Evidence map›Paper›PMID 34341778›Full record

ArticleSN computer science2021

Topics, Sentiments, and Emotions Triggered by COVID-19-Related Tweets from IRAN and Turkey Official News Agencies.

Waseem Ahmad, Bang Wang, Han Xu, Minghua Xu, Zeng Zeng

Abstract readNews
PubMed Full text at PMC Publisher
In one paragraph

Article in SN computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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.

Waseem AhmadSchool of Electronic Information and Communications, Huazhong University of Science and Technology (HUST), Wuhan, China.
Bang WangSchool of Electronic Information and Communications, Huazhong University of Science and Technology (HUST), Wuhan, China.
Han XuSchool of Journalism and Information Communication, Huazhong University of Science and Technology (HUST), Wuhan, China.ORCID 0000-0001-9861-4868
Minghua XuSchool of Journalism and Information Communication, Huazhong University of Science and Technology (HUST), Wuhan, China.
Zeng ZengInstitute for Infocomm Research, Agency for Science, Technology and Research (ASTAR), Singapore, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There is no doubt that the COVID-19 epidemic posed the most significant challenge to all governments globally since January 2020. People have to readapt after the epidemic to daily life with the absence of an effective vaccine for a long time. The epidemic has led to society division and uncertainty. With such issues, governments have to take efficient procedures to fight the epidemic. In this paper, we analyze and discuss two official news agencies' tweets of Iran and Turkey by using sentiment- and semantic analysis-based unsupervised learning approaches. The main topics, sentiments, and emotions that accompanied the agencies' tweets are identified and compared. The results are analyzed from the perspective of psychology, sociology, and communication.

Indexed as

COVID-19Emotion classificationSentiment analysisTopic modeling

Identifiers

PMID34341778
PMCPMC8319903

What OpenQuestion holds

Textmetadata
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.