“But the data is already public”: on the ethics of research in Facebook

By Michael T. Zimmer. 2010

888 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Communication, Sociology and Political Science

How researchers see this paper

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

In Artificial Intelligence

In Communication

In Sociology and Political Science

Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Studies that leverage data from millions of social media users often do it without any kind of consent from them (Zimmer, 2010; Hutton and Henderson, 2015a).”
Privacy Protection for Natural Language Records: Neural Generative Models for Releasing Synthetic Twitter Data (2016, background): “We find that redaction maintains high utility for simple tasks but at the cost of high risk, while some neural synthesis models are able to produce higher levels of utility, even for more complicated tasks, while maintaining lower levels of risk.”
Studying Facebook via data extraction: the Netvizz application (2013, background): “Recent debates amongst Internet researchers [20] have tended to put emphasis on the question of individual privacy.”
The Brexit Botnet and User-Generated Hyperpartisan News (2019, background): “Although the information collected for this study is public, there are important ethical issues associated with harvesting public Twitter accounts (Zimmer, 2010).”
Accepting the challenges of social media research (2015, background): “Whilst this was not the first time that a study of Facebook spiked ethical concerns – see Zimmer (2010) for another example – the use of an experimental setting in this case has led to some new concerns.”
MOOCs: A systematic study of the published literature 2008-2012 (2013, neutral): “This may well be due to a lack of good guidance and best practice examples on the general question of the use of data from online social media and similar sources for such research (Zimmer, 2010).”
Fairness in Machine Learning: A Survey (2020, background): “Understanding “related” variables in a general sense is well studied, especially in the privacy and data archiving literature, where overlooked variable relationships can enable the deanonymiza-tion of published data (see Reference [318]).”
Ethical Decision-Making and Internet Research: Version 2.0 Recommendations from the AoIR Ethics Working Committee (2012, neutral): “ 17 See for example Zimmer (2010).”
Smart, Responsible, and Upper Caste Only: Measuring Caste Attitudes through Large-Scale Analysis of Matrimonial Profiles (2019, background): “Also, previous releases of anonymized data have been deanonymized, putting users at possible risk (Zimmer 2010).”