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Tweeting the Environment #Brexit.
・ISBN 978-1-78756-502-9 paper US$ 71.99
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| 著者・編者 | Tong, Jingrong / Zuo, Landong, |
|---|---|
| シリーズ | Emerald Points |
| 出版社 | (Emerald, UK) |
| 出版年月 | 2018 |
| ページ数 | 155 pp. |
| 言語 | ENG |
| ニュース番号 | ＜647-L1523 647-L2087＞ |
解説
The level of politicisation of the environment has been low in the UK. Economic concerns outweigh environmental ones in political debates, public policies and political agendas. Can the rise of social media communication change this situation?
Tweeting the Environment #Brexit argues that, although limited by the dynamics of the British context, the technological affordances of Twitter enabled social actors such as the Green Party, ENGOs, and their associates to advance their political and green claims in order to mobilise voters before the 2016 EU referendum and to express their concerns in order to change environmental politics in the aftermath.
The interdisciplinary research employed a combination of big data applications such as ElasticSearch and Kibana and desktop applications such as Gephi and SPSS in analysing large-scale social data. Adopting an inductive and data-driven approach, this book shows the importance of mixed methods and the necessity of narrowing down "big" to "small" data in large-scale social media research.
Tweeting the Environment #Brexit argues that, although limited by the dynamics of the British context, the technological affordances of Twitter enabled social actors such as the Green Party, ENGOs, and their associates to advance their political and green claims in order to mobilise voters before the 2016 EU referendum and to express their concerns in order to change environmental politics in the aftermath.
The interdisciplinary research employed a combination of big data applications such as ElasticSearch and Kibana and desktop applications such as Gephi and SPSS in analysing large-scale social data. Adopting an inductive and data-driven approach, this book shows the importance of mixed methods and the necessity of narrowing down "big" to "small" data in large-scale social media research.