Human migration is a constant phenomenon in human history, and its study involves numerous research fields. To date, data not typically used for studying migration are increasingly available. These include the so-called social Big Data: digital traces left by humans through cell phones, online social networks, and online services. More and more technologies can be employed to extract information from these large datasets. However, how can Big Data help to understand the migration phenomenon?

To date, both traditional and novel models and data are employed to understand the mechanisms of the different stages of migration (the journey, the stay, and the return).

The journey: migration flows and stocks

Tracking international migrants' flows and stocks is a task as important as it is challenging. Researchers and policymakers relying on traditional data sources such as official statistics or administrative data often meet various limitations. These limitations are typically due to the involvement of various nations in the migration process; i.e., data may be inconsistent across different countries' databases. While traditional data are useful to study the journey of migrants, social Big Data may help researchers to overcome the limitations of traditional data and may allow in real-time analyses (see for instance, [1,2,3]).

The use of social Big Data to study the immigrants' journey is increasing. Various data types fall under this category; between these, Twitter data, Skype Ego networks, Google Trend Index (GTI) [4], LinkedIn data [5], publications in academic journals [6], ORCID data^[1], and long-term origin-destination data. For instance, Twitter data can be used to quantify diversity in communities [7] and estimate user nationality; Skype Ego networks data can be used to explain international migration patterns [8].

As well as traditional data, unconventional Big Data has its limitations, including bias and privacy issues. Thus, new methods are developing to address issues and take advantage of the almost worldwide data coverage. The hope is that merging knowledge from both traditional and novel datasets may lead to overcoming issues and building more and more accurate models to nowcast immigrants’ journeys and immigration rates.

The stay: effects on communities, immigrant integration

The study of immigrants' integration and the effect of migration on the communities is complex and challenging. Integration and cultural changes have been traditionally analyzed using census data, administrative registries, and surveys.

Integration has been analyzed from multiple viewpoints, including marriage relationships [9,10], social relations [11], labor market [12], and language adoption [13]. On the other side, educational expectations [14,15], economic prosperity [16], cultural distance with the origin country, school class composition [17], and ethnic attitudes are used to study the effects on the local population due to integration.

As for the journey, Big Data can help to analyze the stay producing real-time results. Several works have been done using Call Detail Records (CDRs) in understanding individual [18] and group mobility [19], even during environmental disasters [20]. These data can be used to describe social interaction, mobility, and segregation. However, CDR may lead to coverage issues when analyzing international migration flows.

Retail data, such as those from a supermarket chain, may help understand how immigrants adopt habits and whether they are converging to or diverging from the norms of the destination country [21].

Also, Online Social Networks (OSNs) data, for instance, can help study social integration looking at the opinions of the locals related to migration topics. The language used on OSNs can be used to depict the worldwide linguistic geography [22], detect linguistic variation [23], identify patterns in language usage, analyze the language diversity [7], changes in the local language, and sentiment towards immigrants [24, 25].

The return: migrants returning to the country of origin

Migration can also be considered as a temporary phenomenon. Since return migration is increasing in several countries, it has been extensively investigated wrt different aspects, such as decreasing violence [29].

Together with factors involved in the decision of return, scholars also investigated the benefits that return migration brings to the countries of origin. Advantages fall in various fields. Economically, new skills learned abroad may help returned migrants to start their new one business in the origin country; and, the money sent from migrants to their families is a valuable incoming [30,31]. Benefits also affect educational attainment and health conditions. For instance, regarding education, return migrants can be associated with increases and improvement of educational attainment [34], and social practices introduced by return migrants positively affect healthcare [35].

Other studies [28, 36, 37] focuses on electoral participation. These works suggest that local policies are typically positively affected by returning migrants since they contribute to increase political participation and enhance political accountability.

Especially in recent times, much of the research has focused on the relationship between return migration and personal skills. In particular, researchers investigated the “brain gain'” provided by the return of high-skilled individuals, such as scientists returning in the origin country [32, 33].

Discussion

Human Migration can be studied following three lines of research. Today social Big Data can complement existing approaches, but these models still need to be validated and refined. The issue is the lack of gold standards as exact current immigration rates with which to validate nowcasting models. The hope is that better relations between policies and immigration could be a breakthrough in solving this problem.

