Evidence map›Paper›PMID 40017812›Full record

ArticleSocial network analysis and mining2024

Comparing methods for creating a national random sample of twitter users.

Meysam Alizadeh, Darya Zare, Zeynab Samei, Mohammadamin Alizadeh, Mael Kubli, Mohammadhadi Aliahmadi, Sarvenaz Ebrahimi, Fabrizio Gilardi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Social network analysis and mining, 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

8 authors.

Meysam AlizadehDepartment of Political Science, University of Zurich, Zurich, Switzerland.
Darya ZareDepartment of Computer Engineering, Amirkabir University of Technology, Tehran, Iran.
Zeynab SameiDepartment of Computer Science, Institute For Research In Fundamental Sciences, Tehran, Iran.
Mohammadamin AlizadehDepartment of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran.
Mael KubliDepartment of Political Science, University of Zurich, Zurich, Switzerland.
Mohammadhadi AliahmadiDepartment of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
Sarvenaz EbrahimiFaculty of Entrepreneurship, University of Tehran, Tehran, Iran.
Fabrizio GilardiDepartment of Political Science, University of Zurich, Zurich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Twitter data has been widely used by researchers across various social and computer science disciplines. A common aim when working with Twitter data is the construction of a random sample of users from a given country. However, while several methods have been proposed in the literature, their comparative performance is mostly unexplored. In this paper, we implement four common methods to create a random sample of Twitter users in the US: Supplementary Information: The online version contains supplementary material available at. 10.1007/s13278-024-01327-5.

Identifiers

PMID40017812
PMCPMC11861139

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.