Studying the Community of Trump Supporters on Twitter during the 2020 US Presidential Election via Hashtags #maga and #trump2020

(1) The study investigated the social network surrounding the hashtags #maga (Make America Great Again, the campaign slogan popularized by Donald Trump during his 2016 and 2020 presidential campaigns) and #trump2020 on Twitter to better understand Donald Trump, his community of supporters, and their political discourse and activities in the political context of the 2020 US presidential election. (2) Social network analysis of a sample of 220,336 tweets from 96,820 unique users, posted between 27 October and 2 November 2020 (i.e., one week before the general election day) was conducted. (3) The most active and influential users within the #maga and #trump2020 network, the likelihood of those users being spamming bots, and their tweets’ content were revealed. (4) The study then discussed the hierarchy of Donald Trump and the problematic nature of spamming bot detection, while also providing suggestions for future research.


Introduction
While the participation of social media in political discourse is not a new phenomenon, their influence in recent presidential elections has been unprecedented, exceeded previous limits, and indeed dwarfed the regular dominance of legacy media on public opinion. Social media, particularly Twitter, was considered the most critical communication channel for both Donald Trump and Hillary Clinton throughout their 2016 presidential campaigns: on a daily average between October 2015 and November 2016, the two primary presidential candidates tweeted 13.25 and 21.56 times, respectively (Buccoliero et al. 2020). The Democratic candidate spent most of her tweets discussing political issues and attacking Trump. Her opponent, on the other hand, blasted not only Clinton but also anyone who dared to publicly criticize him; he insisted that he was the only candidate who could make America great again, defeat terrorism, contrast illegal immigration, and self-fund his campaign (Lee and Quealy 2019).
Then came 2020, the year in which Donald Trump orchestrated, during the presidential election, what was described by the media as "a media circus" of conspiracy theories designed to distract, exact revenge, and entertain (Autry 2020;Pompeo 2020;Rich 2020;Trudo 2020). He repeatedly spread fake news, misinformation, and disinformation to smear the integrity of mail-in ballots, baselessly accuse the election to be rigged, and claim that he was the rightful winner (Egan 2020;Freking 2020;Riccardi 2020). After political fanatics attacked the Capitol on 6 January 2021, Donald Trump was accused of inciting the insurrection and banned from numerous social platforms (Colarossi 2021;Denham 2021;Eisen and Reisner 2021;Savage 2021;Twitter Inc. 2021).
The utilization of social media, particularly Twitter, in politics in the twenty-first century is often compared to that of television, a then-new mass media, in the 1960s. In 1961, John F. Kennedy was the first presidential candidate to successfully secure the 1.
RQ-Who were the most active and influential users among the social network of Trump supporters on Twitter during the 2020 US presidential election? 2.
RQ-What is the likeliness of those users being spamming bots? 3.
RQ-What were the most associated hashtags and the most retweeted tweets among the particular social network?

Data Collection and Objectives
The study used the dataset provided publicly by Chen et al. (2020). Text files containing dehydrated tweet ids posted between 27 October and 2 November were collected via Subversion, concatenated, and rehydrated with Hydrator (Documenting the Now 2020). From the original set of 34,583,668 tweet ids, only 19,746,355 ids were rehydrated into tweet data (43% loss rate) since many tweets and Twitter accounts had been suspended or deleted either by Twitter or the users. Then, the data was filtered; hence, a corpus of 220,336 tweets from 96,820 unique users containing either the keywords #maga (Make America Great Again, the campaign slogan popularized by Donald Trump during his 2016 and 2020 presidential campaigns) or #trump2020, posted between 27 October and 2 November 2020, was generated. The timeframe between 27 October and 2 November was chosen because it was exactly one week before the general election day (November 3); a drastic surge in the number of tweets posted by political candidates, affiliations, and their supporters is generally expected during the period of time (Kruikemeier 2014). Meanwhile, the hashtags #maga and #trump2020 were often affiliated with Donald Trump and his community of supporters during the 2020 US presidential election.
Network analysis of the corpus revealed users who were the most active (i.e., who posted the most tweets) or most influential (i.e., who were most frequently mentioned or retweeted) within the network. Additionally, the study also examined the relationship between those groups of users. Details, such as those handles' likeliness of being spamming bots, were provided. The most popular topics among the corpus were identified via analysis of the most used hashtags and the most retweeted tweets. Social network graphs were generated and analyzed to examine the relationship between users of the community and the contents they tweeted, i.e., who said what. Such analysis is relevant in understanding Twitter users related to the hashtags, their affiliations, and the nature of such accounts .

Twitter Capture and Analysis Toolset (TCAT)
The Twitter Capture and Analysis Toolset (TCAT) is a set of tools which allow users to retrieve and collect publicly available tweets from Twitter and analyze them in various ways. Apart from methodological transparency, the software provides robust and reproducible data capture and analysis while also interlinking with other existing analytical software. Borra and Rieder (2014) argued that it was not only a solution to a set of problems but also an attempt "to connect the question of toolmaking for social and cultural research to debates regarding the 'politics of method' in ways that are not merely theoretical or critical" (pp. 274-75). For some portions of the study, the author utilized 4CAT, a variation of TCAT designed to capture and analyze the contents of various thread-based platforms. The software suite is created and run by OILab at the University of Amsterdam as part of the ERC-funded ODYCCEUS project (Peeters and Hagen 2018).