On the other hand, research needs to consider issues with the data that is being used, be it traditional or unconventional. An additional issue relies on the ethical dimension of collecting and processing personal data, including sensitive personal data, describing human individuals and activities.

Now more than ever, collecting, preprocessing, and analyzing data need to be managed with ethical and legal values such as privacy and data protection. The context of migration is sensitive to the ethics dimension since individuals described in the data may be particularly vulnerable.

Principal reference:

Sîrbu, A., Andrienko, G., Andrienko, N., Boldrini, C., Conti, M., Giannotti, F., … & Pappalardo, L. (2020). Human migration: the big data perspective. International Journal of Data Science and Analytics, 1-20. https://link.springer.com/article/10.1007/s41060-020-00213-5

Written by:

Laura Pollacci: laura.pollacci@isti.cnr.it

Matteo Bohm: bohm@diag.uniroma1.it

References

1. Zagheni, E., Garimella, V.R.K., Weber, I., et al.: Inferring international and internal migration patterns from Twitter data. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 439–444. ACM(2014)
2. Hawelka, B., Sitko, I., Beinat, E., Sobolevsky, S., Kaza-kopoulos, P., Ratti, C.: Geo-located Twitter as proxy for global mobility patterns. Cartography and Geographic Information Science 41(3), 260–271 (2014)
3. Moise, I., Gaere, E., Merz, R., Koch, S., Pournaras, E.:Tracking language mobility in the Twitter landscape.In: Data Mining Workshops (ICDMW), 2016 IEEE 16th International Conference on, pp. 663–670. IEEE (2016)
4. Bohme, M.H., Groger, A., St ̈ohr, T.: Searching for a better life: Predicting international migration with online search keywords. Journal of Development Economics p.102347 (2019)
5. Li, L., Jing, H., Tong, H., Yang, J., He, Q., Chen,B.C.: Nemo: Next career move prediction with contextual embedding. In: Proceedings of the 26th International Conference on World Wide Web Companion, WWW ’17 Companion, pp. 505–513. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland (2017). DOI 10.1145/3041021.3054200. URL https://doi.org/10.1145/3041021.3054200
6. James, C., Pappalardo, L., Sirbu, A., Simini, F.: Prediction of next career moves from scientific profiles. ArXive-prints (2018)
7. Pollacci, L., Siırbu, A., Giannotti, F., Pedreschi, D.:Measuring the Salad Bowl: Superdiversity on Twitter (2020). Under Submission
8. Kikas, R., Dumas, M., Saabas, A.: Explaining international migration in the skype network: The role of social network features. In: Proceedings of the 1st ACM Workshop on Social Media World Sensors, pp. 17–22. ACM (2015)
9. Qian, Z., Glick, J.E., Batson, C.D.: Crossing boundaries: Nativity, ethnicity, and mate selection. Demography 49(2), 651–675 (2012)
10. Qian, Z., Lichter, D.T.: Social boundaries and marital assimilation: Interpreting trends in racial and ethnic intermarriage. American Sociological Review 72(1), 68–94 (2007)
11. McKenzie, D., Rapoport, H.: Self-selection patterns in Mexico-US migration: the role of migration networks.The Review of Economics and Statistics 92(4), 811–821(2010)
12. Barra, A., Contucci, P., Sandell, R., Vernia, C.: An analysis of a large dataset on immigrant integration in spain.the statistical mechanics perspective on social action.Scientific reports 4, 4174 (2014)
13. Van Tubergen, F., Kalmijn, M.: A Dynamic Approach To the Determinants of Immigrants’ Language Proficiency: The United States, 1980–2000 1. International Migration Review 43(3), 519–543 (2009)
14. Minello, A.: The educational expectations of Italian children: the role of social interactions with the children of immigrants. International studies in sociology of education 24(2), 127–147 (2014)
15. Minello, A., Barban, N.: The educational expectations of children of immigrants in Italy. The ANNALS of the American Academy of Political and Social Science 643(1), 78–103 (2012)
16. Alesina, A., Harnoss, J., Rapoport, H.: Birthplace diversity and economic prosperity. Journal of Economic Growth 21(2), 101–138 (2016)
