Evidence map›Paper›PMID 36833983›Full record

ArticleInternational journal of environmental research and public health2023

Social Bots' Role in the COVID-19 Pandemic Discussion on Twitter.

Yaming Zhang, Wenjie Song, Jiang Shao, Majed Abbas, Jiaqi Zhang, Yaya H Koura, Yanyuan Su

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yaming ZhangSchool of Economics and Management, Yanshan University, Qinhuangdao 066004, China.
Wenjie SongSchool of Economics and Management, Yanshan University, Qinhuangdao 066004, China.ORCID 0000-0002-9823-3317
Jiang ShaoSchool of Economics and Management, Yanshan University, Qinhuangdao 066004, China.
Majed AbbasSchool of Economics and Management, Yanshan University, Qinhuangdao 066004, China.ORCID 0000-0002-5584-6383
Jiaqi ZhangSchool of Economics and Management, Yanshan University, Qinhuangdao 066004, China.
Yaya H KouraSchool of Economics and Management, Yanshan University, Qinhuangdao 066004, China.
Yanyuan SuSchool of Economics and Management, Yanshan University, Qinhuangdao 066004, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Social bots have already infiltrated social media platforms, such as Twitter, Facebook, and so on. Exploring the role of social bots in discussions of the COVID-19 pandemic, as well as comparing the behavioral differences between social bots and humans, is an important foundation for studying public health opinion dissemination. We collected data on Twitter and used Botometer to classify users into social bots and humans. Machine learning methods were used to analyze the characteristics of topic semantics, sentiment attributes, dissemination intentions, and interaction patterns of humans and social bots. The results show that 22% of these accounts were social bots, while 78% were humans, and there are significant differences in the behavioral characteristics between them. Social bots are more concerned with the topics of public health news than humans are with individual health and daily lives. More than 85% of bots' tweets are liked, and they have a large number of followers and friends, which means they have influence on internet users' perceptions about disease transmission and public health. In addition, social bots, located mainly in Europe and America countries, create an "authoritative" image by posting a lot of news, which in turn gains more attention and has a significant effect on humans. The findings contribute to understanding the behavioral patterns of new technologies such as social bots and their role in the dissemination of public health information.

Indexed as

COVID-19Social MediaHumansPandemicsPublic HealthSoftwareCOVID-19 pandemicsocial botssocial media analyticsTwitter

Identifiers

PMID36833983
PMCPMC9967279

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.