2022
Journal article  Open Access

Where do migrants and natives belong in a community: a Twitter case study and privacy risk analysis

Kim J., Pratesi F., Rossetti G., Sîrbu A., Giannotti F.

International migration  Community detection  Social network  Twitter  Privacy risk assessment  GDPR 

Today, many users are actively using Twitter to express their opinions and to share information. Thanks to the availability of the data, researchers have studied behaviours and social networks of these users. International migration studies have also benefited from this social media platform to improve migration statistics. Although diverse types of social networks have been studied so far on Twitter, social networks of migrants and natives have not been studied before. This paper aims to fill this gap by studying characteristics and behaviours of migrants and natives on Twitter. To do so, we perform a general assessment of features including profiles and tweets, and an extensive network analysis on the network. We find that migrants have more followers than friends. They have also tweeted more despite that both of the groups have similar account ages. More interestingly, the assortativity scores showed that users tend to connect based on nationality more than country of residence, and this is more the case for migrants than natives. Furthermore, both natives and migrants tend to connect mostly with natives. The homophilic behaviours of users are also well reflected in the communities that we detected. Our additional privacy risk analysis showed that Twitter data can be safely used without exposing sensitive information of the users, and minimise risk of re-identification, while respecting GDPR.

Source: Social Network Analysis and Mining 13 (2022). doi:10.1007/s13278-022-01017-0

Publisher: Springer, Vienna


Metrics



Back to previous page
BibTeX entry
@article{oai:it.cnr:prodotti:475957,
	title = {Where do migrants and natives belong in a community: a Twitter case study and privacy risk analysis},
	author = {Kim J. and Pratesi F. and Rossetti G. and Sîrbu A. and Giannotti F.},
	publisher = {Springer, Vienna},
	doi = {10.1007/s13278-022-01017-0},
	journal = {Social Network Analysis and Mining},
	volume = {13},
	year = {2022}
}

SoBigData-PlusPlus
SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics


OpenAIRE