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Calidad Revistas Científicas Españolas
VOL.
32(1)/
2019
Author / Concha PÉREZ-CURIEL Universidad de Sevilla
Author / Pilar LIMÓN NAHARRO Universidad de Sevilla
More authors:  1 2
Article / Political influencers. A study of Donald Trump’s personal brand on Twitter and its impact on the media and users
Contents /

1. Introduction. The first 100 days of the Trump presidency in the political context of post-truth

Public opinion and the media were flabbergasted by Donald Trump’s victory in the 2016 presidential elections, in which more than 62 million American citizens voted for him. His first 100 days as US President have been indicative of his role as a communication and political marketing strategist. An influencer profile (with a generally negative valence) in a context of fake news making the front pages of the US reference press, which highlights the interest of the “old media,” now metamorphosed into the “new digital media,” in reproducing a tried and tested audience engagement model.

The figure of the influencer (Montoya & Vandehey, 2009; Pérez Ortega, 2014; Rampersad, 2009) refers to set of external personal perceptions that encapsulate the expectations, promises and experiences that an individual displays to others. Citizens have gone from being receivers of information to being prosumers, which has meant that, while still participating as spectators, they also now produce their own content (Rego-Rey & Romero-Rodríguez, 2016). Social networking sites (SNSs), especially Twitter, exhibit candidates with a brand image that not only provokes their political adversaries but also voters, political parties and media outlets professing the same ideology. This personalisation is regarded as the main defining feature of twentieth-century democratic politics (McAllister, 2007).

The visibility, speed and capacity for immediate response characterising social networks have become factors that multiply the effect of political influence. Both Trump and Obama have harnessed their potential using two very different political formulas. Trump, who has prioritised his personal account (@realDonaldTrump) over his institutional one (@Potus), has acquired over six million new followers since becoming President. He uses Twitter as a weapon to confront a host of intellectuals, politicians, businessmen and media outlets, regardless of whether or not they share his views. From an online marketing perspective, and unlike Obama’s strategy, Trump is managing his presidency and the US government with a non-stop barrage of tweets.

The conventional media have passed SNSs a baton that has not only converted them into information benchmarks but also into public opinion formers (Marcos, Sánchez & Olivera, 2017). This has apparently led to a spate of fake news empowering politicians such as Trump, in a clear example of how the circulation of half-truths-supported by the mass media in an idyll that rather than questioning the President has enhanced his image–has prospered. As is customary during presidential election campaigns, the Republican candidate’s statements and claims were fact-checked by specialised US news agencies. For instance, The Washington Post Fact Checker awarded him the highest score on its dishonesty scale –four “Pinocchios”–after observing that 64% of his statements (59 out of the 92 registered up until five days before the end of the campaign) were totally spurious.

Research into the first stage of the Trump administration calls for an analysis of the stance of the media, specifically the traditional press, as regards the daily barrage of messages, their topics and the influence that these dynamics have had on news selection and classification processes. A Pew Research Center report (2010), comparing blog, YouTube and Twitter content with that offered by the mass media, concluded that the most important social media stories and issues differed substantially from those published and addressed in the conventional media. Other studies have confirmed the similarities between political blog content and political articles published in the press (Adamic & Glance, 2005; Reese, 2007; Scott, 2005); e.g. on Twitter, which would confirm the digital media’s agenda setting capacity. Furthermore, and contrary to this stance, according to Roberts, Wanta and Dzwo (2002), the digital media have contributed to establishing alternative and independent agendas. Along these lines, Krane (2010) published a study focusing on the analysis of Twitter content disseminated by three media outlets –The New York Times, CNN and NPR– that bolsters the theory that there is a direct relationship between the content disseminated by the media and the topics that are addressed most often by users. The alternative agenda promoted by social media (Aruguete, 2017; Wallsten, 2007; Meraz, 2011; Sung-Tae & Young-hwan, 2007; Casero-Ripollés, 2015) activates a down-up mechanism, involving the citizenry and civil society, that can condition the media agenda thanks to the impact and reach of messages posted on SNSs. Opportunities for social and political change thus emerge.

