How to Write High-quality News on Social Network? Predicting News Quality by Mining Writing Style
This addresses the challenge of assisting writers in improving news quality on social networks, though it is incremental as it builds on existing writing guidelines and uses popularity as a proxy.
The paper tackled the problem of automatically analyzing how writing style influences news quality on social networks by using popularity as a proxy for quality and designing linguistic features based on writing guidelines. The result was a model (SNQAM) that performs excellently in predicting quality, providing interpretable scores and suggestions for improvement.
Rapid development of Internet technologies promotes traditional newspapers to report news on social networks. However, people on social networks may have different needs which naturally arises the question: whether can we analyze the influence of writing style on news quality automatically and assist writers in improving news quality? It's challenging due to writing style and 'quality' are hard to measure. First, we use 'popularity' as the measure of 'quality'. It is natural on social networks but brings new problems: popularity are also influenced by event and publisher. So we design two methods to alleviate their influence. Then, we proposed eight types of linguistic features (53 features in all) according eight writing guidelines and analyze their relationship with news quality. The experimental results show these linguistic features influence greatly on news quality. Based on it, we design a news quality assessment model on social network (SNQAM). SNQAM performs excellently on predicting quality, presenting interpretable quality score and giving accessible suggestions on how to improve it according to writing guidelines we referred to.