The purpose of this study is to clarify what kind of news is easily retweeted and what kind of news is easily Liked. We believe these actions, retweeting and Liking, have different meanings for users. Understanding this difference is important for understanding people's interest in Twitter. To analyze the difference between retweets (RT) and Likes on Twitter in detail, we focus on word appearances in news titles. First, we calculate basic statistics and confirm that tweets containing news URLs have different RT and Like tendencies compared to other tweets. Next, we compared RTs and Likes for each category and confirmed that the tendency of categories is different. Therefore, we propose metrics for clarifying the differences in each action for each category used in the $\chi$-square test in order to perform an analysis focusing on the topic. The proposed metrics are more useful than simple counts and TF-IDF for extracting meaningful words to understand the difference between RTs and Likes. We analyzed each category using the proposed metrics and quantitatively confirmed that the difference in the role of retweeting and Liking appeared in the content depending on the category. Moreover, by aggregating tweets chronologically, the results showed the trend of RT and Like as a list of words and clarified how the characteristic words of each week were related to current events for retweeting and Liking.

中文翻译：

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关键字指标，用于了解每个类别中的“转发”和“喜欢”之间的区别

这项研究的目的是弄清什么样的新闻容易被转发以及什么样的新闻容易被喜欢。我们相信这些操作（转发和喜欢）对用户而言具有不同的含义。理解这种差异对于理解人们对Twitter的兴趣很重要。为了详细分析推特（RT）和Twitter上的“喜欢”之间的区别，我们重点研究新闻标题中的单词外观。首先，我们计算基本统计数据并确认包含新闻URL的推文与其他推文相比具有不同的RT和Like趋势。接下来，我们比较了每个类别的RT和Likes，并确认类别的趋势有所不同。因此，为了提出针对该主题的分析，我们提出了度量标准，以澄清在\\ chi $平方测试中使用的每个类别在每个操作中的差异。拟议的度量标准比简单计数和TF-IDF更为有用，它可以提取有意义的单词来理解RT和Likes之间的差异。我们使用建议的指标分析了每个类别，并定量确认了根据类别，内容中出现了转发和喜欢角色的差异。此外，通过按时间顺序汇总推文，结果以单词列表的形式显示了RT和Like的趋势，并阐明了每周的特色单词与时事转推和喜欢时如何相关。我们使用建议的指标分析了每个类别，并定量确认了根据类别，内容中出现了转发和喜欢角色的差异。此外，通过按时间顺序汇总推文，结果以单词列表的形式显示了RT和Like的趋势，并阐明了每周的特色单词与时事转推和喜欢时如何相关。我们使用建议的指标分析了每个类别，并定量确认了根据类别，内容中出现了转发和喜欢角色的差异。此外，通过按时间顺序汇总推文，结果以单词列表的形式显示了RT和Like的趋势，并阐明了每周的特色单词与时事转推和喜欢时如何相关。