The digital front pages of USA Today, The Boston Globe, The Wall Street Journal and The New York Times are indicators of the attitude of a sector of the Republican and Democratic media towards this politician and his showman’s discourse. In this connection, it is interesting to underscore the collusion between platforms (De Aguilera, 2014). The Internet and social media do no more than corroborate the principles of the theory of first –and second– level agenda setting (McCombs, 2005; McCombs & Evatt, 1995) in which it is the media that not only decide what topics are newsworthy, but also assess the substantive dimension (the candidate’s ideology, stance on problems, qualifications and experience or personality) and the affective dimension (the candidate’s positive, negative or neutral views and discourse).

Since Obama’s first campaign in 2008 until the last presidential race between Trump and Clinton in 2016 (Enli, 2017), Twitter has become increasingly more important. It can now be understood as a political communication tool, particularly during election time (Campos-Domínguez, 2017), making it possible to organise relatively quick and inexpensive campaigns with the potential to reach very broad target audiences (Karaduman, 2013; Thelwall & Cugelman, 2017). The pros for political parties and their representatives greatly outweigh the cons. If the Obama campaign in 2008 is remembered for its capacity to drum up grassroots support through the media and for having been orchestrated online (Kreiss, 2016), Trump’s 2016 campaign represented the viralisation of bots and fake news and the intensive use of Twitter.

Some politicians still regard social networks as information sources, but on these it is essential to listen, to reply and to update the latest news (Giansante, 2015). They contribute to political processes and democracy, insofar as they give voice to citizens and enable them to promote their own actions (Enguix Oliver, 2017), thus creating connected multitudes (Rovira, 2017). Nowadays, it is online users, converted into gatekeepers, who create bubbles of opinion among the members of their own communities, with like-minded preferences for political models. This has led to a crisis in journalism which, so far, has not known how to react to this new trend in which millions of news stories are shared and reproduced on the Web. Consequently, news stories tend to be more sensational and to go into less details (Thompson, 2017). There is also the widespread conviction that social networks are “neutral” media, thus implying that users are in full control (Enguix Oliver, 2017; Gainous & Wagner, 2014).

Before, during and after the presidential elections, Trump seems to have understood the utility of social networks, specifically Twitter, as a niche of influence and self-promotion. Some of the tactics employed by him are similar to the ideas expressed by Joseph Goebbels as regards propaganda. Twitter has developed as a tool of persuasion and propaganda in political contexts and situations of crisis, to such an extent that it has given rise to a rhetoric of persuasion and propaganda discourse models (Mancera Rueda & Helfrich, 2014). Voting for Trump was a knee-jerk reaction against the establishment, the continuity of the Clinton model, the status quo, globalisation, immigration and/or the press, and Twitter relentlessly multiplied the message “Make America Great Again.” Trump’s authoritarian style, his penchant for issuing strongly-worded and rather unconventional statements on race, gender, sexuality and foreign policy issues, gained him the support of some of the Republican delegates. However, it also provoked strong opposition from other conservatives (De las Heras Pedrosa et al., 2017).

Trump’s victory in the 2016 presidential elections and his mounting activity as an influencer make it necessary to reflect on a novel political news format consumed by users under the tyranny of the Internet.

2. Collateral effects of the Twitter-Trump influence on the media and fan communities

A background overview of the context of the 2016 presidential race reveals a hybridisation between social and conventional media in political communication. According to audience figures, US newspapers registered a 7% year-on-year fall in circulation in 2015 (Mitchell & Holcomb, 2016) and although a large proportion (51%) of the population followed the election campaign in the print press, there was a significant dispersion and fragmentation of sources consulted (Gottfried et al., 2016). Similarly, according to a study conducted by the Pew Research Center (Greenwood, Perrin & Duggan, 2016), the majority of US citizens claim that they read news on social networks and half of them allege that they followed the presidential elections online. Nonetheless, Twitter’s communication potential has been all but ignored by the political establishment: politicians continue to perceive it as a one-directional channel in which spontaneous conversations and messages are far and few between (Gómez-Calderón, Roses & Paniagua-Rojano, 2017). So far as the frequency of use is concerned, this seems to intensify at the end of campaigns (Bentivegna, 2014; Jürgens & Jungherr, 2015), as well as during televised debates (Bruns & Burgess, 2011).

