Doomscrolling Is the New Smoking—and Silicon Valley Is Your Dealer

What Do Doomscrolling Social Media, ChatGPT, and Extended Device Use All Have in Common? They are all dangerous for your normal healthy physical, mental, emotional, social, and psychological life! They are all dangerous for your normal healthy physical, mental, emotional, social, and psychological life! Risks range from suicide to mental health disorders to anti-social behaviour. Risks range from suicide to mental health disorders to anti-social behaviour. The Silicon Valley billionaires without any sense of morality selling their dopamine drug Android/iOS app menu are to blame, but: The Silicon Valley billionaires without any sense of morality selling their dopamine drug Android/iOS app menu are to blame, but: One country saw the risks and took action. One country saw the risks and took action. One country saw the risks and took action. Read till the end if you want to know more about what to do with the worldwide digital device addiction pandemic! Sources: Doomscrolling: Doomscrolling: https://www.health.harvard.edu/mind-and-mood/doomscrolling-dangers?embedable=true&ref=hackernoon.com https://www.health.harvard.edu/mind-and-mood/doomscrolling-dangers?embedable=true&ref=hackernoon.com ChatGPT: ChatGPT: https://time.com/7295195/ai-chatgpt-google-learning-school/?embedable=true https://time.com/7295195/ai-chatgpt-google-learning-school/?embedable=true Device addiction: Device addiction: https://www.bbc.com/future/article/20230403-how-cellphones-have-changed-our-brains?embedable=true https://www.bbc.com/future/article/20230403-how-cellphones-have-changed-our-brains?embedable=true An anecdote is apt here. An anecdote is apt here. Steve Jobs refused his children iPads when they first came out. Steve Jobs refused his children iPads when they first came out “We don’t let our children use iPads.” (Source) “We don’t let our children use iPads.” (Source) (Source) This was the word of the creator of the iPad in 2010. There is an insidious pattern at work here. There is an insidious pattern at work here. The CEOs of big tech are selling the dopamine drug that is social media doomscrolling to billions worldwide and ruining the lives of their users. And they could not care less! And they could not care less! How the Silicon Valley CEOs are Making Billions off Depression, Suicide, and Obesity Meta Platforms Inc. (Parent of Facebook, Instagram, Threads, WhatsApp) Alphabet Inc. (Parent of Google & YouTube) X Corp. (formerly Twitter) Amazon Microsoft Pinterest Reddit Netflix TikTok (and its parent company, ByteDance) Meta Platforms Inc. (Parent of Facebook, Instagram, Threads, WhatsApp) Meta Platforms Inc. (Parent of Facebook, Instagram, Threads, WhatsApp) Alphabet Inc. (Parent of Google & YouTube) Alphabet Inc. X Corp. (formerly Twitter) X Corp. Amazon Amazon Microsoft Microsoft Pinterest Pinterest Reddit Reddit Netflix Netflix TikTok (and its parent company, ByteDance) TikTok (and its parent company, ByteDance) These companies above have taken doomscrolling and perfected it to its most addictive form. These companies above have taken doomscrolling and perfected it to its most addictive form. These companies above have taken doomscrolling and perfected it to its most addictive form. TikTok, especially, is notorious. TikTok, especially, is notorious. These companies promote a culture of non-stop digital device use and make billions in revenue: While ruining the lives of their customers by addicting them to their devices! While ruining the lives of their customers by addicting them to their devices! The Statistics Are In From the Research: And It’s a Damning Picture I have summarized a lengthy report from Perplexity.ai, and it is horrific. Doomscrolling Prevalence: Global survey: 35–40% of youth and adults report regular doomscrolling during crises (e.g., pandemic, political unrest). Regular doomscrolling doubles the risk of moderate-to-severe anxiety and depression. Associated with 14% increase in self-reported sleep disturbance. Doomscrolling Prevalence: Global survey: 35–40% of youth and adults report regular doomscrolling during crises (e.g., pandemic, political unrest). Regular doomscrolling doubles the risk of moderate-to-severe anxiety and depression. Associated with 14% increase in self-reported sleep disturbance. Doomscrolling Prevalence: Doomscrolling Prevalence: Global survey: 35–40% of youth and adults report regular doomscrolling during crises (e.g., pandemic, political unrest). Regular doomscrolling doubles the risk of moderate-to-severe anxiety and depression. Associated with 14% increase in self-reported sleep disturbance. Global survey: 35–40% of youth and adults report regular doomscrolling during crises (e.g., pandemic, political unrest). Global survey: 35–40% of youth and adults report regular doomscrolling during crises (e.g., pandemic, political unrest). Regular doomscrolling doubles the risk of moderate-to-severe anxiety and depression. Regular doomscrolling doubles the risk of moderate-to-severe anxiety and depression. Associated with 14% increase in self-reported sleep disturbance. Associated with 14% increase in self-reported sleep disturbance. Extended Social Media Use Prevalence: 52% of global teens use social media >3 hours/day; 22% use >5 hours/day. Youth with >3 hours/day social media use are 60% more likely to report depressive symptoms. Odds of high anxiety: 50% higher. Sleep and Physical Health: Extended nighttime social media use is associated with a 40% higher risk of chronic sleep deprivation. 