4.2.2. Thematic Analysis
For the analysis of topic evolution across time, a set of time slices is made. According to the Annual Scientific Production, we take three periods to segment the whole Twitter-related scientific development process into three phases: Initial period is from 2006 to 2012: in this period, the publication number is not so much as later years, but RGR is relatively high, DT kept steadily with mild changes. The developing period is from 2013 to 2016; in this period the number of publications increased rapidly, RGR slowed down while DT started to slightly grow. The advanced period is from 2017 to 2020; in this period the number of publications arrived peak, while RGR kept turning down, DT grew immensely.
presents the thematic maps of the three periods, each of the circles represents a cluster and the size of the circle represents the size of the cluster (the number of included terms/keywords). There are fewer clusters in developing and advanced period than the initial period, which implies that there are fewer research topics in last years than the first years.
For the initial period (2006–2012), there are two clusters on the first quadrant with high centrality and density, “marketing, online, google” and “social-web, wikipedia”, these clusters focused on Twitter and other well-known website and marketing, are the motor research themes of this period. The third quadrant mainly consists of three clusters, “innovation”, “crowd-sourcing” and “advertising”, all these three clusters can be considered as specific research topics for business subject, they are the highly developed and isolated themes of 2006–2012. While Twitter was a newly emerged social media in that time, business related topics revealed a high centrality in the initial period, they have been hugely developed in the first years since the foundation of Twitter.
“Democracy, arab-spring” and “design, event-detection, mobile” are the emerging or declining themes, they are independent from each other, “democracy, arab-spring” corresponds to 2010 arab-spring revolution, “design, event-detection, mobile” might related to the studies about smartphone and mobile application, such new electronic device and software also appeared after 2010, there are publications such as “Tweeting with the telly on! Mobile phones as second screen for TV”, “Mobile apps: innovative technology for globalization and inclusion of developing countries” can prove our assumption. It is more reasonable to classify these two clusters as emerging themes, compared to the foundation of Twitter (2006), from 2006 to 2012, such political events and technological innovation occurred in 2010 was even newer.
“Social-networking-site, linkedin, student”, “social-media, microblogging, microblog”, “social-network, web, facebook” are the three clusters that belong to basic and transversal themes; they are mainly focused on other virtual social networks, comparative studies about Twitter and other similar platforms are another important research line in the initial period. However, based on the previous argument, the “social-networking-site, linkedin, student” cluster may also refer to the studies of human resources, online employment and education, there are publications like “Using facebook, linkedin and Twitter for your career”, “Friend or foe? The promise and pitfalls of using social networking sites for HR decisions”, “Comparative survey of students’ behavior on social networks (in Czech perspective)” can prove our assumption.
For the developing period (2013–2016), in general, topics related to business, mobile and arab-spring disappeared from the map, contrarily, computer science related nouns emerged in this period (e.g., algorithm, sentiment-analysis). Cross-platform comparative studies (“social-media, facebook, internet” cluster) moved from basic and transversal themes to motor themes. “Algorithm, credibility, emotion” cluster locates between the first and second quadrant with a very high density, this cluster refers to using computational methods to detect online emotion, and is highly developed within this period. “Microblogging, privacy, altmetric” cluster locates between the third and fourth quadrant, as big data is gaining attention and popularity among researchers in this period, the usage of big data starts to be important, which have also caused people’s awareness about privacy. This cluster may contain two research lines, using Twitter metrics as a tool to measure research impact [48
], and the privacy caution of using microblog service [50
Disaster-management, crisis-management, natural-disaster” cluster is the emerging and declining theme of the developing period, apparently, this cluster refers to studies about crisis management and crisis communication during severe disasters, for example, earthquakes [51
], tsunami [52
], and epidemic crisis [53
] etc. The last cluster of this period is “social-network, sentiment-analysis, big-data”—this cluster belongs to basic and transversal theme, data-driven sentiment analysis becomes a popular research method for social media studies in this period.
For the advanced period (2017–2020), there is no absolute motor theme, “social-media, facebook, political-communication” locates between the first and the second quadrant with a high centrality, this cluster refers to the study of political communication with social media. Two clusters are on the second quadrant, “security, behavior, iot (internet of things)” and “altmetric, citation, bibliometric”; they are highly developed and isolated research themes, and independent from each other. Alongside the rapid development of social network sites, the integration of social media and internet of things has formed a new concept, social internet of things (siot) [54
], meanwhile, social network-based recommendation system emerges as a new research topic, for example, researchers used Twitter data to personalize movie recommendation system [55
], but such advanced technologies also contain considerable security risk. We believe the cluster “security, behavior, iot” refers to use Twitter as an iot medium to study user’s online behavior and the potential cybersecurity concerns of siot. The cluster “altmetric, citation, bibliometric” is easier to interpret—it refers to Twitter-based scientometric studies, compared to the “altmetric” cluster in developing period, the study of scientometrics during 2017 to 2020 becomes an independent and developed research theme.
“Sentiment-analysis, machine-learning, big-data” was the only basic and transversal research theme, this implies computational methods and techniques are widely used in Twitter research from 2017 to 2020. The cluster “social-network, information-diffusion, microblogging” locates between the third and the fourth quadrant, with a low density, this means that although the study of information diffusion on Twitter and microblogs emerged in recent years, yet not fully developed.
presents the alluvial diagram of research thematic evolution across the three previously segmented periods; it provides us a global view of the changes. Each of the nodes represents a cluster, and is labeled by the first three words of the clusters, the edges are their temporal evolution track, generated by keyword co-occurrence of the topics between two time slices [33
Overall, research topics in the initial period were more than in later periods; business-related research lines took an important place in that time. There are two major research topics in the developing period, “social-network” (social-network, sentiment-analysis, big-data) and “social-media” (social-media, facebook, internet). As we have discussed, they imply different research lines, the former represents Twitter study with computational methods, the latter represents cross-platform comparative studies. Most of the research themes of the initial period were lumped together under these two large topics. Furthermore, “disaster-management” (“disaster-management, crisis-management, natural-disaster”) emerged in the developing period, and it evolved to be an important component for the clusters with information diffusion (“social-network, information-diffusion, microblogging”) and big data (“sentiment-analysis, machine-learning, big-data”) in the advanced period. Scientometric study (“altmetric, citation, bibliometric”) was an important research topic in recent years—naturally, it is strongly associated with clusters containing altmetric (microblogging, privacy, altmetric) and big data (social-network, sentiment-analysis, big-data). Such clusters were also evolution sources for the cluster “security, behavior, iot”.