Inferring international and internal migration patterns from Twitter data

By Emilio Zagheni, Venkata Rama Kiran Garimella, Ingmar Weber, Bogdan State. 2014

212 citations recorded by the source corpus.

Subfields: Epidemiology, Sociology and Political Science, Transportation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Epidemiology

1. Alexandra Olteanu
2. Carlos Castillo
3. Emre Kıcıman
4. Fernando Diaz
5. Emilio Zagheni
6. Ingmar Weber

In Sociology and Political Science

1. Alexandra Olteanu
2. Fernando Diaz
3. Carlos Castillo
4. Emre Kıcıman
5. Ingmar Weber
6. Emilio Zagheni
7. Daniel Gatica-Perez
8. Krishna P. Gummadi

In Transportation

1. Alexandra Olteanu
2. Carlos Castillo
3. Emre Kıcıman
4. Fernando Diaz
5. Emilio Zagheni
6. Ingmar Weber
7. Krishna P. Gummadi
8. Olga Russakovsky

Citations in the corpus

• Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, methodology): “…bias, under strong assumptions; including matched analysis (De Choudhury et al., 2016; Sharma and Cosley, 2016), instrumental variables analysis (Sharma et al., 2015), regression discontinuities (Malik and Pfeffer, 2016), and differences-in-differences (Carmi et al., 2012; Zagheni et al., 2014).”
• Characterizing the Demographics Behind the #BlackLivesMatter Movement (2015, background): “Looking at organizations, accounting for about 5% of profiles, we see a 3 times higher tweeting rate than for individuals.”
• The Misgendering Machines (2018, background): “" [108] Despite these hints of how gender is operationalised, it is worth pointing out that none of the papers made their operationalisation explicit.”
• Distilling the Outcomes of Personal Experiences: A Propensity-scored Analysis of Social Media (2017, background): “Recent studies looking at migration patterns [99], shifts in suicidal ideation [28], at the effect of exercise on mental health [31], or of community feedback on individual user behavior [21] try to improve on correlational analyses by applying causal inference techniques that have come into extensi”
• Demographic research with non-representative internet data (2015, methodology): “Zagheni et al. (2014) used geolocated Twitter data to document the recent rise of out-migration rates in countries like Ireland, and to show the potential of digital records to bridge the gap between the study of international and internal migration.”
• Leveraging Facebook's Advertising Platform to Monitor Stocks of Migrants (2017, background): “…that leverage innovative Web data sources, like geo-located e-mail logins (Zagheni and Weber 2012; State, Weber, and Za-gheni 2013), Twitter tweets (Zagheni et al. 2014; Hawelka et al. 2014), and LinkedIn profiles (State et al. 2014), have expanded our ability to estimate migration rates and flows.”
• Combining Social Media and Survey Data to Nowcast Migrant Stocks in the United States (2020, background): “Recent efforts have focused on using data from social media and networking websites such as Twitter, LinkedIn and, more recently, Facebook and Instagram.”