Purpose

Drawing on the cognitive-functional model of emotions and emotional contagion, the authors aim to examine the role of negative emotions in the diffusion of cancer tweets.

Design/methodology/approach

Using an integrated approach of social network and text analytics, the authors analyzed 142,883 cancer tweets from February to March 2018. The roles of negative emotions, emotional contagion, cancer themes and user influence on the diffusion of cancer tweets were examined.

Findings

Results indicated that cancer tweets expressing negativity and anger diffused more widely, while those expressing sadness or fear were less likely to diffuse. However, contrary to the authors’ expectation, cancer tweets expressing negative emotions (i.e. negativity, anger and fear) were less likely to arouse similar emotions among retweets, thus suggesting that emotions in cancer tweets were not as contagious as they seemed. Finally, user influence was the most important factor explaining the diffusion of cancer tweets, although cancer-related themes (i.e. affective, informative and social) had marginal effects on likelihood of diffusion.

Originality/value

Using a novel integrated social network–text analytics approach, the authors found that to understand cancer tweets' diffusion, it is critical to go beyond examining the content of tweets about cancer and the influence of messengers – the virality of cancer tweets is inextricable from the negative emotions.

中文翻译：

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负面情绪影响着癌症推文的传播：走向一种整合的社交网络-文本分析方法

目的

利用情绪和情绪传染的认知功能模型，作者旨在研究负面情绪在癌症推文扩散中的作用。

设计/方法/方法

作者使用社交网络和文本分析的集成方法，分析了2018年2月至2018年3月的142,883条癌症推文。研究了负面情绪，情绪传染，癌症主题和用户对癌症推文扩散的作用。

发现

结果表明，表达消极和愤怒的癌症推文散布得更广，而表达悲伤或恐惧的癌症推文散布的可能性更小。但是，与作者的预期相反，表达负面情绪（即消极，愤怒和恐惧）的癌症推文不太可能在转推中引起类似的情绪，因此表明癌症推文中的情绪没有看起来那么具有传染性。最后，尽管与癌症相关的主题（即情感，信息和社会）对扩散的可能性影响很小，但用户的影响力是解释癌症推文扩散的最重要因素。

创意/价值

通过使用新颖的集成社交网络-文本分析方法，作者发现，要了解癌症推文的扩散，至关重要的是，不仅要检查有关癌症的推文内容和信使的影响，而且，癌症推文的病毒性是不可分割的。负面情绪。