Detecting Sentiment toward Emerging Infectious Diseases on Social Media: A Validity Evaluation of Dictionary-Based Sentiment Analysis.

Sanguk Lee, Siyuan Ma, Jingbo Meng, Jie Zhuang, Tai-Quan Peng

Abstract read

In one paragraph

Article in International journal of environmental research and public health, 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

–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.

Spatial-Temporal Pattern Evolution of Public Sentiment Responses to the COVID-19 Pandemic in Small Cities of China: A Case Study Based on Social Media Data Analysis.International journal of environmental research and public health · 2022
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.

Sanguk LeeDepartment of Communication, Michigan State University, East Lansing, MI 48824, USA.

Siyuan MaDepartment of Communication, Michigan State University, East Lansing, MI 48824, USA.

Jingbo MengDepartment of Communication, Michigan State University, East Lansing, MI 48824, USA.

Jie ZhuangBob Schieffer College of Communication, Texas Christian University, Fort Worth, TX 76129, USA.

Tai-Quan PengDepartment of Communication, Michigan State University, East Lansing, MI 48824, USA.ORCID 0000-0002-2588-7491

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite the popularity and efficiency of dictionary-based sentiment analysis (DSA) for public health research, limited empirical evidence has been produced about the validity of DSA and potential harms to the validity of DSA. A random sample of a second-hand Ebola tweet dataset was used to evaluate the validity of DSA compared to the manual coding approach and examine the influences of textual features on the validity of DSA. The results revealed substantial inconsistency between DSA and the manual coding approach. The presence of certain textual features such as negation can partially account for the inconsistency between DSA and manual coding. The findings imply that scholars should be careful and critical about findings in disease-related public health research that use DSA. Certain textual features should be more carefully addressed in DSA.

Indexed as

Communicable Diseases, EmergingSocial MediaAttitudeHumansPublic HealthSentiment AnalysisANEWinfectious diseasesLIWCsentiment analysisSentiWordNetvalidity

Identifiers

PMID35682341

DOI10.3390/ijerph19116759

PMCPMC9180278

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.