With the travel and tourism industry flourishing worldwide, it is vital for tourism supplier and markers to understand tourist traits aiming to target consumers and assist decision makings. However, traditional tourist analysis methods, e.g. questionnaire survey, are labor-intensive and time-consuming. With the development of social networks, tourists publish large quantities of travel experiences on them, which enables to discover traditional tourist traits and more new traits which could not be obtained from traditional questionnaires. In this paper, we design a methodology for tourism traits analysis from social networks, which is based on the social learning theory. The methodology includes three components: tourist demographic analysis, tourist social influences analysis, and tourist behaviour analysis. For demographic analysis, it comprises the analysis of gender, age, location, education, profession, and interests for tourists. Regarding social influences analysis, it contains the analysis of follower count, post count, account type, and follower/followee ratio of tourists. The analysis of post pattern, travel frequency, type of tourism-related products, and top visited destinations consists of tourist behaviour analysis. We conduct a case study, which is related on the Chinese tourists toward Switzerland based on our methodology and social media big data analysis from Sina Weibo. The concerning data is collected by project SWICICO from HES-SO Valais in Switzerland. Different significant findings are obtained and some examples are given: the Chinese tourists in Switzerland tend to be young people, are likely to have college experiences. They have interests in travel, sport, art, and education etc, and most of them are from higher economic developed cities. They tend to have higher social influences. Besides, they tend to travel to Switzerland in June, July, August, October and February, and most of them are their first-time travel. The top visited destinations are Jungfrau, Interlaken, Zurich and so on, top-ranking products and services are chocolate, cheese, exhibition in Basel, auto show etc. Those findings could empower tourism suppliers and markers to better align the market efforts while making lasting, meaningful market strategies. And our proposed methodology could be applied to analyse tourist traits in any social network platform.