Automatic detection of fake reviews at marketplaces using expert-based features and consumers’ reactions by A. N. Borodulina, E. V. Mikhalkova

“…The classification model confirms that the formal features identified by experts as indicating fake reviews indeed have predictive potential. The quality of the model is reduced by the imbalance in classes and insufficient number of reviews with buyer reactions in our corpus, which leaves room for further work.…”
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Which Emotions of Social Media Users Lead to Dissemination of Fake News: Sentiment Analysis Towards Covid-19 Vaccine by Maide Feyza Er, Yonca Bayrakdar Yılmaz

“…In our study, sentiment analysis was conducted on the reactions of Twitter users to fake news about the COVID-19 vaccine between December 31, 2019 and July 30, 2022. …”
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Detection of Adulterated Honey by Fluorescence Excitation-Emission Matrices by Tatjana Dramićanin, Lea Lenhardt Acković, Ivana Zeković, Miroslav D. Dramićanin

“…Statistical t-test showed that significant differences between fluorescence of natural and adulterated honey samples exist in 5 spectral regions: (1) excitation: 240–265 nm, emission: 370–495 nm; (2) excitation: 280–320 nm, emission: 390–470 nm; (3) excitation: 260–285 nm, emission: 320–370 nm; (4) excitation: 310–360 nm, emission: 370–470 nm; and (5) excitation: 375–435 nm, emission: 440–520 nm, in which majority of fluorescence comes from the aromatic amino acids, phenolic compounds, and fluorescent Maillard reaction products. Principal component analysis confirmed these findings and showed that 90% of variance in fluorescence is accumulated in the first two principal components, which can be used for the discrimination of fake honey samples. …”
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