How Your Personality is Public Property

Comprehensive Data Collection & Inference Table

Category	Examples of Data Collected	How It’s Collected	What Can Be Inferred?	Who Collects It?
Personal Information	Full name, age, gender, phone number, email, address, date of birth, social security/national ID (sometimes)	Provided at sign-up, account settings, government-linked services	Identity verification, risk profiling, marketing segmentation	Social media (Facebook, LinkedIn), search engines (Google), e-commerce (Amazon, eBay)
Demographics & Preferences	Ethnicity (sometimes inferred), religion, political affiliation, relationship status, sexual orientation (if shared or inferred from content)	User-provided info, post analysis, surveys, interaction patterns	Voting behavior, product interests, susceptibility to certain ads	Facebook, TikTok, Google, ad networks
Browsing & Search History	Websites visited, links clicked, time spent per site, pages viewed, scroll depth	Cookies, tracking pixels, browser fingerprinting	Political opinions, shopping habits, entertainment preferences	Google, Bing, Facebook, ad networks, ISPs
Search Engine Queries	Search terms, autocomplete suggestions used, voice searches, click-through choices	Search engine logs, browser history tracking	Personal concerns, health conditions, upcoming travel, intent to buy	Google, Bing, DuckDuckGo (limited), Yahoo
Social Media Activity	Posts, comments, likes, shares, reactions, video watch history, private messages (sometimes analyzed)	Direct interaction tracking, AI-based analysis	Personality type, hobbies, social circle, emotional state	Facebook, Twitter (X), Instagram, TikTok, LinkedIn, Snapchat
Friends, Connections & Networks	Friend lists, followers, interactions, frequency of communication	User-provided, behavioral analysis	Closeness of relationships, influence level, potential advertising targets	Facebook, Instagram, LinkedIn, TikTok, Twitter (X)
Device & App Usage	Device model, OS, installed apps, app usage time, software updates	Device ID tracking, app permissions, mobile OS logs	Tech-savviness, preferred brands, security risk levels	Google (Android), Apple (iOS), app developers
Purchase & Financial Data	Online purchases, payment methods, card details, billing address, saved shopping carts	E-commerce transactions, payment gateway tracking	Spending habits, financial stability, likelihood to buy premium products	Amazon, eBay, PayPal, Google Pay, Apple Pay, Shopify
Ad Engagement & Interests	Ads viewed/clicked, purchase predictions, conversion tracking	Browser cookies, ad trackers, pixel tracking	Likelihood to respond to marketing, future purchases	Facebook, Google, Instagram, Twitter, TikTok, ad networks
Location & Movement Data	Real-time GPS, IP-based location, WiFi signals, Bluetooth beacons, frequently visited places	GPS tracking, IP logs, app permissions	Inferences: Home & work address, daily routine, preferred stores, nightlife habits, commuting patterns, travel history, income level (based on visited areas)	Google Maps, Facebook, Apple, ISPs, ride-sharing apps
Behavioral & Psychological Data	Personality predictions, emotional analysis, mood tracking, inferred stress levels	AI-based analysis of posts, reactions, search behavior	Inferences: Mental health status, susceptibility to emotional ads, likelihood of engagement in political causes	Facebook, TikTok, Instagram, Google
Health & Fitness Data	Steps, heart rate, sleep patterns, menstrual cycles, exercise history, medical history (sometimes)	Wearable devices, health apps, smartwatches, medical service apps	Inferences: Risk for diseases, fitness level, lifestyle habits, pregnancy (based on tracking patterns)	Apple Health, Google Fit, Fitbit, MyFitnessPal
Voice Data & Smart Assistants	Voice recordings, speech patterns, stored commands, AI transcription of conversations	Smart speakers, voice assistants, AI-powered apps	Inferences: Topics of interest, emotional tone, household dynamics	Alexa, Google Assistant, Siri
Biometric Data	Face recognition, fingerprint scans, iris scans, voice ID	Security features, app permissions	Inferences: Unique identification, physical health analysis, potential security risks	Apple Face ID, Facebook (face tagging), Google Photos, airport security
Audio & Video Surveillance	Home security cameras, public CCTV, facial recognition in crowds	Security cameras, AI recognition software	Inferences: Daily activities, people you interact with, home security risk assessment	Ring (Amazon), Google Nest, city surveillance systems
Employment & Professional Data	Job title, workplace, salary (sometimes inferred), career history, skills, performance reviews	LinkedIn, job boards, HR systems	Inferences: Industry influence, job-switching likelihood, salary negotiation potential	LinkedIn, HR databases, recruitment platforms

Expanded Inferences from Location History

Type of Movement	Possible Inferences
Regular visits to the same place at night	Home address
8+ hours at a location daily	Likely workplace
Frequent visits to high-end areas	Higher income level
Shopping at budget stores vs. luxury brands	Economic class & spending habits
Visits to medical facilities	Health conditions (e.g., therapy, specialist visits)
Travel patterns (international vs. domestic)	Wealth, job type (remote worker, frequent business traveler)
Visits to gyms, parks, yoga studios	Interest in fitness & wellness lifestyle
Attendance at political rallies or religious places	Political and religious affiliations
Frequent late-night movement	Nightlife habits, party lifestyle
Rare movement outside home	Work-from-home status, possible health concerns

Why Does This Matter?

Many of these inferences aren’t explicitly collected but are derived from the data.
Advertisers, governments, and companies use this data for targeted marketing, predictive behavior models, and surveillance.
Some of this data can be used to manipulate user behavior, including showing tailored political ads, influencing spending habits, or even predicting life events (like pregnancy or divorce).