Preprint Data Descriptor Version 1 Preserved in Portico This version is not peer-reviewed

Multimodal Social Network Dataset Based on Goldbach Conjecture Proved Event in Zhihu

Version 1 : Received: 25 November 2021 / Approved: 26 November 2021 / Online: 26 November 2021 (14:23:36 CET)

How to cite: Liu, T.; Geng, S.; Huang, Z.; Wu, S.; Wang, Z. Multimodal Social Network Dataset Based on Goldbach Conjecture Proved Event in Zhihu. Preprints 2021, 2021110511 (doi: 10.20944/preprints202111.0511.v1). Liu, T.; Geng, S.; Huang, Z.; Wu, S.; Wang, Z. Multimodal Social Network Dataset Based on Goldbach Conjecture Proved Event in Zhihu. Preprints 2021, 2021110511 (doi: 10.20944/preprints202111.0511.v1).

Abstract

At the end of 2018, a high school student asked a question in Zhihu community, claiming that he had proved Goldbach's conjecture. The problem caused an explosive reaction and a large number of users participated in the discussion. And has caused the widespread influence. On January 1, 2019, the questioner issued his "proof". His proof was soon proved wrong. The heated discussion caused by the incident contains a lot of information of social science analysis value. Therefore, we follow up the event in the first time and build a time series dataset for the event. Taking the questioner's "proof" as the dividing line, all the answers, comments, sub comments and user information of writing these texts before and after two days were recorded. This series of temporal information can reflect the dynamic features of the interaction between user opinions, and the impact of exogenous shocks (proof release) on community opinions. The dataset can be used not only for the demonstration of various social network analysis algorithms, but also for a series of natural language processing tasks such as fine-grained sentiment analysis for long texts, as well as multimodal tasks combining natural language processing and social network analysis. This paper introduces the characteristics and structure of the dataset, shows the visualization effect of social network, and uses the dataset train the benchmark model of emotion analysis.

Supplementary and Associated Material

Keywords

Social network analysis; Natural language processing; Dataset; Multimode; Opinion Dynamics

Subject

MATHEMATICS & COMPUTER SCIENCE, Information Technology & Data Management

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