Iterated learning takes place when the input into a particular individual's learning process is itself the output of another individual's learning process. This is an important feature to capture when investigating human language change, or the dynamics of culturally learned behaviours in general. Over the last fifteen years, the Iterated Learning Model (ILM) has been used to shed light on how the population-level characteristics of learned communication arise. However, until now each iteration of the model has tended to feature a single immature language user learning from their interactions with a single mature language user. Here, the ILM is extended to include a population of immature and mature language users. We demonstrate that the structure and make-up of this population influences the dynamics of language change that occur over generational time. In particular, we show that, by increasing the number of trainers from which an agent learns, the agent in question learns a fully compositional language at a much faster rate, and with less training data. It is also shown that, so long as the number of mature agents is large enough, this finding holds even if a learner's trainers include other agents that do not yet posses full linguistic competence.
This paper analyses the impact of a series of mass shootings committed in 2018-2019 by right-wing extremists on 8chan/pol, a prominent far-right online forum. Using computational methods, it offers a detailed examination of how attacks trigger shifts in both forum activity and content. We find that while each shooting is discussed by forum participants, their respective impact varies considerably. We highlight, in particular, a "Tarrant effect": the considerable effect Brenton Tarrant's attack of two mosques in Christchurch, New Zealand, had on the forum. In the double context of the rise in far-right terrorism and the growing and diversifying online far-right ecosystem, such interactive offline-online effects warrant the attention of both scholars and security professionals.
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