Big-Data Based Analysis for Communication Effect of Science-Technology Public Accounts on Social Media

Introduction

Since the 21st century, the social media environment has become more and more digitized, socialized and mobile (Ren, Cao, Liu, Huang & Zhu, 2017). The mode of S-T communication on the traditional Internet is gradually adapted to the mobile Internet. Therefore, in the era of emerging mobile Internet, the public accounts on social media, such as WeChat, have opened up new channels for S-T communications. Social media has become a major channel of S-T diffusions. However, due to the high degree of professionalism and rigor, S-T achievements are disseminating by relatively smaller number institutions or individuals compared with content in other fields, thus communication effects are not satisfactory (Zhou, Xu, Wang, Zhu, Luo, Deng &Wu, 2016). Most scientific research institutions and enterprises who have set up STPA-SM are just following the trend of the Internet development, but the actual results are not ideal yet.

Based on the propagation characteristics and operation situation of STPA-SM, this paper proposes a series of hypotheses regarding the influence degree of different factors on communication effects and constructs a measure indicator system that affects the communication effect of STPA-SM. We collect 7,246 articles on STPA-SM from QingBo big data platform and study the influence degree of different factors to communication effect combining techniques with NN and MLR model. Finally, this work provides adaptive operation strategies for STPA-SM to improve its’ communication effect in the social media era.