Paper in a nutshell:

Working papers:

"Visual Bias" [Working Paper(Revise and resubmit, Econometrica)

 ["Best JM paper award" by Unicredit & EEA, "Marco Fanno" best paper award]      


I study the non-verbal language of leading pictures in online news and its influence on readers’ opinions. I develop a visual vocabulary and use a dictionary approach to analyze around 300,000 photos published in US news in 2020. I document that the visual language of US media is politically partisan and significantly polarised. I then demonstrate experimentally that the news’ partisan visual language is not merely distinctive of outlets’ ideological positions, but also promotes them among readers. In a survey experiment, identical articles with images of opposing partisanships induce different opinions, tilted towards the pictures’ ideological poles. Moreover, as readers react more to images aligned with their viewpoint, the news’ visual bias causes issue polarization to increase. Finally, I find that media can effectively slant readers using neutral texts and partisan pictures: this result calls for the inclusion of image scrutiny in news assessments and fact checking, today largely text-based.

Work in progress:

On the political economy of media & AI:

"Strategic choice of pictures and text in press coverage" , with Andrea Mattozzi 

"Dynamic competition and the production of news" , with Paula Onuchic

"Filtered Transmission: the Impact of Media Bias on Central Bank Signals" , with Ryan Rholes

"Algorithmic Social Norms" 

On social norms & health:

"Media coverage and norms around prosocial behavior".  

"What drives prosocial outcomes? A field experiment on registrations as organ donors".  

[on hold] (Awarded the EIEF 2020 Grant

Project description: 

I study the role of cognitive and non-cognitive factors in the take-up of pro-social behaviour, focusing on registrations to posthumously donate organs.