Gephi
Gephi is an open-source application for interactive graph analysis, network analysis, and visualization. It is among the most utilized applications for the exploration and analysis of network data in which users investigate relationships between groups of people, institutions, events, and other connected phenomena (Cherven 2015;Khokhar 2015). It provides easy and broad access to, while also allowing for spatializing, filtering, navigating, manipulating, and clustering of, network data. Thus, by employing TCAT and Gephi together, millions of units of social media data on Twitter can be pre-processed to be effectively used and sorted by algorithms to find users, contents, patterns, or items of importance (Bastian et al. 2009;Groshek et al. 2020).

Botometer
Botometer (at https://botometer.iuni.iu.edu, last accessed on 16 November 2021) is a bot-evaluation API developed by a team from Indiana University. Its algorithm leverages over one thousand features of a respective Twitter handle to evaluate the likeliness of the handle being a social bot and awards the handle with a score from 0 to 5, with 0 for being human-like and 5 for performing like a bot (Davis et al. 2016;Al-Rawi et al. 2019). Initially named BotOrNot, Botometer is a publicly available service aiming to lower the entry barrier for social media researchers, reporters, and enthusiasts as bot detection has become an integral part of the social media experience for users. Over 80% of Botometer users believe the bot-evaluation service is accurate, and over 80% of the users find scores and descriptions presented by Botometer easy to understand (Yang et al. 2019).
Although it is tempting to set an arbitrary threshold score (i.e., an average bot score), then consider everything above that number a bot and everything below a human, binary classification of accounts using two classes may be problematic. It should be more informative to look at the distribution of scores over a sample of accounts (Yang et al. 2019(Yang et al. , 2020.

Data Analysis and Results
Between 27 October and 2 November 2020, an average of 31,476.43 tweets containing the hashtags #maga or #trump2020 (M = 31,019, SD = 7196.03) were posted daily. November 2 saw the highest number of tweets posted (44,418) while October 28 saw the lowest (24,723). Over half (13,4906, or 61.2%) of the tweets were original tweets (i.e., not retweeted). Meanwhile, an average of 20,808.71 unique users (M = 18,105, SD = 4566.93) posted on Twitter with the hashtags every day. November 2 had the highest number of unique Twitter users (29,459) while October 28 had the lowest (17,403) ( Figure 1). The numbers of tweets, as well as unique users participating in the #maga and #trump2020 network on Twitter, indeed increased greatly and gradually between 27 October and 2 November 2020, supporting the argument that a drastic surge in the number of tweets posted by political candidates, affiliations, and their supporters is generally expected before election days (Kruikemeier 2014 The approach to investigating RQ1 and RQ2, due to noise and irrelevant content on social media, was to select top lists following previous studies that examined large datasets

The Most Active Users
Regarding the most active users among the #maga and #trump2020 network on Twitter during the 2020 US presidential election, it can be seen that @Drizzle_500 ranked first in the most-active chart ( Table 1). The account, appeared to be a Chinese account supporting Donald Trump, posted 550 tweets during the seven days between 27 October and 2 November 2020. @Drizzle_500 was followed by @cogitarus (453 tweets), @ReimTopher (341 tweets), @HassanYadollahi (341 tweets), and @Beorn1234 (268 tweets). Together, the top 100 most active users posted a total of 12,858 tweets (M = 128.58, Median = 108.5, SD = 70.41), making up 5.8% of the whole corpus.
While the majority of the most active users, either humans or bots, were supportive of Donald Trump and Republican ideologies, several of them used the hashtags #maga or #trump2020 to do the opposite (i.e., voice their opinions against Donald Trump and Republican ideologies) such as @Earl18E (#17-), author Gerald Weaver (@Gerald_Weaver_, Figure 1. Distribution of tweets and unique users mentioning #maga or #trump2020 between 27 October and 2 November 2020. The blue dots and line represent the daily number and the trend of tweets, while the red dots and line represent the daily number and the trend of unique users.

The Most Active/Influential Users and Their Likeliness of Being Spamming Bots: Results for RQ1 and RQ2
The approach to investigating RQ1 and RQ2, due to noise and irrelevant content on social media, was to select top lists following previous studies that examined large datasets (Al-Rawi 2017Al-Rawi et al. 2019;Wilkinson and Thelwall 2012).