The use of new algorithms replacing the gatekeepers of old offers users the opportunity to generate content, mobilise their fan groups and voice their opinions with a “like,” by sharing content or by posting comments (van Dijck, 2015; Casero-Ripollés, 2017). In this connection, the Pew Research Center has published a study that indicates that “about a third (32%) say they often see made-up political news online” (Barthel, et al., 2016). In turn, “about half (51%) say they often see political news online that is at least somewhat inaccurate.” While “about a quarter (23%) say they have ever shared made-up news stories themselves, with roughly equal shares saying they have done so either knowingly or unknowingly.” In this last group, 16% admitted to having shared fake news unintentionally, while 14% confessed to having done so intentionally. Moreover, in a study performed by Allcott and Gentzkow (2017), social media were the main source of information during elections for 13.8% of the respondents.

Trump’s Twitter candidacy has been totally eclipsed by his Twitter presidency. More often than not, he is an influencer who acts above and beyond party politics, applies corporate communication and marketing techniques, and has found in social networks an expeditious format and effective discourse that catches the attention of active communities, notwithstanding his all but complete lack of interaction with his followers. In this regard, several studies of the use of social networks in election campaigns have in fact highlighted the low level of interactivity. In contrast, they make intensive use of these digital technologies first and foremost as mechanisms for furthering their proposals and distributing their manifesto, applying a propaganda logic based on viralisation (López García, 2016; Dader, 2017; Campos-Domínguez & Calvo, 2017). All of which emphasises the limited transformation capacity of the rationales of political communication brought about by social media in an electoral context (Casero-Ripollés, 2017).

A comprehensive understanding of the current situation has allowed Trump and his team to design their main tactic, namely, to engage public opinion using conspicuous behaviour (via social networks on which the behavioural filters of the conventional media are conspicuous by their absence) and trust that the traditional media will spread the word (Simon, 2016). Trump’s tweets –pre-announcements of the front page news in the mainstream media– continue to repeat the campaign model with attacks against many different collectives. It has been defined as a discourse grounded in hate, fear, lies and scandal. In fact, some studies have revealed that voters perceive that Trump has no respect for Muslims (47%), immigrants (44%), people who do not support him (41%), Hispanics (37%), women (36%) or blacks (30%) (Doherty, Kiley & Johnson, 2016). The reason for this is that “over the past few years political mud-slinging has been replaced by verbal abuse as a weapon for convincing the masses” (Vallés, 2017). According to Karen Stenner, there are three aversions inherent to conservative thought–aversion to change, to the government and to difference–and Trump has often included all three in his speeches, images and videos, giving rise to a feeling of “us versus them” (Johnson & Brown, 2017).

The increase in leadership of politicians on social networks has coincided with the decline of the traditional media. Politicians resort to them less and less to reach their audiences, since they can now engage them directly on the information websites of public and private institutions (Salvador Benítez & Sánchez Vigil, 2016). For their part, the media have developed apps that analyse what content is most suited to being circulated on the Internet. For example, The New York Times uses an app (Blossom) that allows it to predict the web performance of news stories posted on its e-edition or blogs (Wang, 2017), while The Washington Post has developed the tool Bandito which, based on different variations, selects the most “compatible” combination of headlines and images (Marshall, 2016). All in all, the traditional media have shown a disproportionate desire to recover the prominent role that social networks have usurped. Hitherto, the media set the agenda and chose the news and the approach. Neither political parties nor candidates had full control over the final message. Nonetheless, SNSs are an excellent non-mediated communication platform because there is no intermediary re-elaborating or coding the message and audience feedback is facilitated (Túñez & Sixto, 2011). And, with its potential for agenda setting, Twitter stands out, establishing which messages are a priority for the sender, the order in which they appear and their very nature (López García, 2016).

A review of the front page news published during the first 100 days of the Trump presidency highlights the interest of the major media groups in reflecting, whether from a favourable or unfavourable perspective, the role of a president capable of monopolising the attention of the public on SNSs, as well as reinforcing the initial assumptions made about the influence of his storytelling on news selection and production processes. The chain of causes that explains this state of affairs includes the traditional media’s need to copy a communication model with an impact and which, in terms of political marketing, “sells.” On account of the fall in circulation in the wake of the social media invasion, they have thus resorted to pseudo-political sources and topics, i.e. the trademark of the Web.

3. Research plan: methods, objectives and hypotheses

The empirical basis of this study establishes as the main objective to identify the degree of influence that Trump’s tweets (O1), relating to the extent to which Twitter users follow and interact with his messages (O2), have on the US mass media (press).

This analysis is based on two initial research questions:

Do the media, and specifically the US reference press, reproduce Trump’s Twitter discourse and the topics that he covers, due to the great impact that these have on online users?

