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 language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017 |
| Editors | Jian-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 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 4353-4361 |
| Number of pages | 9 |
| ISBN (Electronic) | 9781538627143 |
| DOIs | |
| State | Published - Jul 1 2017 |
| Externally published | Yes |
| Event | 5th IEEE International Conference on Big Data, Big Data 2017 - Boston, United States Duration: Dec 11 2017 → Dec 14 2017 |
Publication series
| Name | Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017 |
|---|---|
| Volume | 2018-January |
Other
| Other | 5th IEEE International Conference on Big Data, Big Data 2017 |
|---|---|
| Country/Territory | United States |
| City | Boston |
| Period | 12/11/17 → 12/14/17 |
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.