[论文解读] Good Friends, Bad News - Affect and Virality in Twitter

Lars Kai Hansen, Adam Arvidsson+3 more|arXiv (Cornell University)|Jan 3, 2011

Opinion Dynamics and Social Influence参考文献 19被引用 22

一句话总结

本研究探讨了推特上情感与传播性之间的关系，采用朴素贝叶斯分类器区分新闻与非新闻内容。研究发现，负面情感在新闻相关推文中增强了转发行为——这与非新闻语境中观察到的积极情感效应相反——揭示出双重机制：在朋友间分享正面内容，但通过公开传播负面新闻以实现最大传播范围。

ABSTRACT

The link between affect, defined as the capacity for sentimental arousal on the part of a message, and virality, defined as the probability that it be sent along, is of significant theoretical and practical importance, e.g. for viral marketing. A quantitative study of emailing of articles from the NY Times finds a strong link between positive affect and virality, and, based on psychological theories it is concluded that this relation is universally valid. The conclusion appears to be in contrast with classic theory of diffusion in news media emphasizing negative affect as promoting propagation. In this paper we explore the apparent paradox in a quantitative analysis of information diffusion on Twitter. Twitter is interesting in this context as it has been shown to present both the characteristics social and news media. The basic measure of virality in Twitter is the probability of retweet. Twitter is different from email in that retweeting does not depend on pre-existing social relations, but often occur among strangers, thus in this respect Twitter may be more similar to traditional news media. We therefore hypothesize that negative news content is more likely to be retweeted, while for non-news tweets positive sentiments support virality. To test the hypothesis we analyze three corpora: A complete sample of tweets about the COP15 climate summit, a random sample of tweets, and a general text corpus including news. The latter allows us to train a classifier that can distinguish tweets that carry news and non-news information. We present evidence that negative sentiment enhances virality in the news segment, but not in the non-news segment. We conclude that the relation between affect and virality is more complex than expected based on the findings of Berger and Milkman (2010), in short 'if you want to be cited: Sweet talk your friends or serve bad news to the public'.

研究动机与目标

• 探究情感（积极与负面情绪）如何影响推特上的转发行为。
• 检验电子邮件研究中发现的积极情感与传播性关联（Berger & Milkman, 2010）是否可推广至推特等社交媒体平台。
• 解决社会语境中积极情感促进传播与媒体理论中负面情感驱动新闻传播之间的明显矛盾。
• 开发并验证一种用于识别短文本社交媒体内容新闻价值的分类器。

提出的方法

• 在布朗语料库上训练朴素贝叶斯分类器以检测文本的新闻价值，准确率达84%。
• 将训练好的分类器应用于两个推特数据集：随机样本和COP15气候峰会数据集。
• 使用广义线性模型检验情绪（积极/负面）对转发概率的影响，同时控制新闻价值因素。
• 创建负面情绪与新闻价值概率之间的交互项，以评估条件效应。
• 将分析范围限制在非零唤醒度（即情绪强烈的内容）的推文中，以隔离情感影响。
• 通过两个不同的推特语料库验证研究结果，评估情绪对传播性影响的语境依赖性。

实验结果

研究问题

• RQ1 机器学习分类器能否可靠检测短推文中文本的新闻价值？
• RQ2 在一般推特流量中，负面情绪是否降低或增强转发？
• RQ3 如经典新闻媒体理论所预测，负面情绪是否增强与新闻相关的推文的转发？
• RQ4 情绪与新闻价值之间的交互作用如何影响推特上的传播性？

主要发现

• 朴素贝叶斯分类器在短文本中检测新闻价值的准确率达到84%，表明其在推特内容中具备可靠的新闻识别能力。
• 在随机样本中，约23%的推文被分类为具有新闻价值，而在与COP15气候峰会相关的推文中，该比例上升至31%。
• 在随机样本中，负面情绪对转发的影响略为负面但统计上不显著，而积极情绪则是病毒式传播的强预测因子。
• 在COP15样本中，负面情绪显著提高了转发概率，尤其在情绪唤醒度高的内容中更为明显。
• 在两个数据集中，负面情绪与新闻价值的交互作用均为转发行为的显著预测因子，证实负面新闻传播范围更广。
• 研究结果解决了社交媒体分享（偏好积极情感）与新闻媒体（偏好负面情感）之间的矛盾，揭示了传播性在不同语境下的机制差异。