Is Twitter regarded as an online platform on which the influence of a user with Trump’s characteristics is capable of setting the media agenda?

The two related hypotheses can be disaggregated from this approach:

(1)        The Twitter discourse of President Trump influences and conditions the media agenda of the US press.

(2)        The traditional media (press) attempt to reproduce Trump’s communication model on Twitter and to publish the most engaging topics for users on their front pages.

Accordingly, the quantitative and qualitative content analysis performed here (Callejo, 2010) focuses on the number of tweets posted by the President and their topics, on their similarities to the front page news in USA Today, The Boston Globe, The Wall Street Journal and The New York Times, and on quantifying the frequency and valence (positive, negative or neutral) of user metrics (“likes,” retweets and comments). The selection of front page news has been based on objective circulation and production volume criteria, according to data provided by the World Association of Newspapers and News Publishers (WAN-IFRA) and the World Press Trends database, which both place these media high in the US press rankings. Regardless of their editorial lines, they have been highly critical of Trump’s policies, with attacks in their editorial pieces which began during the election campaign and have continued until this very day. Although the review of the literature goes back to the 2016 US presidential campaign and elections, which underlines the impact of the Republican candidate’s political discourse on the media (Rúas, Mazaira & Rodríguez, 2017), the study sample covers the first 100 days of the Trump presidency, from 9 November 2016 (post-election period) to 16 February 2017. This focus will help to verify whether, upon assuming the presidency, Trump has maintained the same political discourse as during the election campaign.

Trump’s personal Twitter account (@realDonaldTrump versus his institutional one @POTUS) has been chosen since it is the SNS most frequently used by politicians and on which the President has been most active and has shown the greatest degree of interaction with users (45 million followers during his first year in the presidency). Based on the initial assumption as regards the impact that Trump’s tweets have had on agenda setting in the US reference press (H1) and given number and variety of topics that he has broached on Twitter and his different approaches to them, only those tweets relating to immigration, international relations, women and the media have been selected, regardless of their media impact. Specifically, those tweets in which the President has taken a stance on an issue, has used a language of confrontation and has normally provoked a reply from the actors involved have been regarded as blocks. This trend may explain the interest shown by SNSs in new politics. In parallel, the Twitter accounts of USA Today, The Boston Globe, The Wall Street Journal and The New York Times (@USATODAY, @BostonGlobe, @WSJ and @nyt_front_page) have been used to locate the front pages of the print editions of these traditional newspapers reproducing images of Trump and/or specific issues that have triggered the strongest reaction and generated the largest number of replies from Twitter users.

An analysis of the variables figuring in the quantitative and qualitative content worksheet reveals, on the one hand, the subject matter and function of the tweets posted by Trump and, on the other, the number of front pages of the study reference press giving prominence to the politician in a personal capacity (rather than in representation of his party) and/or similar issues selected by these outlets themselves, owing to their power to arouse the interest of digital consumers.

3.1. Quantitative/qualitative content worksheet

The general variables established here are as follows:

 

Tweet

Topic

Media outlet

Topic/front page

Presence/absence of Trump

User metrics

Language

 

There are also other supplementary variables deriving from the main ones such as “specific topic,” “message valence,” “citizen participation” (number of “likes,” retweets and comments), “propaganda mechanisms” and “mechanisms of persuasion.”

Considering the number of tweets posted by Trump on his personal account during the study period (the first 100 days of his presidency) and the statistical figure of seven tweets per day on average (700 tweets), out of a total of 519 tweets only those relating to Trump or to the main issues under study here (51) and making the front pages of the press have been selected for analysis. Likewise, 5,732,111 “likes,” 1,552,285 retweets and 1,348,006 comments on the four issues that provoked the strongest reaction from users have been selected and analysed.

As the results suggest, the traditional newspapers reproduce those messages and images of the President on their front pages that swell follower numbers for any number of reasons, from approval to aversion, through indifference, and an information and discourse model that since it works on social media, should also work for them. A content analysis of these front pages reveals the thematic correlations and their media coverage, in addition to the degree of similarity to the idiosyncrasies of Trump’s online political discourse. In this respect, the study worksheet includes a section covering political language resources that not only addresses what is posted by the President and echoed in the media, but also the ins and outs of the discursive strategies employed.

