Evidence map›Paper›PMID 38163247›Full record

ArticleHeliyon2024

Social media network analysis of Smallholder livestock farming communities in the United Kingdom.

Samuel Munaf, Kevin Swingler, Franz Brulisauer, Anthony O'Hare, George Gunn, Aaron Reeves

Abstract read
In one paragraph

Article in Heliyon, 2024. 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

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

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

6 authors.

Samuel MunafDivision of Computing Science and Mathematics, University of Stirling, Stirling, United Kingdom.
Kevin SwinglerDivision of Computing Science and Mathematics, University of Stirling, Stirling, United Kingdom.
Franz BrulisauerSRUC Veterinary Services, Scotland's Rural College (SRUC), Inverness, United Kingdom.
Anthony O'HareDivision of Computing Science and Mathematics, University of Stirling, Stirling, United Kingdom.
George GunnCentre for Epidemiology and Planetary Health, Department of Veterinary and Animal Sciences, Northern Faculty, Scotland's Rural College (SRUC), Inverness, United Kingdom.
Aaron ReevesCentre for Applied public health research, RTI international, Raleigh, NC, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The creation of targeted policies and actions to help small-scale livestock keepers and reduce the risks associated with disease outbreaks in this sector is hampered by the scarcity of information about smallholder farmers. Smallholders play a crucial part in disease outbreaks containment, hence there is a need for better monitoring methods that take this population into account while gathering data. According to the literature, these communities frequently use social media as a channel for communication and information exchange. In this study we conducted social network analysis of an influential smallholder within the UK and visualised the user follower network. Additionally, we performed influential user analysis, Twitter user categorisation, and community detection to uncover more insights into the livestock farming networks. Our findings reveal distinct communities within the smallholder farming sector and identify influential users with the potential to impact information dissemination and animal health practices. The study also highlights the role of community structure in surveillance and control of animal diseases and emphasises the need for further research to refine our understanding of these communities and their unique characteristics. This work contributes to the growing body of literature on small-scale livestock farming in the UK and underscores the importance of incorporating smallholder communities into disease surveillance and control efforts.

Indexed as

Information dispersionLivestock farmingnetwork analysisSmallholdingssocial media

Identifiers

PMID38163247
PMCPMC10757016

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.