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Young Adult Unemployment Through the Lens of Social Media: Italy as a Case Study

Part of the Lecture Notes in Computer Science book series (LNISA,volume 12467)

Abstract

Youth unemployment rates are still in alerting levels for many countries, among which Italy. Direct consequences include poverty, social exclusion, and criminal behaviours, while negative impact on the future employability and wage cannot be obscured. In this study, we employ survey data together with social media data, and in particular likes on Facebook Pages, to analyse personality, moral values, but also cultural elements of the young unemployed population in Italy. Our findings show that there are small but significant differences in personality and moral values, with the unemployed males to be less agreeable while females more open to new experiences. At the same time, unemployed have a more collectivist point of view, valuing more in-group loyalty, authority, and purity foundations. Interestingly, topic modelling analysis did not reveal major differences in interests and cultural elements of the unemployed. Utilisation patterns emerged though; the employed seem to use Facebook to connect with local activities, while the unemployed use it mostly as for entertainment purposes and as a source of news, making them susceptible to mis/disinformation. We believe these findings can help policymakers get a deeper understanding of this population and initiatives that improve both the hard and the soft skills of this fragile population.

Keywords

  • Personality traits
  • Moral foundations
  • Human values
  • Inequalities
  • Social media
  • Facebook
  • Unemployment
  • NLP
  • Topic modeling

A. Urbinati and K. Kalimeri—These authors contributed equally to this work.

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Fig. 1.
Fig. 2.
Fig. 3.

Notes

  1. 1.

    https://ec.europa.eu/social/main.jsp?catId=1036.

  2. 2.

    Facebook Pages, are assigned to one of the predefined Facebook categories. Link to the full list of Facebook Categories: https://www.facebook.com/pages/category/.

  3. 3.

    Figure 4 in SI provides a clear picture of the geographical distribution per region as compared to the expected values with respect to the census.

  4. 4.

    Due to space limitations the full topic descriptions are presented in the Appendix, please refer to Fig. 5 for the Italian and English languages respectively.

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Correspondence to Alessandra Urbinati or Kyriaki Kalimeri .

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A Appendix

A Appendix

Fig. 4.
figure 4

Geographic distribution of the population in the Complete Dataset (dark-coloured bars) as compared to the expected population distribution according to the official census data per region (light-coloured bars).

Fig. 5.
figure 5

The ten topics according to the NMF algorithm with the most contributing words per topic, for the Italian (first two columns) and the English language (last two columns).

Table 5. Logistic regression models predicting qualification using demographic (D), Big Five (B5), Moral Foundation (MFT), Political Opinion (PO). The Table shows alongside their coefficient estimate and their corresponding p-values (Bonferroni-adjusted). Confidence levels: p < 0.001 ***, p < 0.01 **, p < 0.05 *.

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Urbinati, A., Kalimeri, K., Bonanomi, A., Rosina, A., Cattuto, C., Paolotti, D. (2020). Young Adult Unemployment Through the Lens of Social Media: Italy as a Case Study. In: , et al. Social Informatics. SocInfo 2020. Lecture Notes in Computer Science(), vol 12467. Springer, Cham. https://doi.org/10.1007/978-3-030-60975-7_28

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