Analyzing Digital Political Campaigning Through Machine Learning: An Exploratory Study for the Italian Campaign for European Union Parliament Election in 2024
Abstract
:1. Introduction
- Analysis of Micro-targeting Practices: The study delves into the use of micro-targeting on social media platforms, where tailored messages are delivered to specific voter segments. While this enhances engagement, it also raises ethical and legal questions, including the dissemination of misleading information and the reinforcement of echo chambers.
- Assessment of Social Media Dynamics: By analyzing data from major social media networks, the paper provides insights into how interactions on these platforms define modern voter–representative relationships.
- Introduction of a Novel Dataset and Predictive Modeling: The research introduces a new dataset comprising social media posts from the 2024 political elections, including post content, engagement metrics, and temporal data. By applying machine learning techniques, the study aims to predict variables such as post popularity and assess their influence on campaign outcomes, offering a nuanced understanding of how digital content resonates with the electorate.
2. Related Works
2.1. Political Campaign Analysis
2.2. Machine Learning Analysis
3. Materials and Methods
3.1. Data Acquisition
- Più Europa
- Italia Viva
- Azione
- Lega
- Fratelli d’Italia
- Forza Italia
- Bonaccini
- Movimento 5 Stelle
- Alleanza
- Elly
- Partito Democratico
3.2. Data Processing and Aggregation
- Low popularity: value from 0 to 1000,
- Medium popularity: value from 1000 to 5000,
- High popularity: value above 5000.
3.3. Machine Learning Framework
- Accuracy:
- Precision:
- Recall:
- F1-score:
- Matthews correlation coefficient (MCC):
- Area under the curve (AUC):
4. Results
4.1. Experimental Setting
4.2. Obtained Results
5. Discussions and Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
EU | European Union |
ML | Machine Learning |
AI | Artificial Intelligence |
CNN | Convolutional Neural Network |
ViT | Vision Transformer |
SVM | Support Vector Machine |
GB | Gradient Boosting |
MLP | Multilayer Perceptron |
MCC | Matthews Correlation Coefficient |
AUC | Area Under the Curve |
LR | Logistic Regression |
TP | True Positive |
TN | True Negative |
FP | False Positive |
FN | False Negative |
KPI | Key Performance Indicator |
API | Application Programming Interface |
CS | Computer Science |
GPU | Graphics Processing Unit |
RTX | Ray Tracing Texel eXtreme |
APIFY | API-based Web Scraping Framework |
URL | Uniform Resource Locator |
FC | Fully Connected |
CCDCOE | NATO Cooperative Cyber Defence Centre of Excellence |
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Field | Description |
---|---|
image | The image associated with the post. |
caption | The text description accompanying the Instagram post. |
timestamp | The date and time when the post was published. |
likesCount | The number of likes the post received. |
commentsCount | The number of comments on the post. |
shareCount | The number of times the post was shared. |
hashtags | The hashtags used in the post. |
type | The type of media (image, video, carousel, etc.). |
url | The direct link to the post. |
videoViewCount | The number of views on the video. |
locationName | The name of the location tagged in the post. |
engagementRateSum | A calculated metric: (likes + comments + shares). |
Backbone | Classifier | Accuracy | AUC | F1 | Precision | Recall | MCC |
---|---|---|---|---|---|---|---|
Inception V3 | LR | 0.595 | 0.682 | 0.541 | 0.563 | 0.595 | 0.268 |
SVM | 0.595 | 0.687 | 0.558 | 0.578 | 0.595 | 0.276 | |
GB | 0.611 | 0.680 | 0.540 | 0.582 | 0.611 | 0.306 | |
MLP | 0.603 | 0.669 | 0.549 | 0.567 | 0.603 | 0.286 | |
VGG16 | LR | 0.627 | 0.756 | 0.566 | 0.634 | 0.627 | 0.339 |
SVM | 0.611 | 0.754 | 0.554 | 0.579 | 0.611 | 0.302 | |
GB | 0.611 | 0.688 | 0.546 | 0.626 | 0.611 | 0.311 | |
MLP | 0.643 | 0.786 | 0.574 | 0.745 | 0.643 | 0.385 | |
ResNet50 | LR | 0.587 | 0.671 | 0.538 | 0.535 | 0.587 | 0.258 |
SVM | 0.556 | 0.641 | 0.522 | 0.513 | 0.556 | 0.210 | |
GB | 0.627 | 0.710 | 0.578 | 0.623 | 0.627 | 0.336 | |
MLP | 0.595 | 0.672 | 0.547 | 0.543 | 0.595 | 0.274 | |
ViT-B16 | LR | 0.595 | 0.732 | 0.559 | 0.588 | 0.595 | 0.275 |
SVM | 0.603 | 0.739 | 0.577 | 0.592 | 0.603 | 0.298 | |
GB | 0.611 | 0.691 | 0.551 | 0.704 | 0.611 | 0.311 | |
MLP | 0.611 | 0.718 | 0.552 | 0.610 | 0.611 | 0.301 |
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Share and Cite
Sernani, P.; Cossiri, A.; Di Cosimo, G.; Frontoni, E. Analyzing Digital Political Campaigning Through Machine Learning: An Exploratory Study for the Italian Campaign for European Union Parliament Election in 2024. Computers 2025, 14, 126. https://doi.org/10.3390/computers14040126
Sernani P, Cossiri A, Di Cosimo G, Frontoni E. Analyzing Digital Political Campaigning Through Machine Learning: An Exploratory Study for the Italian Campaign for European Union Parliament Election in 2024. Computers. 2025; 14(4):126. https://doi.org/10.3390/computers14040126
Chicago/Turabian StyleSernani, Paolo, Angela Cossiri, Giovanni Di Cosimo, and Emanuele Frontoni. 2025. "Analyzing Digital Political Campaigning Through Machine Learning: An Exploratory Study for the Italian Campaign for European Union Parliament Election in 2024" Computers 14, no. 4: 126. https://doi.org/10.3390/computers14040126
APA StyleSernani, P., Cossiri, A., Di Cosimo, G., & Frontoni, E. (2025). Analyzing Digital Political Campaigning Through Machine Learning: An Exploratory Study for the Italian Campaign for European Union Parliament Election in 2024. Computers, 14(4), 126. https://doi.org/10.3390/computers14040126