标题： 社交网络地理全球机器人清单 显示英文标题

标题： Social Cyber Geographical Worldwide Inventory of Bots

摘要： 社会网络地理学是通过社会关系在数字网络领域中产生的空间。 在社交媒体生态系统中，交流不仅由于人类互动而发生，还受到算法控制的机器人代理的推动。 大多数研究没有关注机器人的社会网络地理学，因为它们专注于单一国家内的机器人活动。 由于创建机器人使用的是通用编程技术，机器人在全球范围内的普及程度如何？ 为了量化全球的机器人活动，我们对2021年冠状病毒疫情期间从X收集的大规模社交媒体数据集进行了多语言和地理空间分析。 这场疫情影响了世界大部分地区，因此成为共同讨论的话题。 我们的数据集包括由约3100万用户生成的约1亿条帖子。 大多数机器人研究只关注英语国家，因为大多数机器人检测算法是为英语设计的。 然而，只有47%的机器人使用英语写作。 为了在我们的机器人检测算法中支持多种语言，我们构建了Multilingual BotBuster，一种多语言机器人检测算法，以识别这个多样化数据集中的机器人。 我们还创建了一个地理定位标识符，以快速确定用户在其描述中所属的国家。 我们的结果表明，机器人似乎可以从一个国家移动到另一个国家，但它们使用的语言相对保持不变。 机器人在与他们自声明的国家归属相关的不同主题上传播叙述。 最后，尽管机器人位置在全球范围内分布多样，但每个国家的机器人比例约为20%。 我们的工作强调了对网络和物理领域进行联合分析的重要性，我们结合这两个领域来清点社交媒体机器人的语言和位置，并理解沟通策略。 显示英文摘要

摘要： Social Cyber Geography is the space in the digital cyber realm that is produced through social relations. Communication in the social media ecosystem happens not only because of human interactions, but is also fueled by algorithmically controlled bot agents. Most studies have not looked at the social cyber geography of bots because they focus on bot activity within a single country. Since creating a bot uses universal programming technology, bots, how prevalent are these bots throughout the world? To quantify bot activity worldwide, we perform a multilingual and geospatial analysis on a large dataset of social data collected from X during the Coronavirus pandemic in 2021. This pandemic affected most of the world, and thus is a common topic of discussion. Our dataset consists of ~100 mil posts generated by ~31mil users. Most bot studies focus only on English-speaking countries, because most bot detection algorithms are built for the English language. However, only 47\% of the bots write in the English language. To accommodate multiple languages in our bot detection algorithm, we built Multilingual BotBuster, a multi-language bot detection algorithm to identify the bots in this diverse dataset. We also create a Geographical Location Identifier to swiftly identify the countries a user affiliates with in his description. Our results show that bots can appear to move from one country to another, but the language they write in remains relatively constant. Bots distribute narratives on distinct topics related to their self-declared country affiliation. Finally, despite the diverse distribution of bot locations around the world, the proportion of bots per country is about 20%. Our work stresses the importance of a united analysis of the cyber and physical realms, where we combine both spheres to inventorize the language and location of social media bots and understand communication strategies.