论文标题

#istandwithputin versus #istandwithukraine:在讨论俄罗斯/乌克兰战争中,机器人与人的相互作用

#IStandWithPutin versus #IStandWithUkraine: The interaction of bots and humans in discussion of the Russia/Ukraine war

论文作者

Smart, Bridget, Watt, Joshua, Benedetti, Sara, Mitchell, Lewis, Roughan, Matthew

论文摘要

2022年俄罗斯对乌克兰的入侵强调了社交媒体在现代战争中所扮演的角色,在物理和信息环境中发生了冲突。在确定恶意的网络活动方面,有大量的工作,但较少关注这项活动对整个对话的影响,尤其是在俄罗斯/乌克兰冲突方面。在这里,我们采用各种技术,包括信息理论措施,情感和语言分析以及时间序列技术来了解机器人活动如何影响更广泛的在线话语。通过汇总帐户组,我们发现了从类似机器人的帐户到非机器人帐户的重要信息流,而行为之间的行为不同。亲俄的非机器人帐户总体上最有影响力,信息流向其他各种帐户组。亲乌克兰的非机器人帐户中没有明显的外向流,从亲乌克兰机器人帐户中大量流入亲乌克兰的非机器人帐户。我们发现,机器人活动驱动围绕焦虑(p = 2.450 x 1e-4)以及周围的工作/治理(p = 3.803 x 1E-18)的对话增加。 Bot活动还显示了与非机器人情绪的显着关系(p = 3.76 x 1e-4),在此我们发现这种关系均符合两个方向。这项工作扩展并结合了现有技术,以量化机器人如何影响俄罗斯/乌克兰入侵的在线对话中的人们。它为研究人员开辟了途径,以定量地了解这些恶意运动的运作方式,以及使它们影响力的原因。

The 2022 Russian invasion of Ukraine emphasises the role social media plays in modern-day warfare, with conflict occurring in both the physical and information environments. There is a large body of work on identifying malicious cyber-activity, but less focusing on the effect this activity has on the overall conversation, especially with regards to the Russia/Ukraine Conflict. Here, we employ a variety of techniques including information theoretic measures, sentiment and linguistic analysis, and time series techniques to understand how bot activity influences wider online discourse. By aggregating account groups we find significant information flows from bot-like accounts to non-bot accounts with behaviour differing between sides. Pro-Russian non-bot accounts are most influential overall, with information flows to a variety of other account groups. No significant outward flows exist from pro-Ukrainian non-bot accounts, with significant flows from pro-Ukrainian bot accounts into pro-Ukrainian non-bot accounts. We find that bot activity drives an increase in conversations surrounding angst (with p = 2.450 x 1e-4) as well as those surrounding work/governance (with p = 3.803 x 1e-18). Bot activity also shows a significant relationship with non-bot sentiment (with p = 3.76 x 1e-4), where we find the relationship holds in both directions. This work extends and combines existing techniques to quantify how bots are influencing people in the online conversation around the Russia/Ukraine invasion. It opens up avenues for researchers to understand quantitatively how these malicious campaigns operate, and what makes them impactful.

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