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The purpose of this paper is to examine the impact of internet censorship, which is represented by the Great Fire Wall, on Chinese internet users’ self-censorship.

A 3×2 factorial experiment (n=315) is designed. Different patterns of censorship (soft censorship, compared censorship, and hard censorship) and the justification of internet regulation are involved in the experiment as two factors. The dependent variable is self-censorship which is measured through the willingness to speak about sensitive issues and the behavior of refusing to sign petitions with true names.

The results show that perceived internet censorship significantly decreases the willingness to talk about sensitive issues and the likelihood of signing petitions with true names. The justification of censorship significantly decreases self-censorship on the behaviors of petition signing. Although there are different patterns of internet censorship that Chinese netizens may encounter, they do not differ from each other in causing different levels of self-censorship.

The subjects are college students who were born in the early 1990s, and the characteristics of this generation may influence the results of the experiment. The measurement of self-censorship could be refined.

The study contributes to the body of literature about internet regulation because it identifies a causal relationship between the government’s internet censorship system and ordinary people’s reaction to the regulation in an authoritarian regime. Unpacking different patterns of censorship and different dimensions of self-censorship depicts the complexity of censoring and being censored.

Tourists with different cultural backgrounds think and behave differently. Accurately capturing and correctly understanding cultural differences will help tourist destinations in product/service planning, marketing communication and attracting and retaining tourists. This research employs Hofstede's cultural dimensions theory to analyse the variations in destination image perceptions of Chinese-speaking and English-speaking tourists to Xiamen, a prominent tourist attraction in China.

The evaluation utilizes a two-stage approach, incorporating LDA and BERT-BILSTM models. By leveraging text mining, sentiment analysis and t-tests, this research investigates the variations in tourists' perceptions of Xiamen across different cultures.

The results reveal that cultural disparities significantly impact tourists' perceived image of Xiamen, particularly regarding their preferences for renowned tourist destinations and the factors influencing their travel experience.

This research pioneers applying natural language processing methods and machine learning techniques to affirm the substantial differences in the perceptions of tourist destinations among Chinese-speaking and English-speaking tourists based on Hofstede's cultural theory. The findings furnish theoretical insights for destination marketing organizations to target diverse cultural tourists through precise marketing strategies and illuminate the practical application of Hofstede's cultural theory in tourism and hospitality.