With the emergence of online communities, more and more people are participating in online technology communities to meet personalized learning needs. This study aims to investigate whether and how male and female users behave differently in online technology communities. Using text data from the Python Technology Community, through the LDA Latent Dirichlet Allocation model, sentiment analysis, and regression analysis, this paper reveals the different topics of male and female users in the online technology community, their sentimental tendencies and activity under different topics, and their correlation and mutual influence.
The results show the following: 1 Male users tend to provide information help, while female users prefer to participate in the topic of making friends and advertising. The activity of female users is more susceptible to emotional orientation. It is urgent for societal members to make full use of information technology to explore the digital learning mode and meet the growing diversified and personalized learning needs Johnson and Ambrose, The Internet provides an increasingly popular platform for the public; users with common interests or goals can exchange information, express opinions, seek emotional support, and establish social relations with others Lu Y.
Gathering people with similar interests into a group and studying the behavioral differences among different groups is helpful to have a deeper understanding of the interests of each group and provide guidance for understanding the information needs, emotional needs, and relationship needs of different groups Liu et al. It varies from society to society and can be changed. User segmentation by gender is an efficient and audience-oriented viable segmentation method to differentiate markets and services Kraft and Weber, The theories of gender differences mainly include social—cultural theory Wood and Eagly, , anthropology evolution theory Tooby and Cosmides, , and selective hypothesis Meyers-Levy, ; Meyers-Levy and Maheswaran, ; Meyers-Levy and Sternthal, These theories are more complementary, and most of the findings of gender differences can be explained by one or more of the above viewpoints Meyers-Levy and Loken, In addition, all gender research methods now recognize socio-cultural factors such as social and cultural role learning, stereotypes, media, and marketing information.
These theories lay the foundation for the study of gender differences. A traditional limitation of gender difference research is the large sample size required to reach broad conclusions. Today, the emergence of user-generated content solves this problem because the Internet can provide many texts on various topics Teso et al. The second limitation concerns the methodological perspective in the analysis of gender differences. When the data sample is large, it is difficult to carry out qualitative analysis.
On the contrary, text mining is an effective solution Liu et al. In recent years, online community posting text has been used by some scholars to study the use behavior of community users, the role of knowledge dissemination, and other issues Chau and Xu, ; Teso et al. An online technology community is a reliable way to produce high-quality and cost-effective software and technology Mockus et al.
