Research on Visual Analysis of Popular Science Content Dissemination Hotspots

DOI

10.2991/978-94-6463-192-0_79How to use a DOI?

Keywords

Web crawler; Bayesian algorithm; Hot Spot Analysis; scientific popularization

Abstract

In this paper, crawler technology is used to obtain data sets, and after automatic classification, visualization technology is used to analyze and study popular science content. The crawler technology uses the Python web crawler library, the automatic classification algorithm is based on Poisson distribution of Bayesian algorithm, and the visualization is realized through tableau. This paper observes and analyzes the data sets within the selected range from a new perspective, text clustering and research the digital dissemination of popular science content by hot spot analysis of the distribution of popular science news, so as to provide decision-making service reference for science popularization workers.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Xing Xu
AU  - Jiya Jiang
AU  - Yonglian Wei
AU  - Wei He
PY  - 2023
DA  - 2023/07/04
TI  - Research on Visual Analysis of Popular Science Content Dissemination Hotspots
BT  - Proceedings of the 2023 2nd International Conference on Educational Innovation and Multimedia Technology (EIMT 2023)
PB  - Atlantis Press
SP  - 609
EP  - 614
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6463-192-0_79
DO  - 10.2991/978-94-6463-192-0_79
ID  - Xu2023
ER  -