PurposeIn order to further advance the research of social bots, based on the latest research trends and in line with international research frontiers, it is necessary to understand the global research situation in social bots.Design/methodology/approachChoosing Web of Science™ Core Collections as the data sources for searching social bots research literature, this paper visually analyzes the processed items and explores the overall research progress and trends of social bots from multiple perspectives of the characteristics of publication output, major academic communities and active research topics of social bots by the method of bibliometrics.FindingsThe findings offer insights into research trends pertaining to social bots and some of the gaps are also identified. It is recommended to further expand the research objects of social bots in the future, not only focus on Twitter platform and strengthen the research of social bot real-time detection methods and the discussion of the legal and ethical issues of social bots.Originality/valueMost of the existing reviews are all for the detection methods and techniques of social bots. Unlike the above reviews, this study is a systematic literature review, through the method of quantitative analysis, comprehensively sort out the research output in social bots and shows the latest research trends in this area and suggests some research indirections that need to be focused in the future. The findings will provide references for subsequent scholars to research on social bots.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2021-0336.
To solve the essential objective of LDA-L1, NLDA-L1 proposes a nongreedy algorithm by constructing an auxiliary function. In this correspondence, we show that essentially, this algorithm directly solves the objective using a gradient ascending procedure, meaning that the auxiliary function may be not necessary. Then, we further show that NLDA-L1 is a special case of ILDA-L1, which applies the same iterative procedure of ILDA-L1.
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