Evidence map›Paper›PMID 36891545›Full record

ArticleCurrent research in food science2023

Analysis of public opinion on food safety in Greater China with big data and machine learning.

Haoyang Zhang, Dachuan Zhang, Zhisheng Wei, Yan Li, Shaji Wu, Zhiheng Mao, Chunmeng He, Haorui Ma, Xin Zeng, Xiaoling Xie and 2 more

Abstract read
In one paragraph

Article in Current research in food science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Artificial intelligence in food safety.NPJ science of food · 2026
    Review
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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

12 authors.

Haoyang ZhangDepartment of Agrotechnology & Food Sciences, Wageningen University and Research, 6708 PB, Wageningen, the Netherlands.
Dachuan ZhangInstitute of Environmental Engineering, ETH Zurich, 8093, Zurich, Switzerland.
Zhisheng WeiState Key Laboratory of Food Science and Technology, School of Biotechnology, Jiangnan University, Wuxi, 214122, China.
Yan LiCollege of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, 710119, China.
Shaji WuSchool of Perfume and Aroma, Shanghai Institute of Technology, Shanghai, 200333, China.
Zhiheng MaoCollege of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, 710119, China.
Chunmeng HeCollege of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, 710119, China.
Haorui MaCollege of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, 710119, China.
Xin ZengCollege of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, 710119, China.
Xiaoling XieCollege of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, 710119, China.
Xingran KouSchool of Perfume and Aroma, Shanghai Institute of Technology, Shanghai, 200333, China.
Bingwen ZhangDepartment of Food Science and Nutrition, University of Jinan, Jinan, 250002, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Internet contains a wealth of public opinion on food safety, including views on food adulteration, food-borne diseases, agricultural pollution, irregular food distribution, and food production issues. To systematically collect and analyze public opinion on food safety in Greater China, we developed IFoodCloud, which automatically collects data from more than 3,100 public sources. Meanwhile, we constructed sentiment classification models using multiple lexicon-based and machine learning-based algorithms integrated with IFoodCloud that provide an unprecedented rapid means of understanding the public sentiment toward specific food safety incidents. Our best model's F1 score achieved 0.9737, demonstrating its great predictive ability and robustness. Using IFoodCloud, we analyzed public sentiment on food safety in Greater China and the changing trend of public opinion at the early stage of the 2019 Coronavirus Disease pandemic, demonstrating the potential of big data and machine learning for promoting risk communication and decision-making.

Indexed as

Big dataDeep learningFoodinformaticsMachine learningNatural language processing

Identifiers

PMID36891545
PMCPMC9988419

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.