• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.2
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• pp.440-447
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• 2020

The purpose of this study is to identify the causation and variation among the various types of onion prices in the major production sites to predict these prices. The Granger causal relationship was tested on the basis of VECM by setting the onion price of the early, middle, and late species as individual variables. The analysis shows that the amount of onions produced in the prior term affects the price of onions for the later period, while garlic in the substitution relationship with onions also affects the prices of onions for the early and middle-variety. On the other hand, the price of the late-variety is affected by the price of the early-variety, and the price of the middle-variety is also affected by the price of the early-variety. If the price of onions on Jeju changes due to other factors, the prices of onions in Jeollanam-do and Gyeongsangnam-do provinces will be affected. Accordingly, when the production of late-variety increases or decreases in production under any factor and to promote stability of the prices of middle and late-variety through preemptive supply and demand measures when the prices of ultra-breed onions rise or fall due to any factor (Ed- I cannot understand this last sentence and cannot guess at the correct meaning. Please try to rewrite very simply).

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.179-188
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• 2023

Recently, fake news disguises the form of news content and appears whenever important events occur, causing social confusion. Accordingly, artificial intelligence technology is used as a research to detect fake news. Fake news detection approaches such as automatically recognizing and blocking fake news through natural language processing or detecting social media influencer accounts that spread false information by combining with network causal inference could be implemented through deep learning. However, fake news detection is classified as a difficult problem to solve among many natural language processing fields. Due to the variety of forms and expressions of fake news, the difficulty of feature extraction is high, and there are various limitations, such as that one feature may have different meanings depending on the category to which the news belongs. In this paper, emotional change patterns are presented as an additional identification criterion for detecting fake news. We propose a model with improved performance by applying a convolutional neural network to a fake news data set to perform analysis based on content characteristics and additionally analyze emotional change patterns. Sentimental polarity is calculated for the sentences constituting the news and the result value dependent on the sentence order can be obtained by applying long-term and short-term memory. This is defined as a pattern of emotional change and combined with the content characteristics of news to be used as an independent variable in the proposed model for fake news detection. We train the proposed model and comparison model by deep learning and conduct an experiment using a fake news data set to confirm that emotion change patterns can improve fake news detection performance.