This article examines Sang (丧) subculture within the context of positive energy (正能量) in post-reform China, and how as an emergent subculture it is characterised by feelings of defeatism and loss. Chinese youths share Sang memes via social media as a form of affective identification to communicate their sense of disenchantment with the ‘main melody’ of official discourse in post-reform China, and in this sense it is similar to other Internet cultures such as e’gao and diaosi. However, unlike subcultures in the West, Sang subculture does not constitute a form of political resistance, but expresses instead an inchoate feeling of loss among Chinese youths. This article asks two research questions: how does Sang subculture parody normative subject positions of youth constructed by official state discourse, and what does it reveal about the subjectivity of its participants? This article employs Raymond Williams’ concept of ‘structures of feeling’ within a semiotic framework to analyse three sets of Sang memes to understand the processes of subjectivity formation and the affective significance to its participants. Through semiotic analysis of Sang memes and semi-structured interviews with 20 participants aged between 18 and 26, we find that Sang subculture is a current of thought-feeling due to a perceived incapacity by Chinese youths to live up to the ideological re-positioning within official consciousness.

The relationship between online media platforms in China and fan groups is a dynamic one when it comes to the distribution of international TV series and other media content, as media platforms incorporate user-generated content to encourage or foster audience engagement. Through a series of case studies, this article investigates how international TV series are acquired, distributed, marketed and curated on Chinese online video platforms. This helps to identify specific strategies and themes used by these platforms to promote international content and engage users. These marketing techniques, however, are not always as successful as expected, suggesting the need for a closer examination of the types of engagement sought by media platforms, and the ways in which Chinese audiences have responded within their cultural context.

How online video platforms could support China's independent microfilm (short film) makers and enhance the Chinese film industry. As with the US and EU media landscapes, the Chinese film industry is dominated by platforms similar to Netflix, Hulu and Amazon, most notably in the form of the BAT (Baidu, Alibaba, Tencent) companies that according to He (2015) are 'taking over the film industry'. These have been described as 'imperialistic' in the monopolization of their respective markets and in the use of their financial muscle to squeeze content creators' incomes (Jin, 2015). While in the western market this undermines the mainly middle-class professionals who drive creativity (Timberg, 2015), in China it limits the opportunities for new talent to grow. This paper will, therefore, give an overview of the Chinese microfilm (online short movies) industry and investigate how Chinese BAT companies and other online video providers could enhance the Chinese film industry by developing infrastructure to direct revenue of microfilms to the creators.

Customer reviews and comments on web pages are important information in our daily life. For example, we prefer to choose a hotel with positive comments from previous customers. As the huge amounts of such information demonstrate the characteristics of big data, it places heavy burdens on the assimilation of the customercontributed opinions. To overcoming this problem, we study an efficient opinion summarization approach for a set of massive user reviews and comments associated with an online resource, to summarize the opinions into two categories, i.e., positive and negative. In this paper, we proposed a framework including: (1) overcoming the big data problem of online comments using the efficient online-LDA approach; (2) selecting meaningful topics from the imbalanced data; (3) summarizing the opinion of comments with high precision and recall. This framework is different from much of the previous work in that the topics are pre-defined and selected the topics for better opinion summarization. To evaluate the proposed framework, we perform the experiments on a dataset of hotel reviews for the variety of topics contained. The results show that our framework can gain a significant performance improvement on opinion summarization.

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