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What makes you tick? The psychology of social media engagement in space science communication

机译:是什么让您打勾?社交媒体参与空间科学传播的心理学

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The rise of social media has transformed the way the public engages with science organisations and scientists. 'Retweet', 'Like', 'Share' and 'Comment' are a few ways users engage with messages on Twitter and Facebook, two of the most popular social media platforms. Despite the availability of big data from these digital footprints, research into social media science communication is scant. This paper presents a novel empirical study into the features of engaging science-related social media messages, focusing on space science communications. It is hypothesised that these messages contain certain psycholinguistic features that are unique to the field of space science. We built a predictive model to forecast the engagement levels of social media posts. By using four feature sets (n-grams, psycholinguistics, grammar and social media), we were able to achieve prediction accuracies in the vicinity of 90% using three supervised learning algorithms (Naive Bayes, linear classifier and decision tree). We conducted the same experiments on social media messages from three other fields (politics, business and non-profit) and discovered several features that are exclusive to space science communications: anger, authenticity, hashtags, visual descriptions-be it visual perception-related words, or media elements-and a tentative tone. (C) 2016 Elsevier Ltd. All rights reserved.
机译:社交媒体的兴起改变了公众与科学组织和科学家互动的方式。 “转推”,“喜欢”,“分享”和“评论”是用户在Twitter和Facebook(两种最受欢迎​​的社交媒体平台)上与消息互动的几种方式。尽管可以从这些数字足迹中获得大数据,但对社交媒体科学传播的研究很少。本文针对结合科学相关的社交媒体消息的特征进行了新颖的实证研究,重点是空间科学传播。假设这些消息包含某些心理语言学特征,这些特征是空间科学领域独有的。我们建立了一个预测模型来预测社交媒体帖子的参与度。通过使用四个特征集(n个语法,心理语言学,语法和社交媒体),我们能够使用三种监督学习算法(朴素贝叶斯,线性分类器和决策树)实现90%左右的预测准确性。我们对来自其他三个领域(政治,商业和非营利组织)的社交媒体消息进行了相同的实验,并发现了空间科学传播所独有的几个功能:愤怒,真实性,主题标签,视觉描述(与视觉感知相关的词语)或媒体元素-以及试探性语气。 (C)2016 Elsevier Ltd.保留所有权利。

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