分析社会机器人和人类的社会和通信网络结构

Tuja Khaund, Kiran Kumar Bandeli, Muhammad Nihal Hussain, A. Obadimu, Samer Al-khateeb, Nitin Agarwal
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引用次数: 4

摘要

最近,一些新闻报道称,由于机器人无处不在,Twitter正在成为错误和虚假信息的领头羊。这些机器人要么是自动化的,要么是半自动化的。了解这些机器人的意图和用途激起了研究人员的科学好奇心。为此,在本研究中,我们分析了机器人在两种不同类别的现实世界事件中的作用,即自然灾害和体育运动。我们收集了近80万用户关于飓风哈维、飓风厄玛、飓风玛丽亚和墨西哥地震的120多万条推文。我们通过检查参与2018年冬奥会的机器人来证实我们的分析。我们收集了来自近70万用户的140多万条推文,这些推文都是基于#Olympics2018和#PyeongChang2018的标签。我们为上述事件检查了机器人和人类的社交和通信网络。我们的研究结果显示,与人类相比,机器人的网络结构具有独特的模式。对推文的内容分析进一步显示,在所有事件中,机器人比人类更均匀地使用标签。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analyzing Social and Communication Network Structures of Social Bots and Humans
Recently, several journalistic accounts have suggested that Twitter is becoming a bellwether for mis- and disinformation due to the pervasiveness of bots. These bots are either automated or semi-automated. Understanding the intent and usage of these bots has piqued the scientific curiosity among researchers. To that effect, in this study, we analyze the role of bots in two distinct categories of real-world events, i.e., natural disasters and sports. We collected over 1.2 million tweets that were generated by nearly 800,000 users for Hurricane Harvey, Hurricane Irma, Hurricane Maria, and Mexico Earthquake. We corroborate our analysis by examining bots that engaged with the 2018 Winter Olympics. We collected over 1.4 million tweets generated by nearly 700,000 users based on the hashtags #Olympics2018 and #PyeongChang2018. We examined the social and communication network of bots and humans for the aforementioned events. Our results show distinctive patterns in the network structures of bots when compared with that of humans. Content analysis of the tweets further revealed that bots used hashtags more uniformly than humans, across all the events.
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