Search

In recent years, due to the booming development of online social networks, fake news has been appearing in large numbers and widespread in the online world. With deceptive words, online social network users can get infected by these online fake news easily, which has brought about tremendous effects on the offline society already. An important goal in improving the trustworthiness of information in online social networks is to identify the fake news timely. However, fake news detection remains to be a challenge, primarily because the content is crafted to resemble the truth in order to deceive readers, and without fact-checking or additional information, it is often hard to determine veracity by text analysis alone. In this paper, we first proposed multi-level convolutional neural network (MCNN), which introduced the local convolutional features as well as the global semantics features, to effectively capture semantic information from article texts which can be used to classify the news as fake or not. We then employed a method of calculating the weight of sensitive words (TFW), which has shown their stronger importance with their fake or true labels. Finally, we develop MCNN-TFW, a multiple-level convolutional neural network-based fake news detection system, which is combined to perform fake news detection in that MCNN extracts article representation and WS calculates the weight of sensitive words for each news. Extensive experiments have been done on fake news detection in cultural communication to compare MCNN-TFW with several state-of-the-art models, and the experimental results have demonstrated the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Objective: To analyze the association between dietary fat intake and the risk of polycystic ovarian syndrome(PCOS)., Methods: PCOS patients treated in a tertiary hospital in Anhui Province from October 2021 to October 2022 were selected as the case group, and non-PCOS patients treated in the hospital during the same period were selected as the control group. A total of 262 subjects were included in the study, 131 were included in the case group and 131 in the control group. A semi-quantitative dietary frequency questionnaire was used to investigate the dietary intake in the past year, and the daily intake of various fatty acids and the ratio of fatty acid energy supply were calculated according to the food intake. Logistic regression analysis was used to investigate the association between dietary fat intake and the risk of PCOS., Results: The dietary intakes of total fat, fatty acid, saturated fatty acid and monounsaturated fatty acid in PCOS patients were higher than those in control group(P>0.05), and there was statistical significance in daily intakes of eicosapentaenoic acid between two groups(P<0.05). After adjusting for confounding factors such as long-term residence, occupation, family per capita monthly income, menstrual cycle regularity, menstrual volume, and weight loss experience, Logistic regression analysis showed that the ratio of fat supply to energy was positively correlated with the risk of PCOS(OR=1.622, 95%CI 1.237-2.127). The energy supply ratio of monosaturated fatty acids(OR=0.597, 95%CI 0.373-0.955) and polyunsaturated fatty acids(OR=0.585, 95%CI 0.372-0.921) were negatively correlated with the risk of PCOS(P<0.05)., Conclusion: The energy supply ratio of fat was positively correlated with the risk of PCOS, while the energy supply ratio of monosaturated fatty acids and the energy supply ratio of polyunsaturated fatty acids were negatively correlated with the risk of PCOS.