Based on data from some 1,200 public accounts, the student trained his algorithm to identify certain personality traits and make connections in two categories: textual and non-textual information. In the first category, adjectives and word types used in biographies and texts published on the platform are analyzed. Each word will then be associated with a subcategory. For example, people who use words relating to vulnerability, social relationships, submission to authority or dependence on others will be associated with a conscientious nature.
Non-textual information is defined according to the number of friends, the number of companies where the person has worked during their career or their field. “Everything you do on social media gives some indication of personality, even not being active there every day,” said the 25-year-old researcher.
Thus, a person who has many friends, who posts very regularly on their newsfeed, who uses few punctuation marks and many adjectives will be considered an extrovert. Conversely, an introverted person, according to a study from the University of Sheffield, will tend to use punctuation marks more.
Currently, recruiters cannot use the algorithm in question because it is biased. “I have demonstrated that it was possible to detect personality on LinkedIn, but I selected users who had previously published their results on personality tests,” explains the student. “These are people who are very active on the network and are therefore not representative of the general population.”
It will therefore be necessary to carry out a new study on an unbiased population for the algorithm to be functional and accessible to recruiters, while ensuring that users’ privacy is protected.