Trump’s role as a politician with an influence on media agenda setting also affects Twitter users. With regard to the second hypothesis, the frequency and valence of prosumer metrics (“likes,” retweets and comments) have been quantified with the aim of gauging the degree of influence that Trump exerts on online users (H2), as well as the impact that the most commented topics have on the press. The majority of the traditional media outlets resort to social media like Twitter to keep abreast of the news. However, research conducted in this field reveals that the use that journalists make of these media is generally limited to crowdsourcing, i.e. when they resort to the public at large as news sources.

4. Results analysis

In order to meet the established objectives and identify the possible effects of Trump’s message on the media and online users, the procedure for measuring the results has been divided into three different blocks: (numerical) quantification; (thematic) qualification; and discursivity (language markers). The first two are linked to quantity, frequency and content, while the third refers to propaganda and persuasion resources in the realm of rhetoric.

IBM SPSS Statistics 24 has been used for data processing and elaborating the tables and graphs, while different types of variables from among the options provided by the software have been employed for data coding. The numerical variables, used for mere quantification, such as the number of related tweets (pertaining to other facts), and the subvariables of citizen participation (number of “likes,” retweets and comments) have been classified as scale variables. The aim of the categorical variables, in which there are only two possible replies (yes or no) and whose aim is to quantify the percentage of positive and negative cases, have been coded as nominal dichotomous variables (yes = 1, no = 2). This type includes “consistency with current affairs” and “presence/absence of Trump in the front page news” (a variable for each newspaper). The categorical variables, to which there are only two possible replies, have also been coded as nominal variables, each possible answer having been assigned a numerical value (1 = a, 2 = b, 3 = c). These correspond to “specific topic” and “message valence.” Lastly, the analysis worksheet contains categorical variables or subvariables with more than two possible replies. Afterwards, the different variables have been grouped together with the option of defining variable sets with multiple answers. Thus, the unique variables as shown in the analysis worksheet are represented as variable sets in SPSS. These belong to those of language (“propaganda mechanisms,” “mechanisms of persuasion” and “general topic”).

4.1. Quantification and qualification indicators

As already noted above, given the sheer number of tweets on such a large variety of issues only those pertaining to immigration, international relations, women and the media have been analysed here. During the election campaign, most of Trump’s tweets, in both on and off mode, had to do with one of these four issues.

The media were the main target of the North American tycoon’s tweets (43.14%), followed by international relations (35.29%) and foreigners or immigration (19.61%), with women being the target of a mere 1.96% of his messages. Trump posted a total of 51 tweets dealing specifically with these issues.

The cross table containing the subvariables of the “valence” (positive, negative or neutral) and “specific topic” of Trump’s tweets represents the approach and valence of each tweet, according to the blocks selected here.

 

Table 1: Specific topic versus Trump’s message valence.

 

Message valence

Total

 

 

 

 

 

 

 

 

 

Specific topic

 

Immigration

 

Positive

Negative

Neutral

 

Tally

0

10

0

10

% of the specific topic

0.00%

100.00%

0.00%

100.00%

% of the message valence

0.00%

24.40%

0.00%

19.60%

% of total

0.00%

19.60%

0.00%

19.60%

International relations

Tally

4

10

4

18

% of the specific topic

22.20%

55.60%

22.20%

100.00%

% of the message valence

100.00%

24.40%

66.70%

35.30%

% of total

7.80%

19.60%

7.80%

35.30%

Women

Tally

0

1

0

1

% of the specific topic

0.00%

100.00%

0.00%

100.00%

% of the message valence

0.00%

2.40%

0.00%

2.00%

% of total

0.00%

2.00%

0.00%

2.00%

The media

Tally

0

20

2

22

% of the specific topic

0.00%

90.90%

9.10%

100.00%

% of the message valence

0.00%

48.80%

33.30%

43.10%

% of total

0.00%

39.20%

3.90%

43.10%

 

 

Total

Tally

4

41

6

51

% of the specific topic

7.80%

80.40%

11.80%

100.00%

% of the message valence

100.00%

100.00%

100.00%

100.00%

% of total

7.80%

80.40%

11.80%

100.00%

Source: Own elaboration.

First and foremost, it can observed that the majority of Trump’s negative tweets were targeted at the media (90.9%).

 

 

 

All the tweets dealing with international relations were positive, while all those focusing on immigration were negative.

