Evidence map›Paper›PMID 35721404›Full record

ArticlePeerJ. Computer science2022

A longitudinal study of topic classification on Twitter.

Mohamed Reda Bouadjenek, Scott Sanner, Zahra Iman, Lexing Xie, Daniel Xiaoliang Shi

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ. Computer science, 2022. 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
0.5field-weighted citation impact, top 41% 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

1 citing paper in PubMed, 8 citations in OpenAlex.

  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

5 authors at 4 institutions in 3 countries.

Mohamed Reda BouadjenekSchool of Information Technology, Deakin University, Geelong, Victoria, Australia.ORCID 0000-0003-1807-430X
Scott SannerDepartment of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada.
Zahra ImanComputer Science, Oregon State University, Corvalis, Oregon, United States.
Lexing XieComputer Science, Australian National University, Canberra, ACT, Australia.
Daniel Xiaoliang ShiDepartment of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada.
University of Toronto · CAAustralian National University · AUDeakin University · AUOregon State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Twitter represents a massively distributed information source over topics ranging from social and political events to entertainment and sports news. While recent work has suggested this content can be narrowed down to the personalized interests of individual users by training topic filters using standard classifiers, there remain many open questions about the efficacy of such classification-based filtering approaches. For example, over a year or more after training, how well do such classifiers generalize to future novel topical content, and are such results stable across a range of topics? In addition, how robust is a topic classifier over the time horizon,

Indexed as

Data analysisSocial network analysisTopic classification

Identifiers

PMID35721404
PMCPMC9202616
OpenAlexW2620887933

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.