a1 Newcastle University
a2 Stanford University
This paper examines a short-lived innovation, quotative all, in real and apparent time. We used a two-pronged method to trace the trajectory of all over the past two decades: (i) Quantitative analyses of the quotative system of young Californians from different decades; this reveals a startling crossover pattern: in 1990/1994, all predominates, but by 2005, it has given way to like. (ii) Searches of Internet newsgroups; these confirm that after rising briskly in the 1990s, all is declining. Tracing the changing usage of quotative options provides year-to-year evidence that all has recently given way to like. Our paper has two aims: We provide insights from ongoing language change regarding short-term innovations in the history of English. We also discuss our collaboration with Google Inc. and argue for the value of newsgroups to research projects investigating linguistic variation and change in real time, especially where recorded conversational tokens are relatively sparse.
An earlier version of this paper was presented at NWAV 35 (New Ways of Analyzing Variation) at Ohio State University in Columbus. We are grateful for comments from the audience, in particular to John V. Singler and Mary Bucholtz. All remaining errors are, of course, our own. We are grateful to John Singler and other reviewers of this paper for their helpful feedback on an earlier draft. We thank Google Inc. for the opportunity to collaborate on this exciting project, drawing on their both personnel and facilities. Many thanks go to Thorsten Brants for his enthusiasm for and support of the project as well as for his enormous input in terms of computational methods. We are also indebted to David Hall for developing and implementing the tools needed to do the searches we requested and for responding swiftly and extensively to all our queries and suggestions. Thanks are due to Carmen Fought, Rachelle Waksler, and Ann Wimmer for allowing us to use their data on quotative all and other forms from the 1980s and 1990s as well as to Bob Bayley and Mackenzie Price for guidance with statistical analysis. Finally, we are grateful to Stanford faculty colleagues for their input and to several Stanford students who provided substantial assistance with data collection and analysis between 2004 and 2010, especially Zoe Bogart, Crissy Brown, Kayla Carpenter, Tracy Conner, Kristle McCracken, Rowyn McDonald, Cybelle Smith, Francesca Smith, and Laura Whitton.