Online social networks allow users to share a variety of multi-media content on the World Wide Web. The rising popularity of such social networking platforms coupled with limitations in verifying the veracity of shared content has contributed to increase in misinformation on these media. Misinformation content such as fake-news and hoaxes, though often considered innocuous, may have high social cost such as influencing elections decision, and thus should be investigated carefully. Many researchers have studied various aspects of fake-news including automated ways to recognize it. However, a large-scale study comparing the sharing patterns of fake-news and trusted-news is missing. In this research, we take Ukraine, a country where fake news is common, as a case study. Using datasets generated by three different Tweets collection strategies, we present an anatomical comparison of fake-news and trusted-news sharing pattern on Twitter. Such a comparison enables to identify the characteristics of tweets sharing fake-news, and allows to find the users who are more inclined to share misinformation. Besides, we also study possible bot activities in the dataset. The top conclusions derived from this study are (a) Users sharing fake-news stories are more likely to include hashtags, and the hashtags used in Tweets sharing fake-news stories are similar to hashtags used in Tweets sharing trusted news. (b) Users sharing fake-news are also more likely to include mentions, but mentions used in tweets sharing fake-news and trusted-news are often different. (c) Tweets sharing fake-news have more negative sentiment. In contrast, tweets sharing trusted-news have more positive sentiment.

在线社交网络使用户可以在万维网上共享各种多媒体内容。这种社交网络平台的日益普及，加上验证共享内容的真实性的局限性，导致这些媒体上的错误信息增多。虚假新闻和恶作剧等虚假信息内容虽然通常被认为是无害的，但其社会成本较高，例如影响选举决策，因此应仔细调查。许多研究人员研究了假新闻的各个方面，包括自动识别假新闻的方法。但是，缺少对假新闻和可信新闻的共享模式进行比较的大规模研究。在这项研究中，我们以乌克兰为例，该国是一个假新闻普遍的国家。使用由三种不同的Tweets收集策略生成的数据集，我们在Twitter上对假新闻和受信任新闻共享模式进行了解剖比较。这样的比较使得能够识别共享假新闻的推文的特征，并允许找到更倾向于共享错误信息的用户。此外，我们还研究了数据集中可能存在的机器人活动。这项研究得出的最高结论是：（a）共享假新闻故事的用户更有可能包含主题标签，并且共享假新闻故事的推文中使用的主题标签类似于共享可信新闻的推文中使用的主题标签。（b）共享假新闻的用户也更可能包含提及，但共享假新闻和可信新闻的推文中使用的提及通常是不同的。（c）分享假新闻的推文具有更多的负面情绪。相比之下，分享可信赖新闻的推文则更具积极情绪。