To ascertain the possible influence of Trump’s discourse on agenda setting in the media and on social media users, the quantitative data pertaining to users’ responses (“likes,” retweets and comments) have been linked both to those relating to the President’s presence/absence and the specific topics covered in the front-page news. The causality lies in the fact that the media select the topics covered by Trump that have the greatest impact on users (given the personal brand of the President’s discourse) with the aim of engaging users by arousing their interest. The results also underscore that Trump’s pet topics on Twitter, which then make it to the front pages, do not always coincide with current affairs. For they are determined by the power of attraction of the leader’s personality and discourse.

 

Graph 1: Correlation with current affairs.

 

Source: Own elaboration.

The media –as the discourse analysis will show later on– give Trump front page coverage due to his power of attraction evinced in the tweets that his followers retweet most. This triangular relationship highlights the influence (irrespective of its valence or value) that Trump exerts on his Twitter followers and on the traditional press. This chain of effects (Trump-consumers-the media) conditions the agenda and makes any story that performs well on social networks newsworthy. It is a new journalistic model in which the fundamental principles of objectivity, contrast and social responsibility are challenged.

The following histograms show the frequency and evolution of the metrics of Trump’s personal Twitter account during his first 100 days in office.

 

Graph 2: Case summary. Number of “likes”.

 

Source: Own elaboration.

The 51 tweets analysed here obtained a total of 5,732,111 “likes,” the average number being 112,394, the median 104,063 and the minimum and maximum values 51,985 and 224,350, respectively.

 

Graph 3: Case summary. Number of retweets.

 

Source: Own elaboration.

Trump’s 51 tweets were retweeted 1,552,285 times, the average being 30,436, the median 27,581 and the minimum and maximum values 14,215 and 95,763, respectively.

An analysis of the metrics has made it possible to determine the dissemination or viralisation capacity of Trump’s personal Twitter account and, accordingly, the influence that he is capable of exerting on the public. A double value has been assigned to retweets versus “likes,” because with retweeting the content of the original tweet appears in the timeline of whoever has retweeted it, thus broadening the message’s reach. Whereas with “likes” this is not the case since the original tweet does not appear in the timeline of whoever clicks on the button and, consequently, the message’s reach is not increased.

The dissemination or viralisation capacity (influence) of the account has been calculated by adding the total number of retweets multiplied by two to that of “likes,” divided by the total number of original tweets posted.

Dissemination or viralisation capacity = (SUM RT*2+SUM FAV)/SUM tweets posted.

 

Table 2: Viralisation capacity of messages on Twitter.

Tweets

Retweets

Retweets x 2

“Likes”

Total

Total/No. of tweets

51

1,552,285

3,104,570

5,732,111

8,836,681

173,268

Source: Own elaboration.

 

Graph 4: Case summary. Number of comments.

 

Source: Own elaboration.

The last variable analysed here, and possibly the most important among the metrics used to gauge the interaction between Trump and the public, is the number of comments. The 51 tweets were commented on 1,348,006 times, the average being 26,431.49 comments per tweet, the median 22,882 and the minimum and maximum values 4922 and 69,115, respectively.

As regards valence, the comments have been assigned the following values: 1 (positive valence); 2 (neutral valence); and 3 (negative valence). The following table shows a breakdown of the comments according to their valence.

 

Table 3: Comments and their valence.

Comments

Valence 1

Valence 2

Valence 3

Total

1,348,006

71%

8%

21%

100%

Source: Own elaboration.

The percentages reflect a marked difference between the positive and negative valence of user comments. This points to support for Trump’s interventions on Twitter (particularly those featuring mechanisms of persuasion or propaganda) versus a much lower percentage of negative opinions. As had occurred with the election results, which called into question the polls and stunned the media and political establishment, alike, US citizens continued to endorse the President’s views which was incommensurate with the front page coverage that, as will be seen, the US reference press gave him.

Using the information on the impact of Trump’s messages on online users, the next step is to check the extent to which this influence was reflected on the front pages of the press, with the President being associated with the study topics, i.e. “immigration,” “international relations,” “women” and “the media.”

 

 

Table 4: Trump in the headlines of The New York Times.

The New York Times

Frequency

%

Valid

Yes

32

62.7

No

16

31.4

Total

48

94.1

Lost

Data

3

5.9

Total

51

100

Source: Own elaboration.

 

Table 5: Trump in the headlines of USA Today.

