The connection between online disinformation and crime is a topic of significant i nterest. A n a spect o f t his topic, with strong research potential, is the causal relationship between crimes committed in the real world and disinformation spreading campaigns in the digital world, often on social media platforms. In essence, this research paper focuses on exploring said causal relationship, by seeking to establish a correlation between the diffusion of disinformation, online, and crimes committed offline; specifically, h ate c rimes. F or t his p urpose, a n ovel method was employed: using robust machine learning algorithms for time-series predictions, in order to reveal causal pathways that traditional quantitative techniques may be unable to capture. Thus, the research conducted here exhibits AI applications in social science research and, at the same time, provides a greater understanding of the link between online disinformation and offline crime.
Lo Giudice, M., Yazdi, A., Aziani, A., Evangelatos, S., Gousetis, N., Nikolopoulos, C. (2024). Informative (Dis)information: Exploring the Correlation Between Social Media Disinformation Campaigns and Real-World Criminal Activity. In EEITE 2024 - Proceedings of 2024 5th International Conference in Electronic Engineering, Information Technology and Education (pp.1-6). Institute of Electrical and Electronics Engineers Inc. [10.1109/EEITE61750.2024.10654415].
Informative (Dis)information: Exploring the Correlation Between Social Media Disinformation Campaigns and Real-World Criminal Activity
Aziani A.;
2024
Abstract
The connection between online disinformation and crime is a topic of significant i nterest. A n a spect o f t his topic, with strong research potential, is the causal relationship between crimes committed in the real world and disinformation spreading campaigns in the digital world, often on social media platforms. In essence, this research paper focuses on exploring said causal relationship, by seeking to establish a correlation between the diffusion of disinformation, online, and crimes committed offline; specifically, h ate c rimes. F or t his p urpose, a n ovel method was employed: using robust machine learning algorithms for time-series predictions, in order to reveal causal pathways that traditional quantitative techniques may be unable to capture. Thus, the research conducted here exhibits AI applications in social science research and, at the same time, provides a greater understanding of the link between online disinformation and offline crime.File | Dimensione | Formato | |
---|---|---|---|
Lo Giudice-2024-EEITE-VoR.pdf
Solo gestori archivio
Tipologia di allegato:
Publisher’s Version (Version of Record, VoR)
Licenza:
Tutti i diritti riservati
Dimensione
526.75 kB
Formato
Adobe PDF
|
526.75 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.