The Most Active Users
Regarding the most active users among the #maga and #trump2020 network on Twitter during the 2020 US presidential election, it can be seen that @Drizzle_500 ranked first in the most-active chart ( Table 1). The account, appeared to be a Chinese account supporting Donald Trump, posted 550 tweets during the seven days between 27 October and 2 November 2020. @Drizzle_500 was followed by @cogitarus (453 tweets), @ReimTopher (341 tweets), @HassanYadollahi (341 tweets), and @Beorn1234 (268 tweets). Together, the top 100 most active users posted a total of 12,858 tweets (M = 128.58, Median = 108.5, SD = 70.41), making up 5.8% of the whole corpus.
While the majority of the most active users, either humans or bots, were supportive of Donald Trump and Republican ideologies, several of them used the hashtags #maga or #trump2020 to do the opposite (i.e., voice their opinions against Donald Trump and Republican ideologies) such as @Earl18E (#17-), author Gerald Weaver (@Gerald_Weaver_, #90), and Dr. Scott McLeod (@mcleod, #95-). Noted that since none of these most active accounts is verified, their identifications cannot be confirmed.
Except for @christo31129690, who was not awarded a bot score since the account was deleted, the other 99 most active users in the corpus received an average bot score of 2.14 (M = 1.5, SD = 1.41); 41 users received an above-average bot score, 34 users received a bot score of 3 or above, and 15 users received a bot score of 4 or above. If the deleted @christo31129690 was taken into consideration, 19 (or 19%) of the most active users in the corpus were potentially bots (i.e., received a bot score from 3 to 3.9), while 16 (or 16%) of them were highly likely to be bots (i.e., received a bot score of 4 or above).  Except for @christo31129690, who was not awarded a bot score since the account was deleted, the other 99 most active users in the corpus received an average bot score of 2.14 (M = 1.5, SD = 1.41); 41 users received an above-average bot score, 34 users received a bot score of 3 or above, and 15 users received a bot score of 4 or above. If the deleted @christo31129690 was taken into consideration, 19 (or 19%) of the most active users in the corpus were potentially bots (i.e., received a bot score from 3 to 3.9), while 16 (or 16%) of them were highly likely to be bots (i.e., received a bot score of 4 or above).

