混合媒体系统中的算法代理：社交机器人、选择性放大和关于新冠肺炎的党派新闻

IF 5.4 3区材料科学 Q2 CHEMISTRY, PHYSICAL

ACS Applied Energy Materials Pub Date : 2022-05-17 DOI:10.1093/hcr/hqac012

Zening Duan, Jianing Li, Josephine Lukito, Kai-Cheng Yang, Fan Chen, Dhavan V. Shah, Sijia Yang

{"title":"混合媒体系统中的算法代理：社交机器人、选择性放大和关于新冠肺炎的党派新闻","authors":"Zening Duan, Jianing Li, Josephine Lukito, Kai-Cheng Yang, Fan Chen, Dhavan V. Shah, Sijia Yang","doi":"10.1093/hcr/hqac012","DOIUrl":null,"url":null,"abstract":"\n Social bots, or algorithmic agents that amplify certain viewpoints and interact with selected actors on social media, may influence online discussion, news attention, or even public opinion through coordinated action. Previous research has documented the presence of bot activities and developed detection algorithms. Yet, how social bots influence attention dynamics of the hybrid media system remains understudied. Leveraging a large collection of both tweets (N = 1,657,551) and news stories (N = 50,356) about the early COVID-19 pandemic, we employed bot detection techniques, structural topic modeling, and time series analysis to characterize the temporal associations between the topics Twitter bots tend to amplify and subsequent news coverage across the partisan spectrum. We found that bots represented 8.98% of total accounts, selectively promoted certain topics and predicted coverage aligned with partisan narratives. Our macro-level longitudinal description highlights the role of bots as algorithmic communicators and invites future research to explain micro-level causal mechanisms.","PeriodicalId":4,"journal":{"name":"ACS Applied Energy Materials","volume":null,"pages":null},"PeriodicalIF":5.4000,"publicationDate":"2022-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":"{\"title\":\"Algorithmic Agents in the Hybrid Media System: Social Bots, Selective Amplification, and Partisan News about COVID-19\",\"authors\":\"Zening Duan, Jianing Li, Josephine Lukito, Kai-Cheng Yang, Fan Chen, Dhavan V. Shah, Sijia Yang\",\"doi\":\"10.1093/hcr/hqac012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Social bots, or algorithmic agents that amplify certain viewpoints and interact with selected actors on social media, may influence online discussion, news attention, or even public opinion through coordinated action. Previous research has documented the presence of bot activities and developed detection algorithms. Yet, how social bots influence attention dynamics of the hybrid media system remains understudied. Leveraging a large collection of both tweets (N = 1,657,551) and news stories (N = 50,356) about the early COVID-19 pandemic, we employed bot detection techniques, structural topic modeling, and time series analysis to characterize the temporal associations between the topics Twitter bots tend to amplify and subsequent news coverage across the partisan spectrum. We found that bots represented 8.98% of total accounts, selectively promoted certain topics and predicted coverage aligned with partisan narratives. Our macro-level longitudinal description highlights the role of bots as algorithmic communicators and invites future research to explain micro-level causal mechanisms.\",\"PeriodicalId\":4,\"journal\":{\"name\":\"ACS Applied Energy Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2022-05-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"20\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Energy Materials\",\"FirstCategoryId\":\"98\",\"ListUrlMain\":\"https://doi.org/10.1093/hcr/hqac012\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Energy Materials","FirstCategoryId":"98","ListUrlMain":"https://doi.org/10.1093/hcr/hqac012","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}

引用次数: 20

摘要

社交机器人，或放大某些观点并在社交媒体上与选定参与者互动的算法代理，可能会通过协调行动影响在线讨论、新闻关注，甚至公众舆论。先前的研究已经记录了机器人活动的存在，并开发了检测算法。然而，社交机器人如何影响混合媒体系统的注意力动态仍然没有得到充分的研究。利用两条推文的大量集合（N = 1657551）和新闻报道（N = 50356）关于早期新冠肺炎大流行，我们采用机器人检测技术、结构主题建模和时间序列分析来表征推特机器人倾向于放大的主题与随后的跨党派新闻报道之间的时间关联。我们发现，机器人占总账户的8.98%，有选择地推广某些话题，并预测与党派叙事一致的报道。我们在宏观层面的纵向描述强调了机器人作为算法传播者的作用，并邀请未来的研究来解释微观层面的因果机制。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Algorithmic Agents in the Hybrid Media System: Social Bots, Selective Amplification, and Partisan News about COVID-19

Social bots, or algorithmic agents that amplify certain viewpoints and interact with selected actors on social media, may influence online discussion, news attention, or even public opinion through coordinated action. Previous research has documented the presence of bot activities and developed detection algorithms. Yet, how social bots influence attention dynamics of the hybrid media system remains understudied. Leveraging a large collection of both tweets (N = 1,657,551) and news stories (N = 50,356) about the early COVID-19 pandemic, we employed bot detection techniques, structural topic modeling, and time series analysis to characterize the temporal associations between the topics Twitter bots tend to amplify and subsequent news coverage across the partisan spectrum. We found that bots represented 8.98% of total accounts, selectively promoted certain topics and predicted coverage aligned with partisan narratives. Our macro-level longitudinal description highlights the role of bots as algorithmic communicators and invites future research to explain micro-level causal mechanisms.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

ACS Applied Energy Materials Materials Science-Materials Chemistry

CiteScore

10.30

自引率

6.20%

发文量

1368

期刊介绍： ACS Applied Energy Materials is an interdisciplinary journal publishing original research covering all aspects of materials, engineering, chemistry, physics and biology relevant to energy conversion and storage. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important energy applications.