Visual analysis of global research trends in social bots based on bibliometrics

Abstract

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.

Examining the differences between human and bot social media accounts: A case study of the Russia-Ukraine War

“…The rest 175 tweets were used for model validation. After model validation, the accuracy of our machine learning model turned out to be 98.6 percent, which outperformed those of previous studies using the SVM multi-class model (Guo, et al, 2020;Chen, et al, 2022). In addition, we also performed an extra validity check for the outcomes of the model.…”

“…There are four existing common bot detection techniques: graph-based, machine learning-based, crowdsourcing-based, and anomaly-based (Orabi, et al, 2020). In this study, we adopted the machine learning approach, which is the most widely used approach (Chen, et al, 2022). Botometer, developed by Yang, et al (2022), has been proven to be a relatively reliable tool for social bot detection.…”

“…Different criteria were proposed in the existing literature to identify bots. Some researchers use a bot score of 0.5 as the threshold for marking social media bots (Badawy, et al, 2018;Ferrara, 2017a;Shao, et al, 2018;Chen, et al, 2022). In this study, we adopted a higher threshold (0.8) for bot identification.…”

“…Therefore, we decided to code the political stance of the sampled tweets through machine learning. Following previous studies, we used the Support Vector Machine (SVM), a stable multi-class classification machine learning model, to classify stances and attitudes of tweets (Joachims, 1998;Chen, et al, 2022). First, we set up a coding team consisting of postgraduate students.…”

“…Social media is no longer a tool of communication but also a potential 'weapon' that can greatly affect human perceptions and opinions (Orabi, et al, 2020). Empirical studies suggest that social media has an impact on opinion formation and transformation (Chen, et al, 2022), emotional contagion (Ferrara and Yang, 2015), and the flow of public opinion (Bradshaw and Howard, 2018;Cheng, et al, 2020). Such effects have substantive implications for international relations and politics (Barnett, et al, 2017).…”