4 hours/day screen time associated with 20–30% increase in overweight/obesity in youth. Device Addiction / Problematic Internet Use Prevalence: 7–12% of youth meet criteria for internet/device addiction. Device-addicted youth are 3x more likely to report suicidal ideation. Doubled risk of failing at least one academic subject per year. Sleep: Device-addicted teens average 6–7 hours/night (vs. 8+ recommended). Self-esteem: Inverse proportionality; heavy users report 32% lower self-esteem scores. Extended Social Media Use Prevalence: 52% of global teens use social media >3 hours/day; 22% use >5 hours/day. Youth with >3 hours/day social media use are 60% more likely to report depressive symptoms. Odds of high anxiety: 50% higher. Extended Social Media Use Prevalence: Extended Social Media Use Prevalence: 52% of global teens use social media >3 hours/day; 22% use >5 hours/day. Youth with >3 hours/day social media use are 60% more likely to report depressive symptoms. Odds of high anxiety: 50% higher. 52% of global teens use social media >3 hours/day; 22% use >5 hours/day. 52% of global teens use social media >3 hours/day; 22% use >5 hours/day. 52% of global teens use social media >3 hours/day; 22% use >5 hours/day. Youth with >3 hours/day social media use are 60% more likely to report depressive symptoms. Youth with >3 hours/day social media use are 60% more likely to report depressive symptoms. Youth with >3 hours/day social media use are 60% more likely to report depressive symptoms. Odds of high anxiety: 50% higher. Odds of high anxiety: 50% higher. Odds of high anxiety: 50% higher. Sleep and Physical Health: Extended nighttime social media use is associated with a 40% higher risk of chronic sleep deprivation. 4 hours/day screen time associated with 20–30% increase in overweight/obesity in youth. Sleep and Physical Health: Sleep and Physical Health: Extended nighttime social media use is associated with a 40% higher risk of chronic sleep deprivation. 4 hours/day screen time associated with 20–30% increase in overweight/obesity in youth. Extended nighttime social media use is associated with a 40% higher risk of chronic sleep deprivation. Extended nighttime social media use is associated with a 40% higher risk of chronic sleep deprivation. Extended nighttime social media use is associated with a 40% higher risk of chronic sleep deprivation. 4 hours/day screen time associated with 20–30% increase in overweight/obesity in youth. 4 hours/day screen time associated with 20–30% increase in overweight/obesity in youth. 4 hours/day screen time associated with 20–30% increase in overweight/obesity in youth. Device Addiction / Problematic Internet Use Prevalence: 7–12% of youth meet criteria for internet/device addiction. Device-addicted youth are 3x more likely to report suicidal ideation. Doubled risk of failing at least one academic subject per year. Sleep: Device-addicted teens average 6–7 hours/night (vs. 8+ recommended). Self-esteem: Inverse proportionality; heavy users report 32% lower self-esteem scores. Device Addiction / Problematic Internet Use Prevalence: Device Addiction / Problematic Internet Use Prevalence: 7–12% of youth meet criteria for internet/device addiction. Device-addicted youth are 3x more likely to report suicidal ideation. Doubled risk of failing at least one academic subject per year. Sleep: Device-addicted teens average 6–7 hours/night (vs. 8+ recommended). Self-esteem: Inverse proportionality; heavy users report 32% lower self-esteem scores. 7–12% of youth meet criteria for internet/device addiction. 