Causal discovery in social media using quasi-experimental designs

By Hüseyin Oktay, Brian J. Taylor, David D. Jensen. 2010

75 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Information Systems

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Yet, different such strategies may lead to different degrees of similarity among the treated users and the control group, and, thus, to different results [Oktay et al. 2010].”
Using matched samples to estimate the effects of exercise on mental health from twitter (2015, background): “Recent work has performed controlled experiments (Kohavi et al. 2009) and quasi-experiments (Oktay, Taylor, and Jensen 2010) on social media systems, though not for health studies, and not with experimental variables inferred from text.”
Steering user behavior with badges (2013, unknown): “[18] use quasi-experimental designs to support causal claims in the context of badges.”
Design lessons from the fastest q&a site in the west (2011, methodology): “Prior work that uses the Stack Overflow data set conducted quasi-experiments about impacts of design decisions through post-hoc database analyses [22]; and considered Stack Overflow as an example of a two-sided market [18].”
Video stream quality impacts viewer behavior: inferring causality using quasi-experimental designs (2012, methodology): “In particular, we use a specific type of QED called the matched design [18] where a treated individual (in our case, a view or viewer) is randomly matched with an untreated individual, where both individuals have identical values for the confounding variables.”
Discovering value from community activity on focused question answering sites: a case study of stack overflow (2012, background): “studied the dynamics of Stack Overflow an swerer arrivals [18] with a focus on demonstrating the use of several quasi-experimental designs to establish causal relat ionships in social media.”