Factors influencing knowledge contribution: An empirical investigation of social networking website users

Purpose: The purpose of this study is to develop an automated frequently asked question (FAQ) answering system for farmers. This paper presents an approach for calculating the similarity between Chinese sentences based on hybrid strategies. Design/methodology/approach: We analyzed the factors influencing the successful matching between a user's question and a question-answer (QA) pair in the FAQ database. Our approach is based on a combination of multiple factors. Experiments were conducted to test the performance of our method. Findings: Experiments show that this proposed method has higher accuracy. Compared with similarity calculation based on TF-IDF, the sentence surface forms and the semantic relations, the proposed method based on hybrid strategies has a superior performance in precision, recall and F-measure value. Research limitations: The FAQ answering system is only capable of meeting users' demand for text retrieval at present. In the future, the system needs to be improved to meet users' demand for retrieving images and videos. Practical implications: This FAQ answering system will help farmers utilize agricultural information resources more effi ciently. Originality/value: We design the algorithms for calculatingn similarity of Chinese sentences based on hybrid strategies, which integrate the question surface similarity, the question semantic similarity and the question-answer similarity based on latent semantic analysis(LSA) to find answers to a user's question.