The Most Influential Users
To determine the most influential users, top lists of the most mentioned users (Table 2) and the users whose tweets were most retweeted by other users in the corpus (Table 3) were generated. Donald Trump (@realDonaldTrump), the incumbent president and Republican nominee for the 2020 US presidential election, ranked first on the most mentioned chart (Table 2) with 9301 times mentioned, followed by his opponent Joe Biden (@JoeBiden, 3678 mentions), Biden's vice-president nominee Kamala Harris (@KamalaHarris, 1050 mentions), actor and producer James Woods (@RealJamesWoods, 668 mentions) who is a staunch Trump supporter, and singer and actress Lady Gaga (@ladygaga, 425 mentions) who has publicly opposed the presidency of Donald Trump. Most (40, or 80%) of the accounts in the top 50 most mentioned are verified and can be categorized into groups of Republican politicians (e.g., Donald Trump (@realDonaldTrump, #2)), Democratic politicians (e.g., Joe Biden (@JoeBiden, #2)), the media (e.g., CNN (@CNN, #14-)), or celebrities (e.g., James Woods (@RealJamesWoods, #4)).  Among the group of others, the Lincoln Project (@ProjectLincoln, #39) is an American movement who describes themselves as "dedicated Americans protecting democracy." They are a committee formed in late 2019 by Republicans that committed to fighting against Trumpism, first by defeating Donald Trump at the ballot box in the 2020 presidential election (Conway et al. 2019; The Lincoln Project 2021). There were more Republican politicians than Democratic politicians (14 to 9), but fewer conservative media outlets and personalities than liberal media outlets and personalities (6 to 7).
The former White House Deputy Chief of Staff for Communications Dan Scavino (@DanScavino), was retweeted 18,013 times, led the top retweeted chart (Table 3). He was retweeted about 4.5 times more than his runner-up, the conservative media outlet Right Side Broadcasting Network (@RSBNetwork, 4006 times). They were followed by the incumbent vice-president and Republican vice-president nominee for the 2020 US presidential election Mike Pence (@Mike_Pence, 3195 times), Donald Trump's daughter and senior advisor Ivanka Trump (@IvankaTrump, 2747 times), and conservative media personality Lou Dobbs (@LouDobbs, 1557 times). The 100 most retweeted users constituted 47,962 retweets (or 21.77%) of the corpus. Furthermore, all verified accounts of the top 15 most retweeted users, constituting 33,191 retweets (or 15.06%) of the corpus, were Republican politicians, conservative media outlets and personalities, or individuals who had personal ties with Donald Trump. Thus, a massive amount of their messages, ideas, comments, and discussions, which were often supportive of the 45th president, were disseminated to the #maga and #trump2020 community on Twitter during that period.
The top 50 most retweeted users in the corpus received an average bot score of 1.52 (M = 1.05, SD = 1.22); 20 users received an above-average bot score, and nine users received a bot score of 3 or above. Only one user, @honnnnie2 (#36-, 4.8), received a bot score of 4 or above. The handle, self-described as "um Journal. Media 2021, 2, FOR PEER REVIEW 10 ties with Donald Trump. Thus, a massive amount of their messages, ideas, comments, and discussions, which were often supportive of the 45th president, were disseminated to the #maga and #trump2020 community on Twitter during that period. The top 50 most retweeted users in the corpus received an average bot score of 1.52 (M = 1.05, SD = 1.22); 20 users received an above-average bot score, and nine users received a bot score of 3 or above. Only one user, @honnnnie2 (#36-, 4.8), received a bot score of 4 or above. The handle, self-described as " ✝️ Texas Conservative Wife & Mother! Oil and Gas Family! #MAGA SOUTHERN BIRACIAL TRUTH SPEAKER!!", often used hashtags to support Donald Trump and Republican ideologies (e.g., #TrumpPence2020 #TRUMP2020ToSaveAmerica, #Trump2020LandslideVictory, #AmericaFirst, or #Vot-eRedToSaveAmerica) or attack Joe Biden (e.g., #BidenCrimeFamily or #Hunterbidenlaptop). All other users evaluated as having high bot-like performance (i.e., received a bot score from 3 to 3.9) were not verified. The verified accounts to unverified accounts ratio among the most retweeted users (22 to 28) was more balanced than that of the most mentioned users (40 to 5). The 28 unverified most retweeted users received an average bot score of 2.03 (M = 1.65, SD = 1.34). Including the five unverified most mentioned users, the 33 unverified most influential users received an average bot score of 1.92 (M = 1.6, SD = 1.35); 13 users received an aboveaverage bot score, 10 users received a bot score of 3 or above, and @honnnnie2 (4.8) was the only user receiving a bot score of at least 4. @Beorn1234 was, notably, the only unverified account appearing in all three charts (#5 most active, #41 most mentioned, and #17 most retweeted). The account received a bot score of 3.2 (i.e., highly likely to be a bot), and was predominantly associated with hashtags supporting Donald Trump (e.g., #TRUMP2020ToSaveAmerica), promoting conspiracy theories (e.g., #StopTheSteal, #WWG1WGA, or #QAnon), and popularizing Restart, a fringe dissident community of cl Texas Conservative Wife & Mother! Oil and Gas Family! #MAGA SOUTHERN BIRACIAL TRUTH SPEAKER!!", often used hashtags to support Donald Trump and Republican ideologies (e.g., #TrumpPence2020 #TRUMP2020ToSaveAmerica, #Trump2020LandslideVictory, #AmericaFirst, or #VoteRed-ToSaveAmerica) or attack Joe Biden (e.g., #BidenCrimeFamily or #Hunterbidenlaptop). All other users evaluated as having high bot-like performance (i.e., received a bot score from 3 to 3.9) were not verified.
The verified accounts to unverified accounts ratio among the most retweeted users (22 to 28) was more balanced than that of the most mentioned users (40 to 5). The 28 unverified most retweeted users received an average bot score of 2.03 (M = 1.65, SD = 1.34). Including the five unverified most mentioned users, the 33 unverified most influential users received an average bot score of 1.92 (M = 1.6, SD = 1.35); 13 users received an above-average bot score, 10 users received a bot score of 3 or above, and @honnnnie2 (4.8) was the only user receiving a bot score of at least 4. @Beorn1234 was, notably, the only unverified account appearing in all three charts (#5 most active, #41 most mentioned, and #17 most retweeted). The account received a bot score of 3.2 (i.e., highly likely to be a bot), and was predominantly associated with hashtags supporting Donald Trump (e.g., #TRUMP2020ToSaveAmerica), promoting conspiracy theories (e.g., #StopTheSteal, #WWG1WGA, or #QAnon), and popularizing Restart, a fringe dissident community of Iranian opposition and conspiracy groups similar to QAnon (e.g., #MIGA, #RestartMIGA, or #restartleader) (Tabatabai 2020).
