Evidence map›Paper›PMID 39592012›Full record

ArticleJournal of the Royal Society, Interface2024

Exploring agent interaction patterns in the comment sections of fake and real news.

Kailun Zhu, Songtao Peng, Jiaqi Nie, Zhongyuan Ruan, Shanqing Yu, Qi Xuan

Abstract read
In one paragraph

Article in Journal of the Royal Society, Interface, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

1 citing paper in PubMed.

  1. Article
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

6 authors.

Kailun ZhuInstitute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, People's Republic of China.ORCID 0009-0009-1862-806X
Songtao PengInstitute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, People's Republic of China.
Jiaqi NieInstitute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, People's Republic of China.
Zhongyuan RuanInstitute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, People's Republic of China.ORCID 0000-0003-4953-070X
Shanqing YuInstitute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, People's Republic of China.
Qi XuanInstitute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

User comments on social media have been recognized as a crucial factor in distinguishing between fake and real news, with many studies focusing on the textual content of user reactions. However, the interactions among agents in the comment sections for fake and real news have not been fully explored. In this study, we analyse a dataset comprising both fake and real news from Reddit to investigate agent interaction patterns, considering both the network structure and the sentiment of the nodes. Our main findings reveal that, compared with fake news, where users generate more negative sentiment, real news tends to elicit more neutral and positive sentiments. Additionally, nodes with similar sentiments cluster together more tightly than anticipated. From a dynamic perspective, we found that the sentiment distribution among nodes stabilizes early and remains stable over time. These findings have both theoretical and practical implications, particularly for the early detection of real and fake news within social networks.

Indexed as

Social MediaDeceptionHumansagent interactionfake and real newssentiment

Identifiers

PMID39592012
PMCPMC11597403

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.