可想象的语言会让你的推文更有说服力吗?

Andy Bernhardt, T. Strzalkowski, Ning Sa, Ankita Bhaumik, Gregorios A. Katsios
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引用次数: 0

摘要

可想象性是词汇的一种心理语言学特性,它表明一个词唤起心理意象或其他感官体验的速度和容易程度。高度可想象的单词更容易阅读和理解,因此,它们在社交媒体等交流中的使用,使信息更容易被记住,并且可能更有影响力和影响力。在本文中,我们探讨了社交媒体中信息的可想象性与其对目标受众的影响之间的关系。我们专注于围绕重要公共事件的消息,并通过消息收到的转发数量来近似消息的影响力。首先,我们提出了一种新的方法来确定文本的可想象性得分,利用MRCPD+词典中的词级可想象性得分,以及词嵌入、图像标题数据和词频数据的组合。接下来,我们将这些新的可想象性评分函数与2017年法国总统选举领域中推文可想象性与转发次数之间的相关性的各种简单基线函数进行比较。我们发现消息的可想象性得分通常与转发数量相关,并且在主题和新颖性标准化时也是如此;因此,可想象的语言可能更有影响力。我们考虑将推文分组到可想象性评分范围内,并发现在可想象性评分较高范围内的推文平均比在较低范围内的推文获得更多的转发。最后,我们对少量推文进行了可想象性标注,结果表明,当人类评分者之间的一致性较高时,我们的可想象性评分函数与人类注释者的结果吻合得很好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Does Imageable Language Make Your Tweets More Persuasive?
Imageability is a psycholinguistic property of words that indicates how quickly and easily a word evokes a mental image or other sensory experience. Highly imageable words are easier to read and comprehend, and, as a result, their use in communications, such as social media, makes messages more memorable, and, potentially, more impactful and influential. In this paper, we explore the relationship between the imageability of messages in social media and their influence on the target audience. We focus on messages surrounding important public events and approximate the influence of a message by the number of retweets the message receives. First, we propose novel ways to determine an imageability score for a text, utilizing combinations of word-level imageability scores from the MRCPD+ lexicon, as well as word embeddings, image caption data, and word frequency data. Next, we compare these new imageability score functions to a variety of simple baseline functions in correlation between tweet imageability and number of retweets in the domain of the 2017 French Presidential Elections. We find that the imageability score of messages is correlated with the number of retweets in general, and also when normalized for topic and novelty; thus, imageable language is potentially more influential. We consider grouping tweets into imageability score ranges, and find that tweets within higher ranges of imageability scores receive more retweets on average compared to tweets within lower ranges. Lastly, we manually annotate a small number of tweets for imageability and show that our imageability score functions agree well with the human annotators when the agreement between human raters is high.
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