Bot detection in twitter landscape using unsupervised learning

被引：9

作者：

Anwar, Ahmed ^{[1
]}

Yaqub, Ussama ^{[1
]}

机构：

[1] Lahore Univ Management Sci, Lahore, Pakistan

来源：

PROCEEDINGS OF THE 21ST ANNUAL INTERNATIONAL CONFERENCE ON DIGITAL GOVERNMENT RESEARCH, DGO 2020 | 2020年

关键词：

Twitter; Social Media; Unsupervised learning; Clustering;

D O I：

10.1145/3396956.3401801

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The aim of this paper is to identify and understand bot activity in twitter discussion. The prevalence of Twitter bots have gained significant limelight recently due to their misuse in influencing public sentiment for political gains. For our analysis, we use Twitter data of 2019 Canadian Elections. We perform principal component analysis and K-means clustering on the data set. Using the results we isolate bots from human accounts.

引用

页码：329 / 330

页数：2

共 7 条

[1]

[Anonymous], 2019, Twitter

[2] Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate [J].