  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.
