CodE Alltag 2.0 — A Pseudonymized German-Language Email Corpus

Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 4466–4477 Marseille, 11–16 May 2020 c⃝ European Language Resources Association (ELRA), licensed under CC-BY-NC 4466 CODE ALLTAG 2.0 — A Pseudonymized German-Language Email Corpus Elisabeth Eder1 Ulrike Krieg-Holz1 Udo Hahn2 1Institut f¨ur Germanistik, Alpen-Adria-Universit¨at Klagenfurt, Klagenfurt, Austria; 2Jena University Language & Information Engineering (JULIE) Lab, Friedrich-Schiller-Universit¨at Jena, Jena, Germany {elisabeth.eder | ulrike.krieg-holz}@aau.at | udo.hahn@uni-jena.de Abstract The vast amount of social communication distributed over various electronic media channels (tweets, blogs, emails, etc.), so-called user-generated content (UGC), creates entirely new opportunities for today’s NLP research. Yet, data privacy concerns implied by the unauthorized use of these text streams as a data resource are often neglected. In an attempt to reconciliate the diverging needs of unconstrained raw data use and preservation of data privacy in digital communication, we here investigate the automatic recognition of privacy-sensitive stretches of text in UGC and provide an algorithmic solution for the protection of personal data via pseudonymization. Our focus is directed at the de-identification of emails where personally identifying information does not only refer to the sender but also to those people, locations, dates, and other identifiers mentioned in greetings, boilerplates and the content-carrying body of emails. We evaluate several de-identification procedures and systems on two hitherto non-anonymized German-language email corpora (CODE ALLTAGS+d and CODE ALLTAGXL), and generate fully pseudonymized versions for both (CODE ALLTAG 2.0) in which personally identifying information of all social actors addressed in these mails has been camouflaged (to the greatest extent possible). Keywords: email corpus, de-identification, pseudonymization, data privacy, German language resource 1. Introduction With the rapidly increasing adoption of electronic interac- tion platforms, we observe an unprecedented upsurge of digitally transmitted private communication and exploding volumes of so-called user-generated contents (UGC). As a major characteristic of these new communication habits, a sender’s individual email, post, comment is distributed to an often (very) large number of addressees—the recipients of an email, followers in social media, other users of a plat- form etc.. Hence, hitherto private communication becomes intentionally public. In response to these changes, digital (social) media com- munication has become a major focus of research in NLP. Yet there seems to be a lack of awareness among NLP re- searchers that the exploitation of natural language data from such electronic communication channels, whether for com- mercial, administrative or academic purposes, has to com- ply with binding legal regulations (Wilson et al., 2016). Dependent on each country’s legislation system, different rules for privacy protection in raw text data are enforced (cf., e.g., two recent analyses for the US (Mulligan et al., 2019) and the EU (Hoofnagle et al., 2019)). Even privacy- breach incidents in a legal grey zone can be harmful for the actors involved (including NLP researchers). This dilemma is evidenced dramatically in the so-called AOL search data leak.1 In August of 2006, American On- line (AOL) made a large query log collection freely acces- sible on the Internet for a limited time. The data were ex- tracted …

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