7–12% of youth meet criteria for internet/device addiction. Device-addicted youth are 3x more likely to report suicidal ideation. Device-addicted youth are 3x more likely to report suicidal ideation. Doubled risk of failing at least one academic subject per year. Doubled risk of failing at least one academic subject per year. Sleep: Device-addicted teens average 6–7 hours/night (vs. 8+ recommended). Sleep: Device-addicted teens average 6–7 hours/night (vs. 8+ recommended). Self-esteem: Inverse proportionality; heavy users report 32% lower self-esteem scores. Self-esteem: Inverse proportionality; heavy users report 32% lower self-esteem scores. All of this and more, to line the pockets of billionaires in Silicon Valley. All of this and more, to line the pockets of billionaires in Silicon Valley. While destroying the lives of billions around the world! While destroying the lives of billions around the world! Our addictions lead to higher profits - so: optimize for addiction. Our addictions lead to higher profits - so: optimize for addiction. It’s Not Just the Youth I’m aiming this section at adults. How many times has a look at your phone led to three hours on Netflix? How many times has a look at your phone led to three hours on Netflix? How many times has a WhatsApp message led to 2 hours on Facebook? How many times has a WhatsApp message led to 2 hours on Facebook? How many times have you watched YouTube shorts for hours? How many times have you watched YouTube shorts for hours? When was the last time you stopped going through Instagram voluntarily before 2 hours were over? When was the last time you stopped going through Instagram voluntarily before 2 hours were over? This just doesn’t damage the youth - it attacks people of all ages. And I see people lose productivity, jobs, and even lives. The Silicon Valley billionaires: Don’t care. Our lives mean nothing to them, but the billions of dollars in advertising revenue does! Don’t care. Our lives mean nothing to them, but the billions of dollars in advertising revenue does! People of all ages are exercising less, going outside less, and finding everything they need in a smartphone, tablet, or laptop. This entire device ecosystem that MAANG is pushing on us is ruining us - one infinite scroll at a time. A/B tests are done to see which tactic keeps users engaged longer. While children are most at risk, I see adults with the same problem. How can it be addressed? We’ll deal with that soon. But kudos to a country that saw what this technology does to youngsters, and saved their generation. China Saw This Coming - and Took Steps I am not a China fanboy - especially as an Indian. I am not a China fanboy - especially as an Indian. If it were not for China, India would have been on the Security Council of the United Nations decades ago. If it were not for China, India would have been on the Security Council of the United Nations decades ago. If it were not for China, India would have been on the Security Council of the United Nations decades ago. If it were not for China, India would have been on the Security Council of the United Nations decades ago. But they have done some exemplary work when it comes to device safety for children and the youth in their nation. But they have done some exemplary work when it comes to device safety for children and the youth in their nation. You need to read this: You need to read this: China's Internet Restrictions: Platform Bans, Domestic Alternatives, and AI Regulation China now maintains one of the world's most comprehensive internet censorship systems, commonly known as the Great Firewall, which blocks access to major Western platforms, including YouTube, Netflix, Amazon Prime Video, Facebook, Twitter, WhatsApp, Instagram, and Google services. the Great Firewall, This extensive filtering system serves many purposes, but one purpose stands out - safeguarding the mental health of its children and youth. Instead of these blocked platforms, China has developed a robust ecosystem of domestic alternatives such as Youku and iQiyi for video streaming, Baidu for search, WeChat and Weibo for social media, and Tmall for e-commerce. Recent developments in AI regulation have extended these restrictions to include limitations on ChatGPT and other OpenAI services, as China seeks to maintain control over artificial intelligence technologies while developing its own competitive alternatives. This was carefully calculated move with some highly intelligent leadership. The USA leadership - well - that orange stain in the White House is actually in favor of TikTok - a Chinese company sabotaging the lives of youngsters in the US. The USA leadership - well - that orange stain in the White House is actually in favor of TikTok - a Chinese company sabotaging the lives of youngsters in the US. that orange stain in the White House Source: https://www.cnbc.com/2025/01/22/tiktok-return-harms-kids-jonathan-haidt-anxious-generation.html?embedable=true https://www.cnbc.com/2025/01/22/tiktok-return-harms-kids-jonathan-haidt-anxious-generation.html?embedable=true Cultural and Social Considerations Cultural protection motivations include concerns about the impact of foreign content on Chinese values and social norms. Chinese values and social norms Authorities argue that unrestricted access to foreign platforms could undermine traditional Chinese culture, promote inappropriate behaviors or values, or create social divisions based on access to foreign versus domestic content and services. The government has also expressed concerns about addiction and social media dependency, particularly among