To analyze the influential users and communities of users beyond simple frequency analysis of user mentions, a social network graph by mentions ( Figure 2) was generated based on interactions between users. If a user (i.e., node) mentioned another user, a directed link (i.e., edge) would be created between them. The more frequently two users mentioned each other, the stronger their directed link would be. The graph consists of 1164 nodes, representing the most influential unique users, and 4406 directed edges, representing mentions.
To analyze the influential users and communities of users beyond simple frequency analysis of user mentions, a social network graph by mentions ( Figure 2) was generated based on interactions between users. If a user (i.e., node) mentioned another user, a directed link (i.e., edge) would be created between them. The more frequently two users mentioned each other, the stronger their directed link would be. The graph consists of 1164 nodes, representing the most influential unique users, and 4406 directed edges, representing mentions.
The two most significant clusters of users (i.e., communities) within the #maga and #trump2020 network were those who were related to @realDonaldTrump (i.e., the orange cluster), and those who were related to @JoeBiden and @KamalaHarris (i.e., the blue cluster). @realDonaldTrump was the most influential node in the network, receiving an eigenvector centrality score of 1 (i.e., the node was connected to many nodes who themselves had high scores) (Negre et al. 2018). @Drizzle_500, who topped the most-active chart with 550 tweets, mentioned 76 different users in 208 tweets. @Beorn1234, another unverified user of concern who appeared in all three top charts (#5 most active, #41 most mentioned, and #17 most retweeted) and received a bot score of 3.2 (i.e., highly likely to be a bot), mentioned 9 different users 142 times and was mentioned by three different users 114 times. The account, curiously, mentioned itself 110 times; thus, while its connection was somewhat limited, the sheer number of self-mentions boosted its visibility within the network.
While there was not a distinct and apparent pattern of connection, the social network graph by mentions helped identify communities of influential users and their location within the network. Additionally, it revealed the activity patterns of certain users of concern, hence providing more evidence to determine the likeliness of those users being spamming bots and how they became visible in the network.  A list of the 50 most frequently used hashtags (case insensitive) among the community was generated (Table 4) to answer RQ3. Apart from the two hashtags used to query The two most significant clusters of users (i.e., communities) within the #maga and #trump2020 network were those who were related to @realDonaldTrump (i.e., the orange cluster), and those who were related to @JoeBiden and @KamalaHarris (i.e., the blue cluster). @realDonaldTrump was the most influential node in the network, receiving an eigenvector centrality score of 1 (i.e., the node was connected to many nodes who themselves had high scores) (Negre et al. 2018). @Drizzle_500, who topped the most-active chart with 550 tweets, mentioned 76 different users in 208 tweets. @Beorn1234, another unverified user of concern who appeared in all three top charts (#5 most active, #41 most mentioned, and #17 most retweeted) and received a bot score of 3.2 (i.e., highly likely to be a bot), mentioned 9 different users 142 times and was mentioned by three different users 114 times. The account, curiously, mentioned itself 110 times; thus, while its connection was somewhat limited, the sheer number of self-mentions boosted its visibility within the network.
While there was not a distinct and apparent pattern of connection, the social network graph by mentions helped identify communities of influential users and their location within the network. Additionally, it revealed the activity patterns of certain users of concern, hence providing more evidence to determine the likeliness of those users being spamming bots and how they became visible in the network.
Donald Trump received support from several social and political movements on Twitter as well, including #miga (#10, 7467 times) which is related to the dissident Restart community in Iran, #blexit (#30, 1418 times) which convinces African American voters to stop supporting the Democratic party, #walkaway (#31, 1405 times) which encourages liberals to flee from the Democratic party, and #latinosfortrump (#36, 1281 times) which is a coalition of Latino supporters of Donald Trump.
Joe Biden, Donald Trump's rival, was also a popular target of discussion among the #maga and #trump2020 community during the 2020 US presidential election. #biden (#18) was employed 2832 times, followed by #bidenharris2020 (#19, 2780 times) and #joebiden (#22-, 1993 times). The Democratic presidential nominee and his family were primarily accused of corruption (e.g., #bidencrimefamiily, #33, 1394 times; #bidencorruption, #40, 1121 times; #bidencrimefamily, #41, 1113 times); ironically, #bidencrimefamiily, which had a typo in itself, appeared more frequently in the corpus than #bidencrimefamily. Dreyfuss (2020), however, argued that this typo was, among others, an intentional tactic by Donald Trump to rally supporters around a conspiracy theory, neuter the attempts of social media companies to stop its spread, and further sow doubt about the integrity of the election. Since the beginning of his campaign for the presidential office in 2016, Donald Trump had repeatedly used the motto #draintheswamp (#39, 1143 times) to demonstrate his pledge to disrupt the political culture of Washington and warn of the power of lobbyists and political donors to buy off elected officials. The pledge was, in fact, never fulfilled (Dawsey et al. 2020). The term "drain the swamp" was first used in 1903 by Social Democratic Party organizer Winfield R. Gaylord to metaphorically describe how socialists wish to deal with big business (Know Your Meme 2017; Polpik 2010).
#covid19 (#24, 1878 times) was another topic of discussion. As of 1 November 2020, about 9.3 million COVID-cases and 230 thousand COVID-deaths had been reported in the US, while 46 million COVID-cases and 1.2 million COVID-deaths had been reported globally (Centers for Disease Control and Prevention-CDC 2020; World Health Organization-WHO 2020). Although Donald Trump and his supporters gave him "a 10 out of 10" on his efforts against COVID-19, experts generally criticized the Trump administration's response to the coronavirus disease, arguing that their strategy was "lack of candor," "lack of science," and "very likely did cost lives" (Chalfant 2020;Howard and Kelly 2021;Stracqualursi 2021).
