Language change and evolution in Online Social Networks
Creators: name: Kershaw, Daniel
Publisher: name: Lancaster University
Year: 2018

description: Language is in constant flux, whether through the creation of new terms or
      the changing meanings of existing words. The process by which language
      change happens is through complex reinforcing interactions between
      individuals and the social structures in which they exist. There has been
      much research into language change and evolution, though this has involved
      manual processes that are both time consuming and costly. However, with
      the growth in popularity of osn, for the first time, researchers have
      access to fine-grained records of language and user interactions that not
      only contain data on the creation of these language innovations but also
      reveal the inter-user and inter-community dynamics that influence their
      adoptions and rejections. Having access to these osn datasets means that
      language change and evolution can now be assessed and modelled through the
      application of computational and machine-learning-based methods.
      Therefore, this thesis looks at how one can detect and predict language
      change in osn, as well as the factors that language change depends on. The
      answer to this over-arching question lies in three core components: first,
      detecting the innovations; second, modelling the individual user adoption
      process; and third, looking at the collective adoption across a network of
      individuals. 
      
      
      In the first question, we operationalise traditional language acceptance
      heuristics (used to detect the emergence of new words) into three classes
      of computation time-series measures computing the variation in frequency,
      form and/or meaning. The grounded methods are applied to two osn, with
      results demonstrating the ability to detect language change across both
      networks. By additionally applying the methods to communities within each
      network, e.g. geographical regions, on Twitter and Subreddits in Reddit,
      the results indicate that language variation and change can be dependent
      on the community memberships. 
      
      
      The second question in this thesis focuses on the process of users
      adopting language innovations in relation to other users with whom they
      are in contact. By modelling influence between users as a function of past
      innovation cascades, we compute a global activation threshold at which
      users adopt new terms dependent on exposure to them from their neighbours.
      Additionally, by testing the user interaction networks through random
      shuffles, we show that the time at which a user adopts a term is dependent
      on the local structure; however, a large part of the influence comes from
      sources external to the observed osn. 
      
      
      The final question looks at how the speakers of a language are embedded in
      social networks, and how the networks' resulting structures and
      dynamics influence language usage and adoption patterns. We show that
      language innovations diffuse across a network in a predictable manner,
      which can be modelled using structural, grammatical and temporal measures,
      using a logistic regression model to predict the vitality of the
      diffusion. With regard to network structure, we show how innovations that
      manifest across structural holes and weak ties diffuse deeper across the
      given network. Beyond network influence, our results demonstrate that the
      grammatical context through which innovations emerge also play an
      essential role in diffusion dynamics - this indicates that the adoption of
      new words is enabled by a complex interplay of both network and linguistic
      factors.
      
      
      The three questions are used to answer the over-arching question, showing
      that one can, indeed, model language change and forecast user and
      community adoption of language innovations. Additionally, we also show the
      ability to apply grounded models and methods and apply them within a
      scalable computational framework. However, it is a challenging process
      that is heavily influenced by the underlying processes that are not
      recorded within the data from the osns.