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The Social Dynamics of Language Change in Online Networks

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Social Informatics (SocInfo 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10046))

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Abstract

Language change is a complex social phenomenon, revealing pathways of communication and sociocultural influence. But, while language change has long been a topic of study in sociolinguistics, traditional linguistic research methods rely on circumstantial evidence, estimating the direction of change from differences between older and younger speakers. In this paper, we use a data set of several million Twitter users to track language changes in progress. First, we show that language change can be viewed as a form of social influence: we observe complex contagion for phonetic spellings and “netspeak” abbreviations (e.g., lol), but not for older dialect markers from spoken language. Next, we test whether specific types of social network connections are more influential than others, using a parametric Hawkes process model. We find that tie strength plays an important role: densely embedded social ties are significantly better conduits of linguistic influence. Geographic locality appears to play a more limited role: we find relatively little evidence to support the hypothesis that individuals are more influenced by geographically local social ties, even in their usage of geographical dialect markers.

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Notes

  1. 1.

    The basic unit of linguistic differentiation is referred to as a “variable” in the sociolinguistic and dialectological literature [50]. We maintain this terminology here.

  2. 2.

    After running SAGE to identify words with coefficients above 2.0, we manually removed hashtags, named entities, non-English words, and descriptions of events.

  3. 3.

    Other sources, such as http://urbandictionary.com, report asl to be an abbreviation of age, sex, location? However, this definition is not compatible with typical usage on Twitter, e.g., currently hungry asl or that movie was funny asl.

  4. 4.

    ard, inna, and lls appear on multiple cities’ lists. These words are characteristic of the neighboring cities of Baltimore, Philadelphia, and Washington D.C.

  5. 5.

    The shuffle test assumes that the likelihood of two users forming a social network connection does not change over time. Researchers have proposed a test [32] that removes this assumption; we will scale this test to our data set in future work.

  6. 6.

    We also compared the full feature set—i.e., F1+F2+F3+F4—to feature set F1+F2+F3 and feature set F1+F2+F4. The results were almost identical, indicating that F3 (tie strength) and F4 (local) provide complementary information.

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Acknowledgments

Thanks to the reviewers for their feedback, to Márton Karsai for suggesting the infection risk analysis, and to Le Song for discussing Hawkes processes. John Paparrizos is an Alexander S. Onassis Foundation Scholar. This research was supported by the National Science Foundation under awards IIS-1111142 and RI-1452443, by the National Institutes of Health under award number R01-GM112697-01, and by the Air Force Office of Scientific Research.

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Authors and Affiliations

  1. Georgia Institute of Technology, Atlanta, Georgia, USA

    Rahul Goel, Sandeep Soni, Naman Goyal & Jacob Eisenstein

  2. Columbia University, New York, New York, USA

    John Paparrizos

  3. Microsoft Research, New York, New York, USA

    Hanna Wallach & Fernando Diaz

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  1. Rahul Goel
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  7. Jacob Eisenstein
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Correspondence to Jacob Eisenstein .

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Editors and Affiliations

  1. University of Washington, Seattle, Washington, USA

    Emma Spiro

  2. Indiana University, Bloomington, Indiana, USA

    Yong-Yeol Ahn

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Goel, R. et al. (2016). The Social Dynamics of Language Change in Online Networks. In: Spiro, E., Ahn, YY. (eds) Social Informatics. SocInfo 2016. Lecture Notes in Computer Science(), vol 10046. Springer, Cham. https://doi.org/10.1007/978-3-319-47880-7_3

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  • DOI: https://doi.org/10.1007/978-3-319-47880-7_3

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