Análise do comportamento através dos dados coletados na internet

Abstract

This work presents an analysis of human behavior through data collected on the internet. They will be confirmed as Big Techs and the Cambridge Analytica case study. We show that digital records of behavior easily obtained, through likes, through Facebook can be used to automatically and accurately predict a range of highly confidential personal attributes, including: sexual orientation, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, separation from parents, age and sex. The based analysis is based on a data set of more than 58,000 volunteers who provided Facebook likes, detailed demographic profiles and the results of various psychometric tests. The proposed model uses dimensionality reduction to pre-process the tanned data, which is then inserted in linear regression to predict individual psych demographic profiles of tanned. The model correctly discriminates between homosexual and heterosexual men in 88% of cases, African-Americans and Caucasian Americans in 95% of cases, and between Democrats and Republicans in 85% of cases. For the personality trait "Aperture", prediction accuracy is close to the test-retest accuracy of a personality test pattern. We give examples of associations between attributes and likes and discuss it as a conclusion for online personalization and privacy.