#michigan (#43, 1073 times) and #pennsylvania (#44, 1070 times) also appeared in the most used hashtags chart since, perhaps, Donald Trump was then repeatedly attacking Michigan's Democratic Governor Gretchen Whitmer for her coronavirus response, accusing her of being dishonest (Mason and Martina 2020;Schulte and Eggert 2020). In Pennsylvania, his campaign filed lawsuits attempting to challenge the state's poll-watching law and limit mail-in ballots (Levy 2020;Sherman 2020). Michigan and Pennsylvania were considered crucial swing states during the 2020 US presidential election; Joe Biden won in both states.
A network graph by hashtag co-occurrences ( Figure 3) was generated to further investigate the association between hashtags within the network. If two hashtags (i.e., nodes) appeared in the same tweet, a link (i.e., edge) would be created between them. The more often hashtags appeared together, the stronger their link would be. The graph consists of 2628 nodes, representing hashtags, and 104,492 undirected edges, representing hashtag co-occurrences. The spatialization layout of choice was radial axis which groups nodes and draws the groups in axes (or spars); thus, it helps study homophily by showing distributions of nodes inside groups with their links. #biden and #bidenharris2020 were related to the #trump2020 cluster while #joebiden belonged to neither of the two major clusters. #covid19 was categorized into the #maga cluster. There were several hashtags paired with #covid19 to express displeasure towards the Trump administration's response to the coronavirus, such as #trumpvirus (262 times), #trumpliespeopledie (32 times), and #trumphasnoplan (30 times).
#biden and #bidenharris2020 were related to the #trump2020 cluster while #joebiden belonged to neither of the two major clusters. #covid19 was categorized into the #maga cluster. There were several hashtags paired with #covid19 to express displeasure towards the Trump administration's response to the coronavirus, such as #trumpvirus (262 times), #trumpliespeopledie (32 times), and #trumphasnoplan (30 times).
A bipartite social network graph by hashtag-user co-occurrences (Figure 4) was generated to further investigate the association between hashtags and users within the network. If a user (i.e., a user node) posted a tweet with a certain hashtag (i.e., a hashtag node), a link (i.e., edge) would be created between that user and the hashtag. The more frequently a user employed a hashtag, the stronger their link would be. The bipartite social network graph visualized 5212 nodes, representing 3672 users and 1540 hashtags, and 48,396 undirected edges, representing user-hashtag co-occurrences. #trump2020 was employed by 2773 different Twitter users with @Drizzle_500, our most active user among the corpus, using it 551 times. The account employed a total of 41 hashtags with #yourchoice, #election, #votered, #vote2020, and #4moreyears being some of their favorites, being used 529, 528, 506, 502, and 502 times, respectively. Meanwhile, @cogitarus, the runner-up in the most active chart, used 16 different hashtags in his tweets with #americafirst (452 times), #blexit, #votered, #maga, #kag, and #patriotismwins (451 times each) being their most frequently used hashtags. Another high-degree hashtag, #maga, was employed by 2523 different users, including some eminent ones such as @dreamchqser (259 times, #29-most active), @Beorn1234 (249 times, #5 most active, #41 most mentioned, and #17 most retweeted), and @Tony_Eriksen (199 times, #8 most active). In comparison, #trump2020 was employed more frequently, by more users among the network than #maga. belonged to neither of the two major clusters. #covid19 was categorized into the #maga cluster. There were several hashtags paired with #covid19 to express displeasure towards the Trump administration's response to the coronavirus, such as #trumpvirus (262 times), #trumpliespeopledie (32 times), and #trumphasnoplan (30 times).
A bipartite social network graph by hashtag-user co-occurrences (Figure 4) was generated to further investigate the association between hashtags and users within the network. If a user (i.e., a user node) posted a tweet with a certain hashtag (i.e., a hashtag node), a link (i.e., edge) would be created between that user and the hashtag. The more frequently a user employed a hashtag, the stronger their link would be. The bipartite social network graph visualized 5212 nodes, representing 3672 users and 1540 hashtags, and 48,396 undirected edges, representing user-hashtag co-occurrences.  #biden, #bidenharris2020, and #joebiden (#18, #19, and #22-most used hashtags) were employed by 490, 340, and 348 unique users, respectively. It can be seen that the number of users who used Biden-supporting hashtags were significantly, and understandably, lower than users who used Trump-supporting hashtags. Nevertheless, a user employing certain candidate-supporting hashtags did not necessarily mean that the user supported the particular candidate. For instance, among users who used #maga were @Earl18E (152 times) and @mcleod (78 times) whose Twitter activities indicated that they were Trump opposers. Similarly, @Drizzle_500 used #joebiden (60 times) while also employing #bidencorruption (288 times) and #bidencrimefamily (271 times), attempting to illustrate an ill-favored portrayal of the Democratic candidate.
Not only did the bipartite social network graph by hashtag-user co-occurrences help investigate the association between hashtags and users within the network, but it also assisted in examining certain users and hashtags of concern, thus revealing users' favorite hashtags and general sentiment, how hashtags were employed and whether they were employed following their original purpose (e.g., using #maga against Donald Trump instead of supporting him), and users' strategies of using hashtags to disseminate their messages, arguments, and ideologies within the social network.
The network graph by hashtag co-occurrence, while unable to comprehensively describe and explain tweets' content, helped identify the clusters of the most used hashtags, their relationship and association with each other, and how they were employed by users within the social network. It also assisted in studying particular hashtags of concern, partially revealing whether such hashtags were used intentionally or merely added as a mass-tagging strategy.