17. Bubritzki, S., van Tubergen, F., Weesie, J., Smith, S.:Ethnic composition of the school class and interethnic attitudes: a multi-group perspective. Journal of Ethnic And Migration Studies 44(3), 482–502 (2018)
18. Lu, X., Bengtsson, L., Holme, P.: Predictability of population displacement after the 2010 Haiti earthquake. Proceedings of the National Academy of Sciences 109(29), 11576–11581 (2012). URL: http://www.pnas.org/content/109/29/11576
19. Pappalardo, L., Pedreschi, D., Smoreda, Z., Giannotti,F.: Using big data to study the link between human mobility and socio-economic development. In: 2015 IEEE International Conference on Big Data (Big Data), pp.871–878 (2015). DOI 10.1109/BigData.2015.7363835
20. Giannotti, F., Nanni, M., Pedreschi, D., Pinelli, F.,Renso, C., Rinzivillo, S., Trasarti, R.: Unveiling the complexity of human mobility by querying and mining massive trajectory data. The VLDB Journal—The International Journal on Very Large Databases 20(5),695–719 (2011)
21. Guidotti, R., Monreale, A., Nanni, M., Giannotti, F.,Pedreschi, D.: Clustering individual transactional data for masses of users. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 195–204. ACM (2017)
22. Nguyen, D., Dogruoz, A.S., Rose, C.P., de Jong, F.:Computational sociolinguistics: A survey. Computational Linguistics 42(3), 537–593 (2016)
23. Kulkarni, V., Perozzi, B., Skiena, S.: Freshman re Fresher? Quantifying the Geographic Variation of Language in Online Social Media. In: ICWSM, pp. 615–618(2016)
24. Coletto, M., Esuli, A., Lucchese, C., Muntean, C.I., Nar-dini, F.M., Perego, R., Renso, C.: Sentiment-enhanced multidimensional analysis of online social networks: Perception of the mediterranean refugees crisis. In: 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016,San Francisco, CA, USA, August 18-21, 2016, pp. 1270–1277 (2016)
25. Coletto, M., Esuli, A., Lucchese, C., Muntean, C.I., Nar-dini, F.M., Perego, R., Renso, C.: Perception of social phenomena through the multidimensional analysis of online social networks. Online Social Networks and Media 1, 14 – 32 (2017). DOI https://doi.org/10.1016/j.osnem.2017.03.001. URL: http://www.sciencedirect.com/science/article/pii/S246869641630009X
26. Wahba, J.: Selection, selection, selection: the impact of return migration. Journal of Population Economics 28(3), 535–563 (2015)
27. Wahba, J., Zenou, Y.: Out of sight, out of mind: Migration, entrepreneurship and social capital. Regional Science and Urban Economics 42(5), 890–903 (2012)
28. Chauvet, L., Mercier, M.: Do return migrants transfer political norms to their origin country? Evidence from Mali. Journal of Comparative Economics 42(3), 630–651 (2014)
29. Bucheli, J.R., Fontenla, M., Waddell, B.J.: Return migration and violence. World Development 116, 113–124(2019)
30. Mesnard, A.: Temporary migration and capital market imperfections. Oxford economic papers 56(2), 242{262} (2004)
31. Mesnard, A., et al.: Temporary migration and self employment: evidence from tunisia. Brussels Economic Review 47(1), 119{138} (2004)
32. Docquier, F., Rapoport, H.: Globalization, brain drain, and development. Journal of Economic Literature 50(3), 681{730} (2012)
33. Wahba, J., Zenou, Y.: Out of sight, out of mind: Migration, entrepreneurship and social capital. Regional Science and Urban Economics 42(5), 890{903 (2012)
34. Montoya Arce, J., Salas Alfaro, R., Soberon Mora, J.A.: La migracion de retorno desde Estados Unidos hacia el Estado de Mexico: oportunidades y retos. Cuadernos Geogracos (2011)
35. Levitt, P., Lamba-Nieves, D.: Social remittances revisited. Journal of Ethnic and Migration Studies 37(1), 1{22} (2011)
36. Chauvet, L., Mercier, M.: Do return migrants transfer political norms to their origin country? Evidence from Mali. Journal of Comparative Economics 42(3), 630{651} (2014)
37. Batista, C., Vicente, P.C.: Do migrants improve governance at home? Evidence from a voting experiment. The World Bank Economic Review 25(1), 77{104 (2011