Work

Analyzing Digital Political Campaigning Through Machine Learning: An Exploratory Study for the Italian Campaign for European Union Parliament Election in 2024

Year: 2025

Type: article

Abstract: The rapid digitalization of political campaigns has reshaped electioneering strategies, enabling political entities to leverage social media for targeted outreach. This study investigates the impact of digital political campaigning during the 2024 EU elections using machine learning techniques to analyze social media dynamics. We introduce a novel dataset—Political Popularity Campaign—which comprises social media posts, engagement metrics, and multimedia content from the electoral period. By applying predictive modeling, we estimate key indicators such as post popularity and assess their influence on campaign outcomes. Our findings highlight the significance of micro-targeting practices, the role of algorithmic biases, and the risks associated with disinformation in shaping public opinion. Moreover, this research contributes to the broader discussion on regulating digital campaigning by providing analytical models that can aid policymakers and public authorities in monitoring election compliance and transparency. The study underscores the necessity for robust frameworks to balance the advantages of digital political engagement with the challenges of ensuring fair democratic processes.

Source: Computers

DOI: 10.3390/computers14040126

Authors: Paolo Sernani, Angela Cossiri, Giovanni Di Cosimo, Emanuele Frontoni

Institution: University of Macerata

Language: English

Topic: Social Media and Politics

Subfield: Communication

Field: Social Sciences

Domain: Social Sciences

SDG: Peace, justice, and strong institutions

Open Access status: gold

Estimated APC paid: $2,172