{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T03:17:19Z","timestamp":1774667839671,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T00:00:00Z","timestamp":1743292800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2014Next Generation EU","award":["P2022MCYCK"],"award-info":[{"award-number":["P2022MCYCK"]}]},{"name":"European Union\u2014Next Generation EU","award":["CUP D53D23022340001"],"award-info":[{"award-number":["CUP D53D23022340001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>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\u2014Political Popularity Campaign\u2014which 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.<\/jats:p>","DOI":"10.3390\/computers14040126","type":"journal-article","created":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T01:59:36Z","timestamp":1743386376000},"page":"126","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Analyzing Digital Political Campaigning Through Machine Learning: An Exploratory Study for the Italian Campaign for European Union Parliament Election in 2024"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7614-7154","authenticated-orcid":false,"given":"Paolo","family":"Sernani","sequence":"first","affiliation":[{"name":"Department of Law, University of Macerata, Piaggia dell\u2019Universit\u00e0 2, 62100 Macerata, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9628-720X","authenticated-orcid":false,"given":"Angela","family":"Cossiri","sequence":"additional","affiliation":[{"name":"Department of Law, University of Macerata, Piaggia dell\u2019Universit\u00e0 2, 62100 Macerata, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9786-2189","authenticated-orcid":false,"given":"Giovanni","family":"Di Cosimo","sequence":"additional","affiliation":[{"name":"Department of Law, University of Macerata, Piaggia dell\u2019Universit\u00e0 2, 62100 Macerata, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8893-9244","authenticated-orcid":false,"given":"Emanuele","family":"Frontoni","sequence":"additional","affiliation":[{"name":"Department of Political Sciences, Communication and International Relations, University of Macerata, Via Don Minzoni 22\/A, 62100 Macerata, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"197379","DOI":"10.1109\/ACCESS.2020.3034983","article-title":"A survey on computational politics","volume":"8","author":"Haq","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Singh, L., Kamboj, S., Kaur, T., and Singh, P. 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