Combining Natural Language Processing and Statistical Methods to Assess Gender Gaps in the Mediated Personalization of Politics

Abstract

The media attention to the personal sphere of famous and important individuals has become a key element of the gender narrative. In this setting, we aim at assessing gender gaps in the mediated personalization of a wide range of political office holders in Italy during the period 2017–2020 by means of a combination of NLP and statistical methods. The proposed analysis hinges on the definition of a new score for each word in the corpus that adjusts the incidence rate for the under representation of women in politics. On this basis, evidence is found that political personalization in Italy is more detrimental for women than it is for men, with the persistence of entrenched stereotypes including a masculine connotation of leadership, the resulting women’s unsuitability to hold political functions, and a greater deal of focus on their attractiveness and body parts. In addition, women politicians are covered with a more negative tone than their men counterpart when personal details are reported. By distinguishing between different types of media, we also show that the observed gender differences are primarily found in online news rather than print news. This suggests that the expression of certain stereotypes may be favored when click baiting and personal targeting have a major impact.