Open Access
Research Article
An automated approach for temporal specificity classification of user-generated content
Yanni Yangyangyanni@ctgu.edu.cnAffiliation 1School of Literature and MediaChina Three Gorges UniversityYichang, Hubei, 443000, ChinaAffiliation 2School of Information ManagementCentral China Normal UniversityWuhan, Hubei, 430079, ChinaView full profile → , Jiawei Zhuzhujiawei0522@163.comSchool of Literature and MediaChina Three Gorges UniversityYichang, Hubei, 443000, ChinaView full profile → , *Qian WuCorresponding authorwuqian@mails.ccnu.edu.cnSchool of Information ManagementCentral China Normal UniversityWuhan, Hubei, 430079, ChinaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 07 Aug 2024
- Published Online:
- 12 Jun 2025
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1938
- Pages:
- 1679–1689
Abstract
The temporal specificity of user-generated content (UGC) affects the information recommendation accuracy in online question answering (Q&A) community. Exploring the method for the temporal specificity classification of UGC is beneficial to optimize the content organization of online Q&A communities. In assessments of temporal specificity of UGC, it is relatively one-sided to judge only by the post time of the content. The relationship between the content’s topic category and temporal specificity is often ignored. To solve these problems, an automated approach for assessing the temporal specificity of user-generated content is proposed. The temporal specificity of UGC in the online Q&A community was divided into three types: high temporal specificity, medium temporal specificity, and low temporal specificity. The temporal specificity of UGC is automatically classified based on the Word2Vec-XGBoost algorithm, and question text for different topics from the Zhihu Q&A community is collected for experimental verification. The results show that the classification’s accuracy, recall, and F1 score with weighted temporal specificity are increased by 2.63%, 2.67%, and 2.69%, respectively, compared to those in the base case. The overall accuracy, recall, and F1 score reached 90.30%, 90.24%, and 90.12%, respectively, and the temporal specificity classification performance was good.
Keywords
Subject Classifications
Primary 68T50Secondary 03B65
References
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