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首页> 外文期刊>Frontiers in Psychology >Big Changes Start With Small Talk: Twitter and Climate Change in Times of Coronavirus Pandemic
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Big Changes Start With Small Talk: Twitter and Climate Change in Times of Coronavirus Pandemic

机译:大型谈话开始:Twitter和Coronavirus大流行时期的气候变化

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Behavioural scientists have been studying public perceptions to understand how and why people behave the way they do towards climate change. In recent times, enormous changes to behaviour and people’s interactions have been brought about by the worldwide coronavirus disease 2019 (COVID-19) pandemic, unexpectedly and indefinitely; some of which have environmental implications (e.g., travelling less). An innovative way to analyse public perceptions and behaviour is with the use of social media to understand the discourse around climate change. This paper focuses on assessing changes in social media discourse around actions for climate change mitigation over time during the global pandemic. Twitter data were collected at three different points during the pandemic: February (time 1), June (time 2), and October 2020 (time 3). By using machine learning techniques, including recurrent neural networks (RNN) and unsupervised learning Latent Dirichlet Allocation (LDA) topic modelling, we identified tweets mentioning actions to mitigate climate change. The findings identified topics related to “ government actions ,” “environmental behaviours ,” “ sustainable production ,” and “ awareness ,” among others. We found an increase in tweets identified as “action tweets” relating to climate change for time 2 and time 3 compared with time 1. In addition, we found that the topic of energy seemed to be of relevance within the public’s perceptions of actions for climate change mitigation; this did not seem to change over time. We found that the topic of “ government actions ” was present across all time points and may have been influenced by political events at time 1, and by COVID-19 discourse at times 2 and 3. Moreover, topic changes over time within Twitter indicated a pattern that may have reflected restrictions on mobility as these tended to focus on individual and private sphere behaviours rather than group and public sphere behaviours. Changes in topic patterns may also reflect an increase in salience of certain behaviours (e.g., shopping), which may have received increased attention due to lockdown restrictions. Considering restrictions and adaptability challenges people face in times of a global pandemic may help to identify how to support sustainable behaviour change and the likely persistence of these changes.
机译:行为科学家一直在研究公众看法,了解如何以及为什么人们表现为他们对气候变化的方式。最近,全球冠状病毒2019(Covid-19)大流行,意外,无限期地提出了对行为和人们互动的巨大变化;其中一些具有环境影响(例如,旅行少)。分析公众看法和行为的创新方法是利用社交媒体来了解气候变化周围的话语。本文侧重于在全球大流行期间评估社会媒体话语的社会媒体话语的变化,随着时间的推移在全球大流行期间随着时间的推移。 Twitter数据在大流行期间在三个不同的点收集:2月(时间1),6月(时间2)和2020年10月(时间3)。通过使用机器学习技术,包括经常性神经网络(RNN)和无监督的学习潜在的Dirichlet分配(LDA)主题建模,我们确定了提及措施来缓解气候变化的推文。调查结果确定了与“政府行动”,“环境行为”,“可持续生产”和“意识”中的有关的主题。我们发现推文的增加被确定为与气候变化有关的“行动推文”与时间2和时间3相关的“行动推文”与时间相比,我们发现,我们发现能源的主题似乎与公众对气候行为的看法有关改变缓解;这似乎并没有随着时间的推移而变化。我们发现,“政府行动”的主题出现在所有时间点,可能受到时间1的政治事件的影响,并且在2和3时通过Covid-19话语。此外,Twitter内的时间随着时间的推移而改变了可能反映了移动性限制的模式,因为这些倾向于关注个人和私人球体行为而不是团体和公共球体行为。主题模式的变化也可能反映某些行为的显着性增加(例如,购物),这可能由于锁定限制而受到增加的关注。考虑到全球大流行时期人们面临的限制和适应性可能有助于确定如何支持可持续行为的变化和这些变化的可能持续存在。

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