Browsing by Author "Pearson E"
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- ItemAll the world wide web's a stage: The performance of identity in online social networks(1/01/2009) Pearson EThis paper discusses how ideas of performance can be used to conceptualize the play of identity formation on social networking sites (SNS). Linking Goffman's theories of social performance with Granovetter's notion of the social tie, this paper will argue that identities on SNS are deliberately constructed performances that straddle the frontstage and the backstage, the public and the private, and in doing so both support and rely upon webs of social connections which engage with fluid or playful identity constructions.
- ItemDomestic Internet of Things: Aotearoa New Zealanders' Privacy Concerns and Behaviours(2020-07-01) Pearson E; Jaspers E
- ItemNational Leaders' Usage of Twitter in Response to COVID-19: A Sentiment Analysis(Frontiers Media S.A., 2021-09-08) Wang Y; Croucher SM; Pearson E; Nair PTwitter is a powerful tool for world leaders to disseminate public health information and to reach citizens. While Twitter, like other platforms, affords world leaders the opportunity to rapidly present information to citizens, the discourse is often politically framed. In this study, we analysed how leaders' of the Five Eyes intelligence sharing group use Twitter to frame the COVID-19 virus. Specifically, four research questions were explored: 1) How frequently did each leader tweet about COVID-19 in 2020? 2) Which frames emerged from tweet content of each leader regarding COVID-19? 3) What was the overall tweet valence of each leader regarding COVID-19? and 4) To what extent can leaders' future tweets be predicted by the data? We used natural language processing (NLP) and conducted sentiment analysis via Python to identify frames and to compare the leaders' messaging. Results showed that of the leaders, President Trump tweeted the most, with Prime Minister Morrison posting the least number of tweets. The majority of each leaders' tweets were positive, while President Trump had the most negative tweets. Predictive modelling of tweet behavior was highly accurate.