当前位置: X-MOL 学术Big Data › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Collective Behavior of Social Bots Is Encoded in Their Temporal Twitter Activity.
Big Data ( IF 4.6 ) Pub Date : 2018-06-01 , DOI: 10.1089/big.2017.0041
Andrej Duh 1, 2 , Marjan Slak Rupnik 2, 3 , Dean Korošak 1, 2, 4
Affiliation  

Computational propaganda deploys social or political bots to try to shape, steer, and manipulate online public discussions and influence decisions. Collective behavior of populations of social bots has not been yet widely studied, although understanding of collective patterns arising from interactions between bots would aid social bot detection. In this study, we show that there are significant differences in collective behavior between population of bots and population of humans as detected from their Twitter activity. Using a large dataset of tweets we have collected during the UK-EU referendum campaign, we separated users into population of bots and population of humans based on the length of sequences of their high-frequency tweeting activity. We show that, while pairwise correlations between users are weak, they co-exist with collective correlated states; however the statistics of correlations and co-spiking probability differ in both populations. Our results demonstrate that populations of social bots and human users in social media exhibit collective properties similar to the ones found in social and biological systems placed near a critical point.

中文翻译:

社会性机器人的集体行为被编码在其临时Twitter活动中。

计算性宣传部署了社交或政治机器人,以试图塑造,引导和操纵在线公共讨论并影响决策。尽管了解由机器人之间的交互作用引起的集体模式将有助于检测社交机器人,但尚未广泛研究社交机器人种群的集体行为。在这项研究中,我们显示,从机器人的Twitter活动中可以看出,机器人群体与人类群体之间的集体行为存在显着差异。使用我们在英欧公投期间收集的大量推文数据集,我们根据高频推文活动序列的长度将用户分为机器人群体和人类群体。我们表明,尽管用户之间的成对相关性很弱,它们与集体相关国家共存;但是,两个人群的相关性和共同加标概率的统计数据不同。我们的研究结果表明,社交媒体中的社交机器人和人类用户群体具有类似于在临界点附近的社会和生物系统中发现的集体属性。
更新日期:2018-06-01
down
wechat
bug