USA Today

Frequency

%

Valid

Yes

25

49.9

No

22

43.1

Total

47

92.2

Lost

Data

4

7.8

Total

51

100

Source: Own elaboration.

 

Table 6: Trump in the headlines of The Wall Street Journal.

The Wall Street Journal

Frequency

%

Valid

Yes

36

70.6

No

11

21.6

Total

47

92.2

Lost

Data

4

7.8

Total

51

100

Source: Own elaboration.

 

Table 7: Trump in the headlines of The Boston Globe.

 

The Boston Globe

Frequency

%

Valid

Yes

28

54.9

No

19

37.3

Total

47

92.2

Lost

Data

4

7.8

Total

51

100

Source: Own elaboration.

 

The table above contains data relating to frequency and presence percentages, bearing in mind data lost in the SPSS analysis. As can be seen, Trump made it to the front page more often in The Wall Street Journal (76.6%), followed by The New York Times (66.67%) and USA Today (53.19%).

An analysis of the 51 selected tweets discloses the linearity between tweet content, its effect on followers and how it was reflected in the headlines. The media are one of the main targets of Trump’s attacks (proportionally speaking they are the primary focus of his attention). Similarly, it shows how content shifts from online to offline on the basis of a format highlighting the centrality of Trump’s personality and, at the same time, the triple impact that he produces on politics, the media and the public at large.

 

 

 

 

 

A quantitative content analysis of the examples highlights the number of “likes,” retweets and comments that this tweet against the press obtained at the time. Furthermore, it reveals his central argument (with negative connotations) against the media outlet in question and that the newspapers published clearly unfavourable news in this respect on their front pages.

By the same token, as regards international relations the effect produced by the tycoon’s statements on conflicts with China, Russia and Mexico, among other countries –messages that made it to the headlines in the traditional press– can be clearly seen.

 

 

 

The US reference press covered these attacks due to the engagement that Trump had generated with this tweet (119,854 “likes,” 39,298 retweets and 23,271 comments) with front page headlines such as “Democratic House Candidates Were Also Targets of Russian Hacking” (The New York Times) and “Republican National Committee Security Foiled Russian Hackers” (The Wall Street Journal).

Data crossing reveals that Trump’s tweets on international relations (61/45%) and immigration (60/25%) received more press coverage than those dealing with the media (31/29%).

The following table shows the causality between the specific topic to which Trump’s tweets referred, the proportion of online users following him (user metrics) and the presence and valence of the front-page headlines.

 

Table 8: Front page thematic frequency and valence according to user metrics.

Topic

Metrics

Front page

Main valence

Immigration

37.00%

33.00%

21 % (-) 12 % (+)

Foreign affairs

39.00%

30.00%

22 % (-) 8 % (+)

Media

19.00%

27.00%

19 % (-) 8 % (+)

Women

5.00%

10.00%

10 % (-)

Total

100.00%

100.00%

100.00%

Source: Own elaboration.

Immigration and foreign affairs caught the attention of users and the selected newspapers, findings that do not coincide with the percentage of tweets with a negative valence with which President Trump targeted the media. The reference press reproduced those of Trump’s tweets that caused the greatest stir among his community of followers. Trump’s image (subject-personality-populism) outweighed newsworthy topics (object-political group-Government), an issue linked to the discourse model that defines his strategy on and off Twitter.

4.3. Linguistic markers and discursivity

In light of the above, the assumption of Trump’s role as a political influencer on social networks gains strength. General traits such as impact and the capacity to mobilise public opinion prompt reactions to a specific issue and increase audience, participation and engagement levels, together with the use of a simple, pithy, rhythmic and all but syllabic language that conceals a discourse of domination, empowerment and leadership. The Trump brand, from a marketing and business marketing perspective, evinces the interest in “selling” a daily product on Twitter. In this sense, the theory that the continuous presence of a head of state is due more to a question of personal identity (who and how) than to the object of conflict (what) is grounded in the results obtained in studies of the use of language strategies. Events fade into the background and it is the politician who turns them into news stories, who positions them on the Web and thanks to whom they become trending topics. In line with this assumption and using the classification of propaganda resources in discourse (Nocetti, 1990; van Dijck, 2015), a set of variables applied to the quantitative measurement of the propaganda mechanisms in Trump’s Twitter discourse have been analysed.

 

 

Table 9: Propaganda mechanisms. Frequencies in tweets/front pages.

