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In the era of Big Data, network digital resources are growing rapidly, especially the short-text resources, such as tweets, comments, messages and so on, are showing a vigorous vitality. This study aims to compare the categories discriminative capacity (CDC) of Chinese language fragments with different granularities and to explore and verify feasibility, rationality and effectiveness of the low-granularity feature, such as Chinese characters in Chinese short-text classification (CSTC).

This study takes discipline classification of journal articles from CSSCI as a simulation environment. On the basis of sorting out the distribution rules of classification features with various granularities, including keywords, terms and characters, the classification effects accessed by the SVM algorithm are comprehensively compared and evaluated from three angles of using the same experiment samples, testing before and after feature optimization, and introducing external data.

The granularity of a classification feature has an important impact on CSTC. In general, the larger the granularity is, the better the classification result is, and vice versa. However, a low-granularity feature is also feasible, and its CDC could be improved by reasonable weight setting, even exceeding a high-granularity feature if synthetically considering classification precision, computational complexity and text coverage.

This is the first study to propose that Chinese characters are more suitable as descriptive features in CSTC than terms and keywords and to demonstrate that CDC of Chinese character features could be strengthened by mixing frequency and position as weight.

The citation counts are an important indicator of scholarly impact. The purpose of this paper is to explore the correlation between citations of scientific articles and writing styles of abstracts in papers and capture the characteristics of highly cited papers' abstracts.

This research selected 10,000 highly cited papers and 10,000 zero-cited papers from the WOS (2008-2017) database. The Coh-Metrix 3.0 textual cohesion analysis tool was used to quantify the 108 language features of highly cited and zero-cited paper abstracts. The differences of the indicators with significant differences were analyzed from four aspects: vocabulary, sentence, syntax and readability.

The abstracts of highly cited papers contain more complex and professional words, more adjectives, adverbs, conjunctions and personal pronouns, but fewer nouns and verbs. The sentences in the abstracts of highly cited papers are more complex and the sentence length is relatively longer. The syntactic structure in abstracts of highly cited papers is relatively more complex and syntactic similarities between sentences are fewer. Highly cited papers' abstracts are less readable than zero-cited papers' abstracts.

This study analyses the differences between the abstracts of highly cited and those of zero-cited papers, reveals the common external and deep semantic features of highly cited papers in abstract writing styles, provide suggestions for researchers on abstract writing. These findings can help increase the scientific impact of articles and improve the review efficiency as well as the researchers' abstract writing skills.

This paper is written as an attempt to employ the Chinese Social Science Citation Index (CSSCI) in the evaluation of Chinese humanities and social science research.

This paper uses statistics in the CSSCI (2000‐2004) to analyze the academic impact of researchers, papers and works, institutions and regions on Chinese humanities and social science research.

The authors identify 100 highly cited people, 50 highly cited papers, 50 highly cited works, 20 highly productive institutions and 20 highly cited institutions. Also provided is some regional information about Chinese humanities and social science research.

It is hoped that the CSSCI, as well as the analysis and evaluation based on it, will give researchers a better understanding of Chinese humanities and social science research.