Abstract
In 2011, six academics gathered over 90,000 authentic text messages (SMS) in French from the general public, in compliance with French law (http://sud4s- cience.org, Panckhurst et al., 2013). The SMS ‘donors’ were also invited to fill out a sociolinguistic questionnaire (see Figure A1, Mo ise, 2013, Panckhurst and Mo ise, 2014). The ‘sud4science’ project is part of a vast international initiative, entitled ‘sms4science’ (http://www.sms4science.org/, Fairon et al., 2006, Cougnon and Fairon, 2014, Cougnon, 2015), which aims to build a worldwide database and analyse authentic text messages in different languages. After the ‘sud4science’ SMS data collection, a pre-processing phase of checking and elim- inating any spurious information and a three-step semi-automatic anonymiza- tion phase were conducted (Accorsi et al., 2014, Patel et al., 2013). Two extracts were transcoded into standardized French (1,000 SMS) and annotated (100 SMS). The finalized digital resource of 88,000 anonymized French text messages, the ‘88milSMS’ corpus, the extracts and the sociolinguistic questionnaire data are currently available for all to download, from the Huma-Num web service (http://88milsms.huma-num.fr, Panckhurst et al., 2014). The 88milSMS corpus has also recently become available via a Creative Commons Attribution 4.0 International licence on the ‘Ortolang’ platform (https://hdl.handle.net/11403/- comere/cmr-88milsms/cmr-88milsms-tei-v1, Panckhurst et al., in Chanier (ed), 2016). In this paper, first the authors briefly situate the project and describe the anonymization process. Then, they focus on why they decided to exclude full ‘transcoding’ and linguistic annotation in the first version of the final corpus.
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