The Most Retweeted Tweets
A chart of the top 25 most retweeted tweets (Table 5), which were collectively retweeted 28,267 times, making up 12.8% of the corpus, was generated, indicating the types of messages Twitter users among the network were primarily engaged with and interested in retweeting. It further affirmed that such content was those expressing grassroots support for Donald Trump and the Republican party, believing in an easy victory for Donald Trump in the presidential race, urging eligible voters to cast their ballots, particularly for Donald Trump and his Republican allies, and smearing Joe Biden and his allies. Only #13 by @eortner, retweeted 556 times, framed the #maga community negatively by accusing #MAGA protestors in New York of being racist towards a black Lyft driver.

Discussion
This study is, perhaps, one of the earliest to probe into and provide insights on the community of Trump supporters and their communications during the 2020 US presidential elections. It attempted to not only better understand Donald Trump, his community of supporters, and their political discourse and activities, but to also investigate the participation of social media, particularly Twitter, in political discourse, positioning such concepts in the political context of the 2020 US presidential election.

The Hierarchy of Donald Trump
Donald Trump was understandably the most influential individual among the #maga and #trump2020 community on Twitter during the 2020 US presidential election, so significant as to the point that, as shown in Figure 2, no other individual or institution among the particular network, even those on his side, could compete with him. In Figure 2, Trump had a weighted degree of 2813, 3.37 times more significant than his runner-up Joe Biden (834), 13.14 times more significant than the Republican party itself (214), and 44.65 times more significant than his running-mate, Vice-President Mike Pence (63). It should be noted that by the time this study was conducted, Donald Trump's Twitter handle @realDonaldTrump had been permanently suspended by Twitter "due to the risk of further incitement of violence" after close review on 8 January 2021 (Twitter Inc. 2021), which means that his tweets could not be taken into consideration. Between 27 October and 2 November 2020, Donald Trump posted 366 original tweets via his handle, or 52.29 tweets per day on average (Median = 60, SD = 15.71) (Trump Twitter Archive 2016). He would rank third in the most active chart (Table 1) had his handle not been suspended. Imagine how even bigger and significant his node would be in Figure 2 had his 366 tweets been calculated.
In many ways, the activities, behaviors, and expressions of Donald Trump and his supporters, particularly on Twitter, showed characteristics of a cult of personality, a phenomenon referred "to the idealized, even god-like, public image of an individual consciously shaped and molded through constant propaganda and media exposure" (p. 29). Such idealized, or God-like, figure can then use their influence of public personality to manipulate others although their perspective often focuses on the cultivation of relatively shallow, external images (Wright and Lauer 2013). Hickman (2019) found similarities on the dimensions of cognition negative, contract negative, and performance negative via verbal characteristics between Donald Trump and charismatic leaders. Those charismatic leaders included Benito Mussolini, Joseph Stalin, Adolf Hitler, Vladimir Putin, Jim Jones, David Koresh, Mao Tse-tung, and Winston Churchill, among who were dictators (e.g., Benito Mussolini, Joseph Stalin, Adolf Hitler, and Mao Tse-tung) and notorious cult leaders (e.g., Jim Jones and David Koresh). Reyes (2020) also focused on Donald Trump's cult of personality and self-representation, positing that the 45th president of the United States had built his candidacy and presidency around his persona, distancing himself from the Republican party, traditional politics, and traditional politicians.
The media plays a crucial, instrumental role in the creation of leaders' cults of personality. The charismatic leader, especially in politics, has increasingly become the product of media and self-exposure (Wright and Lauer 2013). Gaufman (2018) used the Russian analytical paradigm of carnival culture to explain the popularity and political success of Donald Trump, arguing that the age of misinformation on the mass media, among other factors, had presented Donald Trump with a unique opportunity to leverage the power of social networks to his advantage. For instance, traditional mass media constantly reported about Donald Trump, conveniently boosting his visibility and disseminating his messages despite seldom taking him seriously. Findings of this study affirmed that the content posted among the #maga and #trump2020 community on Twitter during the 2020 US presidential election was primarily grassroots support for Donald Trump and, to a much lesser extent, his allies. Such results, and the fact that Donald Trump was seemingly the most prominent figure on his side (he was 13.14 times bigger than the party he represented), might suggest that Trump supporters' backing for him was somewhat unquestioning, and they merely regurgitated his rhetoric rather than doing their research and coming up with original contents.

The Problematic Nature of Bot Detection
The most active users in the corpus received an average bot score of 2.14, lower than the binary thresholds defined by Al-Rawi et al. (2019) (2.3) and Keller and Klinger (2019) (3.8), roughly equal to Wojcik et al. (2018) (2.15), and higher than Zhang et al. (2019) (1.25). The average bot score also generally signaled that Botometer's classifier could not be sure about the nature of this group of users. There were 34 users who received a bot score of 3 or above and if the deleted account of @christo31129690 was taken into consideration, it could be said that 35 (or 35%) of the most active users among the #maga and #trump2020 community on Twitter during the 2020 US presidential election were bots. Still, the bot-score evaluation approach using Botometer may be problematic.
Take @Drizzle_500 for example: the account tweeted 550 times during the seven-day period between 27 October and 2 November which was equivalent to averagely 78.57 tweets per day, or roughly one tweet every 18 minutes, nonstop. Such frequency of tweeting seems inhuman even for social media addicts. Howard et al. (2016) identified accounts having a high level of automation as those who posted at least 50 times a day since it was very difficult for human users to maintain such rapid pace of social media activity "without some level of account automation" (p. 4).
Nevertheless, on a scale from 0 to 5, with 0 for being human-like and 5 for performing like a bot, Botometer awarded both @Drizzle_500 a bot score of 1.4, which suggested that the user was relatively human-like. Rauchfleisch and Kaiser (2020) argued that Botometer bot scores were imprecise, especially if tweets were written in a language other than English, which consequently led to false negatives (i.e., bots being classified as humans) and false positives (i.e., humans being classified as bots) in estimating bots. Botometer admits that bot detection via software is a hard task and even trained eyes can be wrong, and the best approach to Botometer is to use the tool to complement instead of completely replacing human judgement. Additionally, binary classification of accounts using two classes (e.g., bot or not) can be problematic since few accounts are completely automated. While such approach to classify bots is not encouraged, a number of studies in social science research still adopt it for bot classification and estimation.
Theoretically, extremely active human users might achieve the "high level of automation" pace of social activity (i.e., posting at least 50 times a day), especially if they were merely retweeting contents (Howard et al. 2016, p. 3). Thus, it is suggested that this study's results regarding the estimation of spamming bots among Twitter users should be used as a reference rather than a definitive conclusion. Although bots did not account for the majority of the most active users, a percentage of 35% of the whole group was still alarming. Twitter claimed that their technological power to proactively identify and remove malicious usage of automation "is more sophisticated than ever," and they permanently suspended millions of accounts that were maliciously automated or spammy every month. They also criticized the approach of bot detection tools, including Botometer and Bot Sentinel, as extremely limited (Roth and Pickles 2020). Twitter's efforts, however, seem to be insufficient.
Many handles evaluated as having extremely high bot-like performance (i.e., received a bot score of 4 or above) or high bot-like performance (i.e., received a bot score from 3 to 3.9) were media outlets' verified accounts (e.g., CNN (@CNN, 4.2 and @CNNPolitics, 3.8), The Hill (@thehill, 4.2), The Washington Post (@washingtonpost, 4), New York Post (@nypost, 4.6), and Fox News (@FoxNews, 3.4)). Since Twitter accounts can be simultaneously controlled by both human and bots (i.e., semi-automated and semi-manual) (Rauchfleisch and Kaiser 2020), it can be concluded that the media also employ a certain level of automation to disseminate their agenda and contents.

