Temporal Patterns in Bot Activities

被引：31

作者：

Chavoshi, Nikan ^{[1
]}

Hamooni, Hossein ^{[1
]}

Mueen, Abdullah ^{[1
]}

机构：

[1] Univ New Mexico, Albuquerque, NM 87131 USA

来源：

WWW'17 COMPANION: PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB | 2017年

基金：

美国国家科学基金会;

关键词：

Time series mining; Pattern mining; Bot; Social Media;

D O I：

10.1145/3041021.3051114

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Correlated or synchronized bots commonly exist in social media sites such as Twitter. Bots work towards gaining human followers, participating in campaigns, and engaging in unethical activities such as spamming and false click generation. In this paper, we perform temporal pattern mining on bot activities in Twitter. We discover motifs (repeating behavior), discords (anomalous behavior), joins, bursts and dynamic clusters in activities of Twitter bots, and explain the significance of these temporal patterns in gaining competitive advantage over humans. Our analysis identifies a small set of indicators that separates bots from humans with high precision.

引用

页码：1601 / 1606

页数：6

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