Building Images of "President Trump": Comparing Co-evolutions of the Trade War Discourse between Influencers and Regular Users on Twitter

Using semantic network analysis, this paper examines how three types of Twitter influencers and the regular Twitter users frame “President Trump” in the discourses of Trade War. In addition to revealing the central words and word clusters adopted by the different categories of Twitter users, this paper also studies how central words co-evolved over time between three types of Twitter influencers and regular users by using coherency and time lag analysis. Overall, we discovered that three types of Twitter influencers all took a negative stance on illustrating the President Trump’s image, while the regular Twitter users demonstrated polarized attitudes toward it. The significant time lags between the highly co-evolved word pairs indicated complicated interactions between Twitter influencers and regular Twitter users. 1. Literature Review 1.1. Political Discussions and Polarization on Social Media Social media are gaining substantial presence in today’s political landscape [1]. They are often described as online public spheres for political discourses [2]. Despite the early euphoria about social media’s potential for bringing global democratization [3] and allowing cross-cutting interactions to promote the exchange of different political views [4], researchers are increasingly concerned about social media’s role in facilitating polarization [5]. Specifically, the echo-chamber effects with more polarized views have been found in the studies of political discourses on Twitter [6, 7]. With the rise of social media, there emerges a new type of celebrity – social media influencers, the individuals who influence others in online social networks [8]. Research has found that polarization could allow for influencers to maintain their social dominance and become even more influential on social media [9]. Social media influencers have different types. Understanding how different types of influencers interact with the regular users thus becomes a critical task for social media researchers. 1.2. Types of Social Media Influencers Social media influencers are usually categorized by audience size, although the number for each tier could vary by platform. A widely adopted classification divides social media influencers into the three categories of mega, macro, and micro influencers [10, 11, 12]. Mega influencers are the highest ranked category with more than a million followers [11]. But their engagement with followers is rather low [10]. Their followers are often highly diverse with different topic interests [12]. Macro influencers are in the tier with 100K to up to a million followers [10]. Compared to mega influencers, they are easier to connect with. Micro influencers are classified to have a follower base ranging from 10,000 to no more than 100K [13]. Despite the relatively smaller number of followers, micro influencers are considered to have a stronger relationship with and be more targeted to their followers than macro and mega influencers for being more accessible and authentic [8]. Different from much existing research focusing on social media influencers’ persuasive impacts on behavioral outcomes [7] [8] [9] [10], we decide to adopt a semantic approach to understand if and how influencers’ chosen words in political discourse are related to the regular users’ on social media. By selecting the U.S.-China Trade War as a case study, we examine how different types of social media influencers and regular users discuss President Trump respectively and how their discourses coevolve over time. Proceedings of the 54th Hawaii International Conference on System Sciences | 2021 Page 2956 URI: https://hdl.handle. net/10125/70974 978-0-9981331-4-0 (CC BY-NC-ND 4.0) 1.3. The U.S.-China Trade War The trade tensions between the U.S. and China scaled up when President Trump signed an executive memorandum launching a Section 301 investigation into China’s intellectual property practices and threatened extra tariffs on imports from China in March 2018 [14]. After that, the two countries went through several rounds of tariff escalations. On January 15, 2020, they officially signed a phase one trade deal [15]. This trade war between the U.S. and China is alleged to be the largest of its kind in the global market in the past 50 years [16]. By choosing the Trade War as the study context, we first identify the different types of influencers by follower size in the relevant discourses on Twitter. Focusing on the tweets mentioning President Trump, we then study the influencers’ and the regular users’ tweets from a semantic perspective and how their Trade War discourses co-evolved over time. 2. Methods and Research Questions 2.1. Semantic Network Analysis of Social Media Discourses Semantic network analysis (SMA) is a form of content analysis identifying the network of associations between words expressed in a text [17]. Rooted in the cognitive paradigm [18] and the tradition of frame semantics in linguistics [19], scholars have argued that words are hierarchically clustered in memory [20]. Thus, spatial models that illustrate the relations among words are representative of meaning [21]. Through examining the visibility and co-occurrence of vocabularies in texts, salience of words can be identified, and the related framing strategies could be explained. Most recently, SMA has been used for analyzing social media discourses on health care [22], nature disasters [23], political campaign [24], and social movements [25]. These studies mainly used centrality analysis and cluster analysis to understand the framing strategies used in social media discourses. Along with these studies, our research uses semantic network analysis to examine how different Twitter influencers and regular Twitter users built the images of “President Trump” in the discourses of Trade War. Four semantic networks were created based on the analysis of word co-occurrence. They are the semantic networks of mega influencers’ tweets (MEGA), macro influencers’ tweets (MACRO), micro influencers’ tweets (MICRO), and regular users’ tweets (REGU). The related research questions are: R1: What are the most central words in MEGA, MACRO, MICRO, and REGU respectively? R2: What are the largest word clusters in MEGA, MACRO, MICRO, and REGU respectively? R3: What are the most central words in the largest word clusters of MEGA, MACRO, MICRO, and REGU respectively? 2.2. Using Coherencies to Examine the Coevolutions of Social Media Discourses Coherency is a measure of association between two time series. It reflects how well correlated two processes are. Specifically, using spectral analysis or frequency domain analysis, coherence squared, defined as analogous to the squared correlations coefficient [26], can be calculated to demonstrate the degree to which individual nodes’ changes are related, and how they co-evolve. The slope of the phase spectrum can also be examined to ascertain the time lag to determine potential direction of causality between highly co-evolved pair of nodes. This approach has been used to examine co-evolutions of international networks and news frames [27]. This research uses coherency and time lag analysis to examine how the frames adopted by regular Twitter users co-evolved with the frames adopted by mega, macro and micro influencers. Since the coherency analysis produces a matrix of relations, a network analysis can thus be conducted. Three coherency networks were created based on the analysis of how the frequency of the most central words in REGU co-evolved with the frequency of the most central words in MEGA, MACRO, and MICRO. They are the coherency networks of mega influencers’ and regular Twitter users’ tweets (MEGA-REGU), macro influencers’ and regular Twitter users’ tweets (MACRO-REGU), and micro influencers’ and regular Twitter users’ tweets (MICRO-REGU). This research raises the following questions to identify the co-evolutions of the tweets on President Trump in the discourses of Trade War: R4: How many clusters are there in MEGA-REGU, MACRO-REGU, and MICRO-REGU respectively? R5: Which set of words are highly co-evolved in each cluster of MEGA-REGU, MACRO-REGU, and MICRO-REGU?