Evidence map›Paper›PMID 37679764›Full record

ArticleArchives of public health = Archives belges de sante publique2023

Infodemic: Challenges and solutions in topic discovery and data process.

Jinjin Zhang, Yang Pan, Han Lin, Zhoubao Sun, Pingping Wu, Juan Tu

Abstract read
In one paragraph

Article in Archives of public health = Archives belges de sante publique, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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.

Jinjin ZhangSchool of Computer Science, Nanjing Audit University, Nanjing, China.
Yang PanSchool of Computer Science, Nanjing Audit University, Nanjing, China.
Han LinSchool of Engineering Audit, Jiangsu Key Laboratory of Public Project Audit, Nanjing Audit University, Nanjing, China. linhan@nau.edu.cn.
Zhoubao SunSchool of Engineering Audit, Jiangsu Key Laboratory of Public Project Audit, Nanjing Audit University, Nanjing, China.
Pingping WuSchool of Engineering Audit, Jiangsu Key Laboratory of Public Project Audit, Nanjing Audit University, Nanjing, China.
Juan TuThe Institute of Acoustics, School of Physics, Nanjing University, Nanjing, China.

Funding

Major Project of Natural Science Foundation of Jiangsu Education Department 21KJA630001, 22KJA630001National Natural Science Foundation of China 72271126, 71771125, 12227808
6 · The paper itself

Abstract

backgroundThe Coronavirus Disease 2019 (COVID-19) pandemic was a huge shock to society, and the ensuing information problems had a huge impact on society at the same time. The urgent need to understand the Infodemic, i.e., the importance of the spread of false information related to the epidemic, has been highlighted. However, while there is a growing interest in this phenomenon, studies on the topic discovery, data collection, and data preparation phases of the information analysis process have been lacking.

objectiveSince the epidemic is unprecedented and has not ended to this day, we aimed to examine the existing Infodemic-related literature from January 2019 to December 2022.

methodsWe have systematically searched ScienceDirect and IEEE Xplore databases with some search limitations. From the searched literature we selected titles, abstracts and keywords, and limitations sections. We conducted an extensive structured literature search and analysis by filtering the literature and sorting out the available information.

resultsA total of 47 papers ended up meeting the requirements of this review. Researchers in all of these literatures encountered different challenges, most of which were focused on the data collection step, with few challenges encountered in the data preparation phase and almost none in the topic discovery section. The challenges were mainly divided into the points of how to collect data quickly, how to get the required data samples, how to filter the data, what to do if the data set is too small, how to pick the right classifier and how to deal with topic drift and diversity. In addition, researchers have proposed partial solutions to the challenges, and we have also proposed possible solutions.

conclusionsThis review found that Infodemic is a rapidly growing research area that attracts the interest of researchers from different disciplines. The number of studies in this field has increased significantly in recent years, with researchers from different countries, including the United States, India, and China. Infodemic topic discovery, data collection, and data preparation are not easy, and each step faces different challenges. While there is some research in this emerging field, there are still many challenges that need to be addressed. These findings highlight the need for more articles to address these issues and fill these gaps.

Indexed as

CommunicationData scienceInfodemicResearch topicsReview

Identifiers

PMID37679764
PMCPMC10483774

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.