Studies Based On Dynamics Of Human Behavior In Mobile Reading Online

With the update of terminal equipment and improvement of network technology, the business application on the mobile internet is also in constant development. Mobile reading is one of them and is developing very fast. Although there is strong user base and great market potential, mobile reading is still short of clear profit model, and users are not willing to pay for it. As a result, the operation income brought by mobile reading is still in a low level. In this paper, the theory and approach of human dynamics is used to study the behavioral characteristic of the mobile reading users. The main factors influencing on users’ behavior are also analyzed, and I also put forward some suggestions which are beneficial to the development of mobile reading.Based on the data statistics of mobile reading users’ behavior, this paper found that users’ behavior has some characteristic such as periodicity, correlation and also heavy-tailed. No matter analyzing from the perspective of the readers or the books, the distribution of time interval between two consecutive comments is a power-law with heavy-tailed. The power exponent is different because of the difference of users’ active degree and work state, etc. The higher users’ active degree results in bigger power exponent, and the serial works could raise the frequency of comment, but users would never reduce the comment or click because of cost free. That means the users are often attracted by the works which have a high quality and refresh rate, and free lunch will not earn attention.The users’ behavioral characteristic is caused by the change of their interest. The interest will reduce as time goes on and rise again because of some accidental event. The behavior is also influence by the writers and the website. Based on these, this paper built a human dynamics model impacted by the interest. The simulation result agrees with the empirical result well, which prove that this model could reflect the mobile reading users’ behavioral characteristic. Through the parameter adjustment and model optimization, it is found that if the authors can speed up the rate of update their works or the website can take the approach of personalized recommendation, more readers could been attracted. Therefore, the website is suggested to optimize its recommendation and invite more high-level writers to host.