@incollection{StockmannTromble, author = {Stockmann, Daniela and Tromble, Rebekah}, title = {Lost Umbrellas: Bias and the Right to be Forgotten in Social Media Research}, series = {Internet Research Ethics for the Social Age}, booktitle = {Internet Research Ethics for the Social Age}, editor = {Zimmer, Michael and Kinder-Kurlanda, Katharina}, publisher = {Peter Lang}, address = {New York}, isbn = {9781433142697}, doi = {10.3726/b11077}, publisher = {Hertie School}, pages = {75 -- 90}, abstract = {Since the European Court of Justice handed down its ruling in the 2014 Costeja case - finding that Google and other search engine operators must consider requests made by individuals to remove links to websites that contain the requesting party's personal information - scholars, policymakers, legal practitioners, media commentators, and corporate representatives around the globe have been vigorously debating the so-called "right to be forgotten." In the American context, many worry that recognizing such a right would undermine the First Amendment's protections for freedom of speech and press. In the European Union, a renamed "right to erasure" has become law as part of the EU's General Data Protection Regulation in 2016. The right to erasure "prevent[s] the indefinite storage and trade in electronic data, placing limits on the duration and purpose for which businesses" can retain such data (Tsesis, 2014, p. 433) and holds that individuals may request the deletion of data when those data have become irrelevant, are inaccurate, or cause the individual harm that is not outweighed by a public benefit in retaining the data (Koops, 2011).}, language = {en} } @article{StockmannTrombleStorz, author = {Stockmann, Daniela and Tromble, Rebekah and Storz, Andreas}, title = {We Don't Know What We Don't Know: When and How the Use of Twitter's Public APIs Biases Scientific Inference}, series = {Available at SSRN}, volume = {3079927}, journal = {Available at SSRN}, doi = {10.2139/ssrn.3079927}, pages = {26}, abstract = {Though Twitter research has proliferated, no standards for data collection have crystallized. When using keyword queries, the most common data sources—the Search and Streaming APIs—rarely return the full population of tweets, and scholars do not know whether their data constitute a representative sample. This paper seeks to provide the most comprehensive look to-date at the potential biases that may result. Employing data derived from four identical keyword queries to the Firehose (which provides the full population of tweets but is cost-prohibitive), Streaming, and Search APIs, we use Kendall's-tau and logit regression analyses to understand the differences in the datasets, including what user and content characteristics make a tweet more or less likely to appear in sampled results. We find that there are indeed systematic differences that are likely to bias scholars' findings in almost all datasets we examine, and we recommend significant caution in future Twitter research.}, language = {en} }