Evidence map›Paper›PMID 39753870›Full record

ArticleScientific reports2025

Evaluating the effect of viral posts on social media engagement.

Emanuele Sangiorgio, Niccolò Di Marco, Gabriele Etta, Matteo Cinelli, Roy Cerqueti, Walter Quattrociocchi

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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.

Emanuele SangiorgioDepartment of Social Sciences and Economics, Sapienza University of Rome, Rome, Italy. emanuele.sangiorgio@uniroma1.it.
Niccolò Di MarcoDepartment of Computer Science, Sapienza University of Rome, Rome, Italy.
Gabriele EttaDepartment of Computer Science, Sapienza University of Rome, Rome, Italy.
Matteo CinelliDepartment of Computer Science, Sapienza University of Rome, Rome, Italy.
Roy CerquetiDepartment of Social Sciences and Economics, Sapienza University of Rome, Rome, Italy.
Walter QuattrociocchiDepartment of Computer Science, Sapienza University of Rome, Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As virality has become increasingly central in shaping information sources' strategies, it raises concerns about its consequences for society, particularly when referring to the impact of viral news on the public discourse. Nonetheless, there has been little consideration of whether these viral events genuinely boost the attention received by the source. To address this gap, we analyze content timelines from over 1000 European news outlets from 2018 to 2023 on Facebook and YouTube, employing a Bayesian structural time series model to evaluate the impact of viral posts. Our results show that most viral events do not significantly increase engagement and rarely lead to sustained growth. We identify two primary types of viral effects corresponding to different mechanisms of collective attention response. A 'loaded-type' virality manifests after a sustained growth phase, representing its final burst, followed by a decline in attention. A 'sudden-type' virality, with news emerging unexpectedly, reactivates the collective response process. Moreover, quick viral effects fade faster, while slower processes lead to more persistent growth. These findings highlight the transient nature of viral events and underscore the importance of consistent, steady attention-building strategies to establish a solid connection with the user base rather than relying on sudden visibility spikes.

Indexed as

Social MediaAttentionBayes TheoremHumansInformation DisseminationAttention economySocial mediaVirality

Identifiers

PMID39753870
PMCPMC11699135

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

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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.