Users' attention behaviors and features in internet forum

Purpose: Attention resource is scarce. Organizing community activities in online forums faces the challenge of attracting users' limited attention. Understanding how users of online forums allocate, maintain, and change their attentional focus and what features of online forms influence their attention behaviors is critical for effective information design. This paper seeks understanding of users' attention behaviors and features when they participate in discussions in online forums. Design/methodology/approach: A conceptual model was established to explore the indicator system of attention's measurement. The related attention data were collected from Alexa Access Statistics Tool and Katie community. Then this paper computed the correlation coefficient and regression relationship between the indicators of visual attention and cognitive attention. Thereafter this paper analyzed and discussed users' attention behaviors and features in Internet forum. Findings: Relevant bivariate correlation analysis and regression analysis discovers that Internet forum's attention is mainly as visual attention in users' early involvement. Attention resources can be transformed. In a deep participation, users' cognitive attention is more significant. Meanwhile cognitive attention behaviors' further development will lead to the phenomenon that cognitive attention input is prone to increase faster in the early duration. That means in-depth discussion and interaction are more likely to appear in the early stages of participation. Research limitations/implications: There are some limitations about this study. The indicators are not comprehensive enough because factors affecting the distribution of attention resources in Internet forums are complex. We didn't distinguish different types of Internet forums when we collected the relevant data. Future research will focus more on how to obtain comprehensive attention data. Originality/value: T his paper shows a new perspective that we can find users' attention behaviors and features using the attention data from its mapping object, which can help operators of portals and Internet communities to attract users' limited attention.