Demographic research with non-representative internet data

By Emilio Zagheni, Ingmar Weber. 2015

101 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. Fernando Diaz
4. Emre Kıcıman

In Sociology and Political Science

1. Alexandra Olteanu
2. Fernando Diaz
3. Carlos Castillo
4. Emre Kıcıman
5. H. A. Schwartz
6. Dirk Hovy
7. Gabriella Kazai
8. Ricardo A. Baeza-Yates

In Transportation

1. Alexandra Olteanu
2. Carlos Castillo
3. Fernando Diaz
4. Emre Kıcıman
5. Olga Russakovsky
6. Angelina Wang
7. Ricardo A. Baeza-Yates

Citations in the corpus

• Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, methodology): “These efforts include employing techniques from the causal inference literature that can lead to more robust research results (Landeiro and Culotta, 2016; Proserpio et al., 2016), or calibrating non-representative social data samples (Zagheni and Weber, 2015).”
• Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview (2019, background): “Zagheni and Weber (2015), studied the use of Internet Data for demographic studies and proposed two approaches to reduce the selection bias in their task.”