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Register a new userLarge-scale Quantitative Evidence of Media Impact on Public Opinion toward China
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Do mass media influence peoples opinion of other countries? Using BERT, a deep neural network-based natural language processing model, we analyze a large corpus of 267,907 China-related articles published by The New York Times since 1970. We then compare our output from The New York Times to a longitudinal data set constructed from 101 cross-sectional surveys of the American publics views on China. We find that the reporting of The New York Times on China in one year explains 54% of the variance in American public opinion on China in the next. Our result confirms hypothesized links between media and public opinion and helps shed light on how mass media can influence public opinion of foreign countries.
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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. In order to systematically collect and analyse public opinion on food safety, we developed IFoodCloud, a platform for the real-time sentiment analysis of public opinion on food safety in China. It collects data from more than 3,100 public sources that can be used to explore public opinion trends, public sentiment, and regional attention differences of food safety incidents. At the same time, we constructed a sentiment classification model using multiple lexicon-based and deep learning-based algorithms integrated with IFoodCloud that provide an unprecedented rapid means of understanding the public sentiment toward specific food safety incidents. Our best models F1-score achieved 0.9737. Further, three real-world cases are presented to demonstrate the application and robustness. IFoodCloud could be considered a valuable tool for promote scientisation of food safety supervision and risk communication.
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We study the entropy of Chinese and English texts, based on characters in case of Chinese texts and based on words for both languages. Significant differences are found between the languages and between different personal styles of debating partners. The entropy analysis points in the direction of lower entropy, that is of higher complexity. Such a text analysis would be applied for individuals of different styles, a single individual at different age, as well as different groups of the population.
MediaSum, a large-scale media interview dataset consisting of 463.6K transcripts with abstractive summaries. To create this dataset, we collect interview transcripts from NPR and CNN and employ the overview and topic descriptions as summaries. Compared with existing public corpora for dialogue summarization, our dataset is an order of magnitude larger and contains complex multi-party conversations from multiple domains. We conduct statistical analysis to demonstrate the unique positional bias exhibited in the transcripts of televised and radioed interviews. We also show that MediaSum can be used in transfer learning to improve a models performance on other dialogue summarization tasks.
To contain the pandemic of coronavirus (COVID-19) in Mainland China, the authorities have put in place a series of measures, including quarantines, social distancing, and travel restrictions. While these strategies have effectively dealt with the critical situations of outbreaks, the combination of the pandemic and mobility controls has slowed Chinas economic growth, resulting in the first quarterly decline of Gross Domestic Product (GDP) since GDP began to be calculated, in 1992. To characterize the potential shrinkage of the domestic economy, from the perspective of mobility, we propose two new economic indicators: the New Venues Created (NVC) and the Volumes of Visits to Venue (V^3), as the complementary measures to domestic investments and consumption activities, using the data of Baidu Maps. The historical records of these two indicators demonstrated strong correlations with the past figures of Chinese GDP, while the status quo has dramatically changed this year, due to the pandemic. We hereby presented a quantitative analysis to project the impact of the pandemic on economies, using the recent trends of NVC and V^3. We found that the most affected sectors would be travel-dependent businesses, such as hotels, educational institutes, and public transportation, while the sectors that are mandatory to human life, such as workplaces, residential areas, restaurants, and shopping sites, have been recovering rapidly. Analysis at the provincial level showed that the self-sufficient and self-sustainable economic regions, with internal supplies, production, and consumption, have recovered faster than those regions relying on global supply chains.
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