young people, leading to various restrictions and regulations designed to limit excessive internet use and promote healthier digital habits. addiction and social media dependency These concerns have influenced platform design requirements, usage time limitations, and content recommendation algorithms for platforms operating in China. Platform Design, Content, and Behavioral Patterns Platform Design, Content, and Behavioral Patterns The social media in China is completely different from the worldwide scenario. More Educational and ‘Wholesome’ Content: More Educational and ‘Wholesome’ Content: Research published in journals like Nature (2023) and large-scale surveys by the Oxford Internet Institute show that Douyin (China’s TikTok) promotes more educational, skill-building, and ‘positive’ content to youth, partly due to regulatory requirements. Nature In contrast, TikTok USA’s algorithm strongly prioritizes entertainment and viral trends, often promoting risky behaviors. Time Restrictions and Digital Wellbeing: Time Restrictions and Digital Wellbeing: China leads the world in enforcing time limits for minors on video games and short-video apps (e.g., Douyin imposes a 40-minute daily limit for users under 14). Studies by UNICEF and China Youth Research Center (2024) report that Chinese teens spend less time on entertainment apps compared to global peers, reducing screen-time-related harms like sleep disruption. This means that 1.3B+ users in China pay Chinese companies to build an Internet that is safe for young people. And the Chinese companies earn billions. Win-win. This means that 1.3B+ users in China pay Chinese companies to build an Internet that is safe for young people. This means that 1.3B+ users in China pay Chinese companies to build an Internet that is safe for young people. And the Chinese companies earn billions. Win-win. And the Chinese companies earn billions. Win-win. And the Result of the Great Firewall? The following table is a side-by-side comparison between China and the USA on mental health metrics for the youth. Disorder / Issue China (10–40) USA (10–40) Depression 17–22% 28–33% Anxiety 18–24% 30–35% Suicide rate* 4.1 13.2 Self-harm 15–19% 20–26% Eating disorders 3–5% 8–13% Internet addiction 10–13% 13–17% Cyberbullying 8–12% 20–30% Disorder / Issue China (10–40) USA (10–40) Depression 17–22% 28–33% Anxiety 18–24% 30–35% Suicide rate* 4.1 13.2 Self-harm 15–19% 20–26% Eating disorders 3–5% 8–13% Internet addiction 10–13% 13–17% Cyberbullying 8–12% 20–30% Disorder / Issue China (10–40) USA (10–40) Disorder / Issue Disorder / Issue China (10–40) China (10–40) USA (10–40) USA (10–40) Depression 17–22% 28–33% Depression Depression Depression 17–22% 17–22% 28–33% 28–33% Anxiety 18–24% 30–35% Anxiety Anxiety Anxiety 18–24% 18–24% 30–35% 30–35% Suicide rate* 4.1 13.2 Suicide rate* Suicide rate* Suicide rate* 4.1 4.1 13.2 13.2 Self-harm 15–19% 20–26% Self-harm Self-harm Self-harm 15–19% 15–19% 20–26% 20–26% Eating disorders 3–5% 8–13% Eating disorders Eating disorders Eating disorders 3–5% 3–5% 8–13% 8–13% Internet addiction 10–13% 13–17% Internet addiction Internet addiction Internet addiction 10–13% 10–13% 13–17% 13–17% Cyberbullying 8–12% 20–30% Cyberbullying Cyberbullying Cyberbullying 8–12% 8–12% 20–30% 20–30% *Suicide rate per 100,000 population annually (ages 10–40, as per reporting standards). Sources: Sources: China: WHO China, Chinese CDC 2024, Lancet Psychiatry, National Health Commission 2024. USA: CDC Youth Risk Surveillance 2025, NIMH, SAMHSA, JAMA, Pew Research 2025. China: WHO China, Chinese CDC 2024, Lancet Psychiatry, National Health Commission 2024. China: Lancet Psychiatry USA: CDC Youth Risk Surveillance 2025, NIMH, SAMHSA, JAMA, Pew Research 2025. USA: The statistics paint a damning picture of what Silicon Valley has done to the people of the USA. Depression in the US double that of China. Anxiety attacks 50% higher. Suicide rates 300% higher than China. That’s a big one! Self-harm around 50% higher. Eating disorders 300% higher. Internet addiction 33% higher. Cyberbullying around 250% higher. The statistics paint a damning picture of what Silicon Valley has done to the people of the USA. The statistics paint a damning picture of what Silicon Valley has done to the people of the USA. Depression in the US double that of China. Anxiety attacks 50% higher. Suicide rates 300% higher than China. That’s a big one! Self-harm around 50% higher. Eating disorders 300% higher. Internet addiction 33% higher. Cyberbullying around 250% higher. Depression in the US double that of China. Anxiety attacks 50% higher. Suicide rates 300% higher than China. That’s a big one! Suicide rates 300% higher than China. That’s a big one! Self-harm around 50% higher. Eating disorders 300% higher. Internet addiction 33% higher. Cyberbullying around 250% higher. That is not pretty reading. That’s a damning indictment of the doom scrolling optimized