#Anorexia, #anarexia, #anarexyia: Characterizing online community practices with orthographic variation

Ian Stewart, Stevie Chancellor, Munmun De Choudhury, Jacob Eisenstein

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Distinctive linguistic practices help communities build solidarity and differentiate themselves from outsiders. In an online community, one such practice is variation in orthography, which includes spelling, punctuation, and capitalization. Using a dataset of over two million Instagram posts, we investigate orthographic variation in a community that shares pro-eating disorder (pro-ED) content. We find that not only does orthographic variation grow more frequent over time, it also becomes more profound or "deep," with variants becoming increasingly distant from the original: as, for example, #anarexyia is more distant than #anarexia from the original spelling #anorexia. We find that the these changes are driven by newcomers, who adopt the most extreme linguistic practices as they enter the community. Moreover, this behavior correlates with engagement with the community: the newcomers that adopt deeper variant orthography tend to remain active for longer in the community, and posts with deeper variation receive more positive feedback in the form of "likes." Previous work has linked community membership change with language change, and our work casts this connection in a new light, with newcomers driving an evolving practice rather than adapting to it. We also demonstrate the utility of orthographic variation as a new lens to study sociolinguistic change in online communities, particularly when the change results from an exogenous force such as a content ban.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
EditorsJian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4353-4361
Number of pages9
ISBN (Electronic)9781538627143
DOIs
StatePublished - Jul 1 2017
Externally publishedYes
Event5th IEEE International Conference on Big Data, Big Data 2017 - Boston, United States
Duration: Dec 11 2017Dec 14 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
Volume2018-January

Other

Other5th IEEE International Conference on Big Data, Big Data 2017
Country/TerritoryUnited States
CityBoston
Period12/11/1712/14/17

Bibliographical note

Funding Information:
We thank the anonymous reviewers for their feedback, and the audience at the Diversity and Variation in Language Conference at Emory for their feedback on an early version of this work. We also thank Brendan O'Connor for providing the Twitter data for comparison in § 5.4. This research was supported by Air Force Office of Scientific Research award FA9550-14-1-0379, by National Institutes of Health award R01-GM112697, and by the National Science Foundation award 1452443.

Funding Information:
We thank the anonymous reviewers for their feedback, and the audience at the Diversity and Variation in Language Conference at Emory for their feedback on an early version of this work. We also thank Brendan O’Connor for providing the Twitter data for comparison in § 5.4. This research was supported by Air Force Office of Scientific Research award FA9550-14-1-0379, by National Institutes of Health award R01-GM112697, and by the National Science Foundation award 1452443.

Publisher Copyright:
© 2017 IEEE.

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