Gender perspectives and issue attention in the Italian digital arena
Speaker: (opens in a new window)Silvia Decadri (Università degli Studi di Milano)
Wednesday, November 16, 14:00–14:45 (Irish time)
Please register (opens in a new window)here to receive the link and password to the online meeting and information on the room at UCD.
Abstract: Political parties are often described as gendered institutions where power is distributed to women’s detriment. We test this claim by shifting attention from the real to the digital political arena, to explore whether the net is a friendlier environment for female politicians, or if it reproduces the same biases and stereotypes they encounter in the real world. Relying on a novel dataset on Italian parliamentarians' tweets from January 2020 to February 2022, we use structural topic models (STM) to compare male and female politicians’ issue-attention across a variety of politically relevant topics. Our STM model shows that women tweet on a wide range of topics, thus preventing the development of a systematic gender gap in issue attention. We do, instead, detect differences in topical content, which signal women's intention to bring their own perspective when discussing the same topics addressed by men. Thus, as for users’ ability to address different topics and to highlight their own perspectives, Twitter seems a friendlier environment for female politicians compared to the “real world” political arena, where gender gaps in issue attention are still detectable.
About the speaker: Silvia Decadri is a postdoctoral researcher at the University of Milan. Previously, she was a PhD student and IRC Scholar at Trinity College Dublin and a Postdoctoral researcher at the University of Zurich. She holds a B.A. in Political Science from UniMI, and a MSc in Economics from UCSC. She has worked as a research analyst, both in the public and in the private sector. Her main research interests include legislative behaviour, party politics, political communication, natural language processing and text analysis.