Internet fed to the world by Silicon Valley. That’s a damning indictment of the doom scrolling optimized Internet fed to the world by Silicon Valley. We lose our minds - They earn billions every month! Causality Analysis Just to be sure that the statistics, like the suicide rate in the US being 3 times that of China, was due to device proliferation and the profits of Silicon Valley, I did a few graphs. The data was gathered by web scraping and arranged into a table, after which the raw data was fed into the Python code: USA - The Data Year Social Media Users (Millions) % of Adults with Any Mental Illness (AMI) % of Youth (12-17) with Major Depressive Episode (MDE) 2010 151 17.7% 8.1% 2012 182 18.1% 9.1% 2014 205 18.1% 11.0% 2016 225 18.2% 12.8% 2018 243 19.1% 14.4% 2020 273 21.0% 17.0% 2021 283 22.8% 19.6% 2022 291 23.1% 20.1% 2023 296 ~23.5% ~20.5% 2024* 301* ~23.9% ~20.8% 2025* 305* ~24.2% Year Social Media Users (Millions) % of Adults with Any Mental Illness (AMI) % of Youth (12-17) with Major Depressive Episode (MDE) 2010 151 17.7% 8.1% 2012 182 18.1% 9.1% 2014 205 18.1% 11.0% 2016 225 18.2% 12.8% 2018 243 19.1% 14.4% 2020 273 21.0% 17.0% 2021 283 22.8% 19.6% 2022 291 23.1% 20.1% 2023 296 ~23.5% ~20.5% 2024* 301* ~23.9% ~20.8% 2025* 305* ~24.2% Year Social Media Users (Millions) % of Adults with Any Mental Illness (AMI) % of Youth (12-17) with Major Depressive Episode (MDE) Year Year Social Media Users (Millions) Social Media Users (Millions) % of Adults with Any Mental Illness (AMI) % of Adults with Any Mental Illness (AMI) % of Youth (12-17) with Major Depressive Episode (MDE) % of Youth (12-17) with Major Depressive Episode (MDE) 2010 151 17.7% 8.1% 2010 2010 151 151 17.7% 17.7% 8.1% 8.1% 2012 182 18.1% 9.1% 2012 2012 182 182 18.1% 18.1% 9.1% 9.1% 2014 205 18.1% 11.0% 2014 2014 205 205 18.1% 18.1% 11.0% 11.0% 2016 225 18.2% 12.8% 2016 2016 225 225 18.2% 18.2% 12.8% 12.8% 2018 243 19.1% 14.4% 2018 2018 243 243 19.1% 19.1% 14.4% 14.4% 2020 273 21.0% 17.0% 2020 2020 273 273 21.0% 21.0% 17.0% 17.0% 2021 283 22.8% 19.6% 2021 2021 283 283 22.8% 22.8% 19.6% 19.6% 2022 291 23.1% 20.1% 2022 2022 291 291 23.1% 23.1% 20.1% 20.1% 2023 296 ~23.5% ~20.5% 2023 2023 296 296 ~23.5% ~23.5% ~23.5% ~20.5% ~20.5% ~20.5% 2024* 301* ~23.9% ~20.8% 2024* 2024* 301* 301* ~23.9% ~23.9% ~23.9% ~20.8% ~20.8% ~20.8% 2025* 305* ~24.2% 2025* 2025* 305* 305* ~24.2% ~24.2% ~24.2% The Python Code import pandas as pd import matplotlib.pyplot as plt import numpy as np # Data for the United States us_data = { 'Year': [2010, 2012, 2014, 2016, 2018, 2020, 2021, 2022, 2023, 2024, 2025], 'Social Media Users (Millions)': [151, 182, 205, 225, 243, 273, 283, 291, 296, 301, 305], 'Adult AMI (%)': [17.7, 18.1, 18.1, 18.2, 19.1, 21.0, 22.8, 23.1, 23.5, 23.9, 24.2], 'Youth MDE (%)': [8.1, 9.1, 11.0, 12.8, 14.4, 17.0, 19.6, 20.1, 20.5, 20.8, 21.1] } us_df = pd.DataFrame(us_data) # Create the plot fig, ax1 = plt.subplots(figsize=(14, 8)) # Plotting Social Media Users on the primary y-axis (ax1) color = 'royalblue' ax1.set_xlabel('Year') ax1.set_ylabel('Social Media Users (Millions)', color=color, fontsize=12) ax1.plot(us_df['Year'], us_df['Social Media Users (Millions)'], color=color, marker='o', linestyle='-', label='Social Media Users (Millions)') ax1.tick_params(axis='y', labelcolor=color) ax1.grid(True, which='major', linestyle='--', axis='y') ax1.set_ylim(0, 350) # Creating a secondary y-axis (ax2) for the percentage data ax2 = ax1.twinx() color_adult = 'darkorange' color_youth = 'forestgreen' ax2.set_ylabel('Prevalence of Mental Health Conditions (%)', color='black', fontsize=12) # Plotting mental health data ax2.plot(us_df['Year'], us_df['Adult AMI (%)'], color=color_adult, marker='s', linestyle='--', label='% Adults with Any Mental Illness (AMI)') ax2.plot(us_df['Year'], us_df['Youth MDE (%)'], color=color_youth, marker='^', linestyle='--', label='% Youth with Major Depressive Episode (MDE)') ax2.tick_params(axis='y', labelcolor='black') ax2.set_ylim(0, 25) # Adding a title and legend plt.title('US: Social Media Growth vs. Mental Health Trends (2010-2025)', fontsize=16) fig.legend(loc="upper left", bbox_to_anchor=(0.1, 0.9)) # Add a note about projected data plt.text(2022.5, 1.5, '*2023-2025 data are projections', fontsize=9, style='italic', color='grey') fig.tight_layout() plt.show() import pandas as pd import matplotlib.pyplot as plt import numpy as np # Data for the United States us_data = { 'Year': [2010, 2012, 2014, 2016, 2018, 2020, 2021, 2022, 2023, 2024, 2025], 'Social Media Users (Millions)': [151, 182, 205, 225, 243, 273, 283, 291, 296, 301, 305], 'Adult AMI (%)': [17.7, 18.1, 18.1, 18.2, 19.1, 21.0, 22.8, 23.1, 23.5, 23.9, 24.2], 'Youth MDE (%)': [8.1, 9.1, 11.0, 12.8, 14.4, 17.0, 19.6, 20.1, 20.5, 20.8, 21.1] } us_df = pd.DataFrame(us_data) # Create the plot fig, ax1 = plt.subplots(figsize=(14, 8)) # Plotting Social Media Users on the primary y-axis (ax1) color = 