Feature Extraction of the Information Clues in the Data Journalism

Nowadays, data journalism was becoming more and more complex in the fields of visual presentation, and which design strategies should be adopted to improve users’ reading efficiency was of great importance for data journalism producer. The purpose of this research was to extract the features of information clues that have a direct impact on the users’ visual perception and information foraging behavior, and use these features to describe the impact of the information clues. First, respondents were interviewed on the basis of understanding of the research material, and then use thematic analysis method to encode, so as to extract the features of the information clues in the data journalism. The results showed that the information clues in the data journalism can be divided into “structural features” and “semantic features” and different types of information clues can affect users’ information foraging behavior at different levels as a whole. Our findings provide a new perspective for the pattern classification and recognition of the information clues in the data journalism and refine the explanation of users’ news reading behavior mechanism.