Conclusions
Twitter has been, and will indeed continue to be, a forum for political participation, particularly political deliberation, a valid indicator of political sentiment, and an appealing vehicle for political conversations as discussed by Ausserhofer and Maireder (2013), Stieglitz and Dang-Xuan (2012a), and Yang et al. (2016). Twitter's political participation and influence in at least the four presidential elections in the last 13 years is evident. For instance, as 34,583,668 tweets were originally tweeted between 27 October and 2 November 2020, about the 2020 US presidential election, a tremendous amount of political information was created, disseminated, and absorbed by Twitter users, which might affect their decision-making process regarding who they should trust in and, eventually, vote for.
Nevertheless, the 2020 US presidential election result suggested that candidates' activity and prominence on social media, particularly Twitter, should not be perceived as a valid predictor of election outcomes. Donald Trump was apparently the circus master of the media circus he generated during the election (i.e., he was the most prominent and most significant figure, not only on social media but also all other media channels); still, it was Joe Biden who won the presidential race instead of the 45th president securing his second term in the White House. Thus, the study agrees with Groshek and Koc-Michalska (2017) in challenging the idea that liberal democracy in the United States was being harmed by social media, especially through its filter bubbles.
The study believes that the ugly and malicious sides of social media, particularly Twitter, will persist. Users with predetermined agendas will believe what they want to believe, utilize arguments that support their confirmation bias, and intentionally and strategically ignore science, truths, and facts. On the other hands, the study also concludes that while social media have flaws and limitations, they provide valuable political outlets and civic engagement opportunities for marginalized groups and people who are often considered politically and civically inactive (e.g., youths). These social platforms and formats are more appealing and accessible than traditional and conventional, typically drier, forms of political communication (Penney 2019). Additionally, if social media indeed have the power to carry an individual to the top, they can also take that individual down to rock-bottom, especially if their actions and behaviors violate standardized moral, decency, and social values.

Limitations and Future Study
The study recognized several of its limitations and, at the same time, proposed viable approaches for future research concerning internet memes, social media, and political communication. Due to Twitter's permanent suspension of Donald Trump's account, as well as many other Twitter handles who were spamming bots, their tweets, as discussed above, could not be included in the dataset. It might consequently make the dataset somewhat incomplete and, to some extent, unable to fully portray and characterize users and contents of the targeted social network. Still, the study is confident that the dataset was representative of the #maga and #trump2020 community during the 2020 US presidential election. Therefore, the data provided was adequate to examine Donald Trump's community of supporters and their political discourse and activities. Additionally, as the study identified some problematic aspects of the bot detection and estimation method, future studies on the topic are encouraged so that more refined, precise, appropriate, and trustworthy bot detecting methods can be offered to the social science community.
While the study probed into the Twitter community and their communication during the 2020 US presidential election, it only investigated the social network of Trump supporters rather than the networks surrounding both candidates. Thus, future research can examine the community of Biden supporters using hashtags equivalent to #maga and #trump2020. Comparative assessment of the two communities of supporters can be then provided, from which contrasts in their actions, behaviors, sentiment, and civility are highlighted. The social networks of political supporters and political contents on legacy media, as well as other social media channels such as Facebook, Reddit, Parler, or 4chan, should also be considered in future studies.
Finally, since the study referred solely to Twitter data, users' demographics could not be identified and analyzed. The media effects could not be determined via content analysis. Hence, ethnographic methods, such as interviews or surveys, are further needed to complement the findings of this study.
Funding: This research received no external funding.
Informed Consent Statement: All data collected and used in this study, including tweets and Twitter accounts, were publicly available via Twitter Public APIs. While some scholars argue that tweets and Twitter handles should not be quoted in research papers without users' consent, the author of this study believes that public Twitter data should be perceived as private data on public display on the basis of ongoing consent under contract (Gold 2020). Thus, as long as the data remain publicly visible, the normal ethical requirement of individual informed consent can be waived.
Data Availability Statement: Data available on request.

Conflicts of Interest:
The author declares no conflict of interest.

1
A dynamic interactive version of the social network graph by mentions with higher resolution and more details can be found at http://gorilladragon.org/dat_t/MDPI/Graph1/index.html (last accessed on 16 November 2021). 2 A dynamic interactive version of the social network graph by hashtag co-occurrences with higher resolution and more details can be found at http://gorilladragon.org/dat_t/Graph2/index.html (last accessed on 16 November 2021). 3 A dynamic interactive version of the bipartite social network graph by hashtag-user co-occurrences with higher resolution and more details can be found at http://gorilladragon.org/dat_t/Graph3/index.html (last accessed on 16 November 2021).