 

 

No. of cases

% of cases

Propaganda mechanisms

Use of labels

23

47.9%

 

Submitting testimonies

6

12.5%

 

Emphatic sweeping statements

33

68.8%

 

The common man

5

10.4%

 

Transferring respect

10

20.8%

 

Creating stereotypes

20

41.7%

 

Speaking through other sources

3

6.3%

 

Tendentious claims

42

87.5%

 

Selecting information

27

56.3%

 

Ambiguous authority

11

22.9%

 

Opinions as facts

29

60.4%

Source: Own elaboration.

Tendentious claims made employing adjectives and verbs loaded with positive or negative connotations as regards the subject matter (immigrants, international relations, women or the media) were the most frequent (87.5%), followed by emphatic sweeping statements (68.8% of the tweets) and presenting opinions as facts (60.4% of the tweets).

A discourse analysis of the study sample indicates a frequent use of linguistic markers and figures of speech typical of the language of persuasion and propaganda, persuasive arguments prevailing over deductive or merely informative ones. Double entendre, irony, fallacy, subjectivity and lack of emotion are some of the characteristics of a discourse that, as has been seen in the analysis of the level of engagement of social media users, had a positive effect.

Due to the success of this discursive format, the media reproduce the persuasion and propaganda strategies in their front page headlines, evincing yet again their interest in reproducing not only the topics covered in Trump’s tweets, but also the language characterising his personality.

The power of attraction and seduction of the leader as a person seems to have worked during both the election campaign and his first months in office. This idiosyncrasy of Trump’s personality and the strategic use of Twitter, together with a general climate of dissatisfaction with other political candidates (i.e. Clinton and Obama), the public’s disavowal of the establishment and the questioning of the transparency of the media, might be among the reasons behind his success as a political influencer and prosumer.

5. Conclusions

An analysis of the influence that US President Donald Trump has exerted, and still exerts, through Twitter on SNS users and on the traditional media has confirmed the main research hypothesis. The agenda of the newspapers analysed here reproduces the discourse model of the President, his image and the topics shared by users which, subsequently, go viral.

Twitter is a digital platform capable of establishing and conditioning the agenda setting of the traditional media in the case of a profile such as that of Trump. The results (obtained from a quantitative and qualitative content analysis) show the President’s level of presence, the thematic frequency of specific political topics (immigration, foreign affairs, women and the media) and the (above all negative) valence of the tweets that he posts on the Web and which then make it to the front pages.

The media (USA Today, The Boston Globe, The Wall Street Journal and The New York Times) dedicate their front pages to the President and to the issues to which he gives priority in his daily tweets, irrespective of whether or not they have anything to do with current affairs. The choice of topics is determined by those issues that online users –followers of Trump’s personal Twitter account– “like,” retweet and comment on, thus converting him into the principal information source, the topics that he addresses into burning issues and Twitter into the best positioned SNSs as regards political information.

Trump has made the most of his clash with the media, with which he was at loggerheads even before the start of the 2016 election campaign, all of which has served to disseminate his discourse on the Web. As shown by the data, Trump has made it more often than not to the front pages of newspapers such as The New York Times and The Washington Post, which did not offer him their support neither as a candidate nor as President.

Trump appears as the linchpin of his own discursive strategy. These types of leaders implement discursive strategies that allow them to master a language that, notwithstanding its superficial simplicity and leisurely pace, includes resources of persuasion and propaganda with an impact –of a positive or negative valence– on the media and, by extension, on the general public (the voters). Trump’s tweets are characterised by their persuasive, rather than informative or deductive arguments. As can be seen in the contingency tables dealing with the discourse markers, the language of Trump’s tweets is directly reproduced in the front-page headlines.

As an approach to the concept of political influencer and using methodological triangulation, a number of conclusions relating to the extent to which Trump managed to engage Twitter users during the first 100 days of his presidency have been reached. Public support for Trump has been confirmed by the number of “likes,” retweets and comments, the latter with more positive than negative connotations.

This study provides food for thought on the social media role of politicians, the invasion of the traditional media by a new communication paradigm and, above all, the action-reaction of citizens who reproduce the discourse of spectacle, thus empowering new political strategists: influencers.

 

We would like to thank Antonio Montoya Sánchez, MA in Institutional and Political Communication from the University of Seville, for his collaboration in the statistical analysis.

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