Evidence map›Paper›PMID 27936009›Full record

ArticlePloS one2016

Predicting Virtual World User Population Fluctuations with Deep Learning.

Young Bin Kim, Nuri Park, Qimeng Zhang, Jun Gi Kim, Shin Jin Kang, Chang Hun Kim

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2016. 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.

Young Bin KimInterdisciplinary Program in Visual Information Processing, Korea University, Seoul, Korea.ORCID http://orcid.org/0000-0002-2114-0120
Nuri ParkDepartment of Computer and Radio Communications Engineering, Korea University, Seoul, Korea.
Qimeng ZhangInterdisciplinary Program in Visual Information Processing, Korea University, Seoul, Korea.
Jun Gi KimSchool of Games, Hongik University, Seoul, Korea.
Shin Jin KangSchool of Games, Hongik University, Seoul, Korea.
Chang Hun KimDepartment of Computer and Radio Communications Engineering, Korea University, Seoul, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper proposes a system for predicting increases in virtual world user actions. The virtual world user population is a very important aspect of these worlds; however, methods for predicting fluctuations in these populations have not been well documented. Therefore, we attempt to predict changes in virtual world user populations with deep learning, using easily accessible online data, including formal datasets from Google Trends, Wikipedia, and online communities, as well as informal datasets collected from online forums. We use the proposed system to analyze the user population of EVE Online, one of the largest virtual worlds.

Indexed as

InternetLearningUser-Computer InterfaceArtificial IntelligenceComputer-Assisted InstructionComputer Communication NetworksData MiningHumans

Identifiers

PMID27936009
PMCPMC5147861

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.