'royalblue' ax1.set_xlabel('Year') ax1.set_ylabel('Social Media Users (Millions)', color=color, fontsize=12) ax1.plot(us_df['Year'], us_df['Social Media Users (Millions)'], color=color, marker='o', linestyle='-', label='Social Media Users (Millions)') ax1.tick_params(axis='y', labelcolor=color) ax1.grid(True, which='major', linestyle='--', axis='y') ax1.set_ylim(0, 350) # Creating a secondary y-axis (ax2) for the percentage data ax2 = ax1.twinx() color_adult = 'darkorange' color_youth = 'forestgreen' ax2.set_ylabel('Prevalence of Mental Health Conditions (%)', color='black', fontsize=12) # Plotting mental health data ax2.plot(us_df['Year'], us_df['Adult AMI (%)'], color=color_adult, marker='s', linestyle='--', label='% Adults with Any Mental Illness (AMI)') ax2.plot(us_df['Year'], us_df['Youth MDE (%)'], color=color_youth, marker='^', linestyle='--', label='% Youth with Major Depressive Episode (MDE)') ax2.tick_params(axis='y', labelcolor='black') ax2.set_ylim(0, 25) # Adding a title and legend plt.title('US: Social Media Growth vs. Mental Health Trends (2010-2025)', fontsize=16) fig.legend(loc="upper left", bbox_to_anchor=(0.1, 0.9)) # Add a note about projected data plt.text(2022.5, 1.5, '*2023-2025 data are projections', fontsize=9, style='italic', color='grey') fig.tight_layout() plt.show() Output I do not know about you, but I see the correlation very clearly. China - The Data China is notoriously secretive. But I got hold of some studies that had limited data but a clear pattern: China: Social Media and Mental Health Trends (2010–2025) Year Social Media Users (Millions) Adult Mental Health Prevalence (%) Youth Mental Health Prevalence (%) 2010 457 6.3 N/A 2011 503 6.3 N/A 2012 554 6.4 N/A 2013 608 6.4 N/A 2014 649 6.5 N/A 2015 668 6.5 N/A 2016 715 6.6 N/A 2017 783 6.6 N/A 2018 852 6.7 N/A 2019 921 6.8 N/A 2020 986 6.8 N/A 2021 1011 6.9 N/A 2022 1022 7.0 N/A 2023 1029 7.1 N/A 2024 1044 N/A N/A 2025 1080 N/A N/A Year Social Media Users (Millions) Adult Mental Health Prevalence (%) Youth Mental Health Prevalence (%) 2010 457 6.3 N/A 2011 503 6.3 N/A 2012 554 6.4 N/A 2013 608 6.4 N/A 2014 649 6.5 N/A 2015 668 6.5 N/A 2016 715 6.6 N/A 2017 783 6.6 N/A 2018 852 6.7 N/A 2019 921 6.8 N/A 2020 986 6.8 N/A 2021 1011 6.9 N/A 2022 1022 7.0 N/A 2023 1029 7.1 N/A 2024 1044 N/A N/A 2025 1080 N/A N/A Year Social Media Users (Millions) Adult Mental Health Prevalence (%) Youth Mental Health Prevalence (%) Year Year Year Social Media Users (Millions) Social Media Users (Millions) Social Media Users (Millions) Adult Mental Health Prevalence (%) Adult Mental Health Prevalence (%) Adult Mental Health Prevalence (%) Youth Mental Health Prevalence (%) Youth Mental Health Prevalence (%) Youth Mental Health Prevalence (%) 2010 457 6.3 N/A 2010 2010 457 457 6.3 6.3 N/A N/A 2011 503 6.3 N/A 2011 2011 503 503 6.3 6.3 N/A N/A 2012 554 6.4 N/A 2012 2012 554 554 6.4 6.4 N/A N/A 2013 608 6.4 N/A 2013 2013 608 608 6.4 6.4 N/A N/A 2014 649 6.5 N/A 2014 2014 649 649 6.5 6.5 N/A N/A 2015 668 6.5 N/A 2015 2015 668 668 6.5 6.5 N/A N/A 2016 715 6.6 N/A 2016 2016 715 715 6.6 6.6 N/A N/A 2017 783 6.6 N/A 2017 2017 783 783 6.6 6.6 N/A N/A 2018 852 6.7 N/A 2018 2018 852 852 6.7 6.7 N/A N/A 2019 921 6.8 N/A 2019 2019 921 921 6.8 6.8 N/A N/A 2020 986 6.8 N/A 2020 2020 986 986 6.8 6.8 N/A N/A 2021 1011 6.9 N/A 2021 2021 1011 1011 6.9 6.9 N/A N/A 2022 1022 7.0 N/A 2022 2022 1022 1022 7.0 7.0 N/A N/A 2023 1029 7.1 N/A 2023 2023 1029 1029 7.1 7.1 N/A N/A 2024 1044 N/A N/A 2024 2024 1044 1044 N/A N/A N/A N/A 2025 1080 N/A N/A 2025 2025 1080 1080 N/A N/A N/A N/A The Python Code import pandas as pd import matplotlib.pyplot as plt # Data from the previously created table data_china = { 'Year': range(2010, 2026), 'Social Media Users (Millions)': [ 457, 503, 554, 608, 649, 668, 715, 783, 852, 921, 986, 1011, 1022, 1029, 1044, 1080 ], 'Adult Mental Health Prevalence (%)': [ 6.3, 6.3, 6.4, 6.4, 6.5, 6.5, 6.6, 6.6, 6.7, 6.8, 6.8, 6.9, 7.0, 7.1, None, None # Use None for plotting ] } df_china_plot = pd.DataFrame(data_china) # Create the plot fig, ax1 = plt.subplots(figsize=(14, 8)) # Plotting Social Media Users on the primary y-axis (ax1) color_sm = 'crimson' ax1.set_xlabel('Year', fontsize=12) ax1.set_ylabel('Social Media Users (Millions)', color=color_sm, fontsize=12) ax1.plot(df_china_plot['Year'], df_china_plot['Social Media Users (Millions)'], color=color_sm, marker='o', linestyle='-', label='Social Media Users (Millions)') ax1.tick_params(axis='y', labelcolor=color_sm) ax1.grid(True, which='major', linestyle='--', axis='y') ax1.set_ylim(0, 1200) # Creating a secondary y-axis (ax2) for the percentage data ax2 = ax1.twinx() color_mh = 'goldenrod' ax2.set_ylabel('Adult Mental Health Prevalence (%)', color='black', fontsize=12) # Plotting mental health data. The .dropna() method will ensure that we only plot years where we have data. df_mh_plot = df_china_plot[['Year', 'Adult Mental Health Prevalence (%)']].dropna() ax2.plot(df_mh_plot['Year'], df_mh_plot['Adult Mental Health Prevalence (%)'], color=color_mh, marker='s', linestyle='--', label='Adult Mental Health Prevalence (IHME est.)') ax2.tick_params(axis='y', labelcolor='black') ax2.set_ylim(0, 10) # Adding a title and combined legend plt.title('China: Social Media Growth vs. Adult Mental Health Trends (2010-2023)', fontsize=16) fig.legend(loc="upper left", bbox_to_anchor=(0.1, 0.9)) # Add a note about the data sources and limitations plt.text(2010, -1.5, '*Social Media data from Statista/DataReportal. Projections for 2024-2025.