Evidence map›Paper›PMID 36395259›Full record

ArticlePloS one2022

Census of Twitter users: Scraping and describing the national network of South Korea.

Lu Guan, Xiao Fan Liu, Wujiu Sun, Hai Liang, Jonathan J H Zhu

Abstract read
In one paragraph

Article in PloS one, 2022. 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

5 authors.

Lu GuanSchool of Journalism, Fudan University, Shanghai, China.ORCID 0000-0002-9309-2834
Xiao Fan LiuWeb Mining Laboratory, Department of Media and Communication, City University of Hong Kong, Kowloon, Hong Kong, China.ORCID 0000-0001-8342-4623
Wujiu SunSchool of Computer Science and Engineering, Southeast University, Nanjing, China.ORCID 0000-0002-7256-4716
Hai LiangSchool of Journalism and Communication, The Chinese University of Hong Kong, Shatin, Hong Kong, China.
Jonathan J H ZhuWeb Mining Laboratory, Department of Media and Communication, City University of Hong Kong, Kowloon, Hong Kong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Population-level national networks on social media are precious and essential for network science and behavioural science. This study collected a population-level Twitter network, based on both language and geolocation tags. We proposed a set of validation approaches to evaluate the validity of our datasets. Finally, we re-examined classical network and communication propositions (e.g., 80/20 rule, six degrees of separation) on the national network. Our dataset and strategy would flourish the data collection pool of population-level social networks and further develop the research of network analysis in digital media environment.

Indexed as

CensusesSocial MediaCommunicationHumansInternetSocial Networking

Identifiers

PMID36395259
PMCPMC9671467

What OpenQuestion holds

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LicenceCC BY
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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.