Challenging Big Data for studying gender-based violence: a methodological proposal

Fiorenza Deriu (), Claudia Villante (), Maria Giuseppina Muratore (), Raffaella Gallo () and Edoardo Toppetti ()
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Fiorenza Deriu: “Sapienza” University of Rome
Claudia Villante: ISTAT-Italian National Institute of Statistics
Maria Giuseppina Muratore: ISTAT-Italian National Institute of Statistics
Raffaella Gallo: Sapienza University of Rome
Edoardo Toppetti: Ispra-European Media Monitornig Team

Quality & Quantity: International Journal of Methodology, 2025, vol. 59, issue 6, No 27, 5537-5555

Abstract: Abstract Violence against woman is a persistent problem affecting our society. The use of Big Data represents a new challenging opportunity to integrate official statistics with more updated information. Social media constitute, in fact, a particularly useful data source for analysing gender-based violence, cyber-violence and gender stereotypes. This report describes the results of a methodological approach, aimed at studying gender stereotypes, using textual data published on social media, showing which positive or negative effects may be generated in public opinion when certain messages are spread. Sentiment and emotion analysis has been carried out to measure how the phenomenon is represented among social network users. The statistical quality of the results was assessed. The proposal of a methodology to generate a linguistic resource aimed at improving the capacity of the machine learning BERT algorithm to classify social media data on gender stereotypes integrates the specific contribution of this study.

Keywords: Big data; Gender-based violence; Machine learning; Sentiment analysis; Text mining (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11135-025-02209-4

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