Abstract
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.
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Notes
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Followers are users that follow a specific user and friends are users that a specific user follows. https://developer.twitter.com/en/docs/twitter-api/v1/accounts-and-users/follow-search-get-users/overview.
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Anagrafe degli italiani residenti all’estero (AIRE) is the Italian register data.
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Acknowledgment
This work was supported by the European Commission through the Horizon2020 European projects “SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics” (grant agreement no 871042) and “HumMingBird - Enhanced migration measures from a multidimensional perspective” (grant agreement no 870661).
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Kim, J., Sîrbu, A., Rossetti, G., Giannotti, F. (2022). Characterising Different Communities of Twitter Users: Migrants and Natives. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1072. Springer, Cham. https://doi.org/10.1007/978-3-030-93409-5_12
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