Evidence map›Paper›PMID 31304299›Full record

ArticleNPJ digital medicine2018

How can we better use Twitter to find a person who got lost due to dementia?

Kelvin K F Tsoi, Nicholas B Chan, Felix C H Chan, Lingling Zhang, Annisa C H Lee, Helen M L Meng

Open access · goldAbstract read
In one paragraph

Article in NPJ digital medicine, 2018. 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
3.0field-weighted citation impact, top 9% of its field
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, 13 citations in OpenAlex.

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 at 1 institution in 1 country.

Kelvin K F Tsoi1Stanley Ho Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong, Sha Tin, Hong Kong.
Nicholas B Chan1Stanley Ho Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong, Sha Tin, Hong Kong.
Felix C H Chan1Stanley Ho Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong, Sha Tin, Hong Kong.
Lingling Zhang1Stanley Ho Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong, Sha Tin, Hong Kong.
Annisa C H Lee3School of Journalism and Communication, The Chinese University of Hong Kong, Sha Tin, Hong Kong.
Helen M L Meng1Stanley Ho Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong, Sha Tin, Hong Kong.
Chinese University of Hong Kong · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Twitter is a social media platform for online message sharing. The aim of this study is to evaluate the effectiveness of using Twitter to search for people who got lost due to dementia. The online messages on Twitter, i.e., tweets, were collected through an Application Programming Interface. Contents of the tweets were analysed. The personal characteristics, features of tweets and types of Twitter users were collected to investigate their associations with whether a person can be found within a month. Logistic regression was used to identify the features that were useful in finding the missing people. Results showed that the young age of the persons with dementia who got lost, having tweets posted by police departments, and having tweets with photos can increase the chance of being found. Social media is reshaping the human communication pathway, which may lead to future needs on a new patient-care model.

Indexed as

CommunicationPublic health

Identifiers

PMID31304299
PMCPMC6550184
OpenAlexW2801856884

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.