, and outliers — ideal for practicing data cleaning and preprocessing.

• 🔗 High correlation between target features
  The perceived_productivity_score and actual_productivity_score are strongly correlated, making this dataset suitable for experiments in feature selection and multicollinearity.

• 🛠️ Feature Engineering playground
  Use this dataset to practice feature scaling, encoding, binning, interaction terms, and more.

• 🧪 Perfect for EDA, regression & classification
  You can model productivity, stress, or satisfaction based on behavior patterns and digital exposure.

🧾 Columns & Feature Info

📌 Notes

• Contains NaN values in critical columns (productivity, sleep, stress) for data imputation tasks
• Includes outliers in media usage, coffee intake, and notification count
• Target columns are strongly correlated for multicollinearity testing
• Multi-purpose: regression, classification, clustering, visualization

💡 Use Cases

• Exploratory Data Analysis (EDA)
• Feature engineering pipelines
• Machine learning model benchmarking
• Statistical hypothesis testing
• Burnout and mental health prediction projects

📥 Bonus

👉 Sample notebook coming soon with data cleaning, visualization, and productivity prediction!