Data-driven Approach for Quality Evaluation on Knowledge Sharing Platform

TL;DR

This paper proposes a data-driven method to automatically evaluate the quality of voice-based knowledge sharing platforms, specifically Zhihu Live, and introduces an open dataset for further research in this area.

Contribution

It introduces a novel data-driven approach for quality assessment and provides an open dataset for future research on knowledge sharing platform evaluation.

Findings

The proposed method effectively evaluates platform quality.

The dataset supports further research and method development.

Experiments confirm the approach's effectiveness.

Abstract

In recent years, voice knowledge sharing and question answering (Q&A) platforms have attracted much attention, which greatly facilitate the knowledge acquisition for people. However, little research has evaluated on the quality evaluation on voice knowledge sharing. This paper presents a data-driven approach to automatically evaluate the quality of a specific Q&A platform (Zhihu Live). Extensive experiments demonstrate the effectiveness of the proposed method. Furthermore, we introduce a dataset of Zhihu Live as an open resource for researchers in related areas. This dataset will facilitate the development of new methods on knowledge sharing services quality evaluation.