\n' '*Adult Mental Health data from IHME Global Burden of Disease estimates (Depressive & Anxiety disorders).\n' '*Separate, continuous data for Youth Mental Health is not available from these sources.', fontsize=9, style='italic', color='grey', ha='left') fig.tight_layout() plt.show() import pandas as pd import matplotlib.pyplot as plt # Data from the previously created table data_china = { 'Year': range(2010, 2026), 'Social Media Users (Millions)': [ 457, 503, 554, 608, 649, 668, 715, 783, 852, 921, 986, 1011, 1022, 1029, 1044, 1080 ], 'Adult Mental Health Prevalence (%)': [ 6.3, 6.3, 6.4, 6.4, 6.5, 6.5, 6.6, 6.6, 6.7, 6.8, 6.8, 6.9, 7.0, 7.1, None, None # Use None for plotting ] } df_china_plot = pd.DataFrame(data_china) # Create the plot fig, ax1 = plt.subplots(figsize=(14, 8)) # Plotting Social Media Users on the primary y-axis (ax1) color_sm = 'crimson' ax1.set_xlabel('Year', fontsize=12) ax1.set_ylabel('Social Media Users (Millions)', color=color_sm, fontsize=12) ax1.plot(df_china_plot['Year'], df_china_plot['Social Media Users (Millions)'], color=color_sm, marker='o', linestyle='-', label='Social Media Users (Millions)') ax1.tick_params(axis='y', labelcolor=color_sm) ax1.grid(True, which='major', linestyle='--', axis='y') ax1.set_ylim(0, 1200) # Creating a secondary y-axis (ax2) for the percentage data ax2 = ax1.twinx() color_mh = 'goldenrod' ax2.set_ylabel('Adult Mental Health Prevalence (%)', color='black', fontsize=12) # Plotting mental health data. The .dropna() method will ensure that we only plot years where we have data. df_mh_plot = df_china_plot[['Year', 'Adult Mental Health Prevalence (%)']].dropna() ax2.plot(df_mh_plot['Year'], df_mh_plot['Adult Mental Health Prevalence (%)'], color=color_mh, marker='s', linestyle='--', label='Adult Mental Health Prevalence (IHME est.)') ax2.tick_params(axis='y', labelcolor='black') ax2.set_ylim(0, 10) # Adding a title and combined legend plt.title('China: Social Media Growth vs. Adult Mental Health Trends (2010-2023)', fontsize=16) fig.legend(loc="upper left", bbox_to_anchor=(0.1, 0.9)) # Add a note about the data sources and limitations plt.text(2010, -1.5, '*Social Media data from Statista/DataReportal. Projections for 2024-2025.\n' '*Adult Mental Health data from IHME Global Burden of Disease estimates (Depressive & Anxiety disorders).\n' '*Separate, continuous data for Youth Mental Health is not available from these sources.', fontsize=9, style='italic', color='grey', ha='left') fig.tight_layout() plt.show() Output Interpretation: very limited to almost no causality! The Chinese were wise. They looked after the mental health of their people. The rest of the world couldn’t, and the country the worst hit was the USA. The Silicon Valley billionaires have played us all. The Silicon Valley billionaires have played us all. The Silicon Valley billionaires have played us all. Actionable Steps to Take to Reclaim Your Life From Device Addiction Here are several steps you can take to decouple yourself from your devices. Here are several steps you can take to decouple yourself from your devices. Bookmark this article if you need to refer to it often! Bookmark this article if you need to refer to it often! 1. Turn Off Notifications Look, check your email, WhatsApp, Slack, and LinkedIn at set times during the day, and do not exceed one hour for all three combined. Notifications going off all day on multiple devices destroy your ability to focus. Tell your team members that you have blocked out time for deep focus. This is not new. There are a huge number of professionals in every field already doing this. Turn. Off. Notiications. On. All. Devices. Turn. Off. Notiications. On. All. Devices. 2. Get off Facebook, Instagram, and TikTok (especially TikTok) Facebook was created for college students to follow consume media from the people they were attracted to (sexually). Today, it still serves the same purpose. Instagram is a smut-vending machine that gives you what you like the most. And gee, what do we like the most when it comes to media? And gee, what do we like the most when it comes to media? TikTok is especially dangerous. If you were not convinced by the article I linked above, well, this might convince you! Here’s another article, shorter this time: https://www.npr.org/2024/10/13/nx-s1-5150594/tiktok-has-known-about-how-the-app-can-harm-teens-and-preteens-new-documents-show?embedable=true https://www.npr.org/2024/10/13/nx-s1-5150594/tiktok-has-known-about-how-the-app-can-harm-teens-and-preteens-new-documents-show?embedable=true Unless you are a content creator, making money off these platforms: Stop using them for at least a short period of time. Start with one single day. Yes, it will seem impossible. But your mind, body, heart, spirit, and soul will thank you. But your mind, body, heart, spirit, and soul will thank you. But your mind, body, heart, spirit, and soul will thank you 3. Stop Netflix, Amazon Prime Video, Disney Hotstar, YouTube Shorts, and Binging YouTube Why? Why? These things are designed for one thing. To hook you, and keep you glued to the screen for hours. To hook you, and keep you glued to the screen for hours. The recommendation systems they use have your: Age Gender Video Likes and Dislikes Your Addictions Your Cravings And what other people like you are watching. Age Gender Video Likes and Dislikes Your Addictions Your Cravings And what other people like you are watching. It’s a never-ending black hole of content. It’s a never-ending black hole of content. It’s a never-ending black hole of content. Stop it. Before it destroys your life when something bad happens to you: A loved one dies You lose your job Deadlines crush you Stress is a problem Exam tension Peer pressure becomes acute A loved one dies You lose your job Deadlines crush you Stress is a problem Exam tension Peer pressure becomes acute Try. Try. Try. But, remember: 4. Replace Your Device Habits With Healthy Ones Play a musical instrument. Join a choir. Get a part-time job. Stop consuming and start creating on the Internet Play sports Practice your religion Join a youth club Play a musical instrument. Join a choir. Get a part-time job. Stop consuming and start creating on the Internet Play sports Practice your religion Join a youth club If you do not replace a habit, the habit comes back. Replace unhealthy device addiction habits with healthy lifestyle habits. Replace unhealthy device addiction habits with healthy lifestyle habits. Replace unhealthy device addiction habits with healthy lifestyle habits. That’s the only way to stay away. That’s the only way to stay away. That’s the only way to stay away. 5. Find Your Community Not an internet community. Not an internet community. Internet communities and chatrooms can be incredibly dangerous, especially for youngsters. Internet communities and chatrooms can be incredibly dangerous, especially for youngsters. Join a sports club Your church youth group A community organization A self-help group A book club - are Kindles not still a thing? Volunteer in an orphanage or an old-age home Serve others around you. Build deep relationships. Join a sports club Your church youth group A community organization A self-help group A book club - are Kindles not still a thing? Volunteer in an orphanage or an old-age home Serve others around you. Build deep relationships. I repeat: But your mind, body, heart, spirit, and soul will thank you. But your mind, body, heart, spirit, and soul will thank you. But your mind, body, heart, spirit, and soul will thank you There is more I could write, but this article is already quite long. I need to wrap things up. Conclusion Your life is more than a device. There’s more to see, than can ever be seen, more to do, than can ever be done (Circle of Life, The Lion King) There’s more to see, than can ever be seen, more to do, than can ever be done (Circle of Life, The Lion King) In the real world! In the real world! There are countries to see. Talents to discover. Skills to learn. Real in-person communities to join. Maybe even find your soulmate! Whatever you are searching for: You won’t find it in an Android or iOS device (or Windows/Linux laptop). Wake up early: Get off your rear end. And explore your world wisely. And if you take just one lesson from this entire article: Turn off your notifications - and do some fantastic work! I believe in you. I believe in you. I believe in you. You got this. You got this. You got this. Turn off your device. Turn off your device. Turn off your device. And start living the life of your dreams! And start living the life of your dreams! And start living the life of your dreams! This article was written almost 100% by a human. This article was written almost 100% by a human. The majority of the research was performed by Perplexity.ai. The majority of the research was performed by Perplexity.ai. Google AI Studio wrote the Python code and did the plots. Google AI Studio wrote the Python code and did the plots. All images generated by the author using NightCafe Studio free subscription: https://creator.nightcafe.studio/explore All images generated by the author using NightCafe Studio free subscription: https://creator.nightcafe.studio/explore https://creator.nightcafe.studio/explore