Online social networks, such as Facebook and Twitter, play an increasingly important role in society. They are widely used by the public, and they are also increasingly employed by politicians, especially since the 2008 U.S. presidential campaign (Cogburn and Espinoza-Vasquez 2011). U.S. President Donald Trump seems to be quite successful in adopting Twitter as a strategic communication tool to disseminate his views and to attain popularity, practice self-promotion and criticize his opponents (Goldfarb 2017; Kreis 2017). He was also present and active on Facebook (FB) during his presidential campaign.
One of Trump’s signature positions is his anti-immigrant stance, one of the aspects that was instrumental in attaining and increasing his popularity. A sizable portion of the U.S. population appears to feel threatened by the ongoing flow of immigrants seeking refuge or a better standard of living. These citizens request a halt or a slowdown of legal immigration and deportation of “illegals” – those who came to the country without proper documentation.
Anti-immigrant anger was initially focused on migrants from Latin and South American countries, especially Mexico, but later, prompted by the Syrian refugee crisis in Europe and exacerbated by reports of several terrorist attacks, it has shifted to include the group designated by the general term ‘Muslims’. Followers of Islam are often discussed, and sometimes discriminated against, as a homogenous group as if all of its members shared the same set of characteristics (Akbarzadeh and Smith 2005; Baker, Gabrielatos and McEnery 2012; Poole 2002; Törnberg and Törnberg 2016). This is how the then presidential candidate Trump referred to them when he suggested a “total and complete shutdown of Muslims entering the United States until our country’s representatives can figure out what the hell is going on” during one of his campaign rallies (Trump 2015).
This message seemed to resonate with the public: the post on Trump’s official FB page with a video clip from the rally where the proposal was made attracted over 17,400 responses within two weeks (over 35,000 at the time of this article’s preparation). Many of those responses received replies of their own, and some of the threads consisted of several hundreds of comments. The resultant debate is worth investigating as it provides insight into the discursive strategies of representing the concepts of ‘Muslim’ and ‘immigrant’ in U.S. online political discourse, and into the ideology of Trump’s supporters as expressed on social media.
Trump’s supporters claimed that they represented mainstream U.S. views as the “silent majority” which supposedly had found its voice and refused to be sidetracked by the “vocal minorities” dominating the U.S. social and political scene. Several FB groups combine the phrase “silent majority” and the name of Trump in their title (e.g., “Trump 2016”). Trump, in turn, has referred to his supporters as the “silent majority”, promising that they will be silent no more and implying that he would verbalize their beliefs and positions (for example, see Crowley 2016). However, such beliefs might be influenced by the tendency of individuals’ social networking accounts to develop info media bubbles and echo chambers (Geschke, Lorenz and Holtz 2019; Pariser 2011; Schwarz and Shani 2016). Social media that allows users to “unfriend” and block others enables people to hide from unfavorable voices, discourages dialogue between different factions, and deepens social division (Baysha 2020; Stroud 2010).
This project aims to determine how closely the ideology of Trump’s social base of supporters was aligned with mainstream American views on immigration and Muslim immigrants in the year before his election. This is done by analyzing the discourse of the group devoted to the issue of Muslim immigration and comparing it to the Corpus of Contemporary American English (COCA), which is a widely-used, genre-balanced corpus of American English of over one billion words of text (over 20 million words each year from 1990 to 2019) (Davies 2008). By analyzing linguistic phenomena, this article makes inferences about the cognitive and psychological features of Trump’s supporters as a discourse community – that is, a group of people sharing a set of basic values, assumptions, and particular ways of communicating (Porter 1992). The representation of Islam and of Muslims in the conversations of the Trump supporters’ discourse community is examined by studying the usage of such keywords as ‘Muslim’, ‘Islam’, ‘Quran’ (Koran), ‘Sharia’ (Shariah), ‘immigrant’ and ‘refugee’.
Immigration (and anti-immigration) discourse has been studied, among others, by van Dijk (2000), Cisneros (2008), Mamadouh (2012), Hart (2013), Gattino and Tartaglia (2015), Knoblock (2017), and Musolff (2019). Representation of Islam and Muslims in traditional media, such as newspapers and magazines, has also been researched (Baker, Gabrielatos and McEnery 2012; Baker and McEnery 2005; Gabrielatos and Baker 2008; Moore, Mason and Lewis 2008; Poole 2002; Richardson 2004), and the examination of the treatment of Islam in the western news media has generally found evidence of negative bias (Akbarzadeh and Smith 2005; Awass 1996; Mårtensson 2014; Kassimeris and Jackson 2015; Richardson 2004; Saeed 2019).
While the analysis of mass media is useful, it makes sense to extend research to the investigation of social networking as a close representation of the public opinion externalized in discourse. Indeed, researchers are turning their attention toward that domain by studying Islamophobia in cyber contexts. For example, Aguilera-Carnerero and Azeez (2016) and Awan (2016) scrutinized Islamophobia on Twitter, Oboler (2016) investigated how Facebook is being used to legitimize hatred of Muslims, and Törnberg and Törnberg (2016) provide an insightful analysis of an online forum known for its right-leaning bias. Unfortunately, the studies focusing on these processes within social media are fewer than those studying traditional media, such as newspapers and broadcast journalism. One probable reason for this is the practical difficulties of collecting, processing, and analyzing the large amounts of unstructured textual data in social media. To avoid these concerns, the current project is a Corpus-Assisted Discourse Study.
Corpus-Assisted Discourse Studies (CADS)
The CADS approach combines elements of Critical Discourse Analysis and Corpus Linguistics. Several authors have suggested that corpus linguistic methods can effectively support quantitative and qualitative research in discourse analysis (Baker et al. 2008; Gabrielatos and Baker 2008; Partington 2006; Salama 2011). This combination is lauded as benefitting from both the rigor of the computational analysis and the richness of subsequent qualitative examination. It has gained popularity, in part, because of the reduction of research subjectivity as well as the improvement of research validity through focusing on quantifiable elements of discourse. Recent studies combining CDA and CL apply corpus linguistic tools and examine statistically significant collocations to reveal ideological information about the groups they study (Baker and McEnery 2005; Baker, Gabrielatos and McEnery 2012; Gabrielatos and Baker 2008; Knoblock 2017, 2020; Orpin 2005; Prentice and Hardie 2009).
The current project continues the trend of addressing the attitudes toward Muslims in online communities using Critical Discourse Analysis (CDA), a well-established framework for research into the relationship between language and society. It underscores the strategic character of linguistic acts and emphasizes the idea that texts are based upon choices, which are ideologically and sociologically driven (Fairclough 1995). It also emphasizes the interconnectedness between discourse and ideology. In accordance with van Dijk (1995: 17), “ideologies are typically, though not exclusively, expressed and reproduced in discourse and communication”. Thus, it is possible to reconstruct mental structures existing in the national consciousness that would be unavailable for direct observation given the information provided by the discourse.
Corpus Linguistic Methods
The issues of collecting and processing material for this study have been addressed by utilizing Corpus Linguistic Methods which have gained popularity with the development of the machine processing of text. This study utilized the online corpus management system Sketch Engine (Kilgarriff et al. 2004), and the analysis included frequency lists, identifying collocations, and comparing the comments on Trump’s FB page and the COCA corpus.
Frequency of particular words in corpora provide insights about the salience of certain terms and topics in genres, modes of communication, or particular groups. Frequency results can be used to draw conclusions about the correlation between the structures of the text and social and political phenomena. Typically, frequencies are calculated in the number of occurrences per million of words as such normalized figures can provide more meaningful comparisons between texts of different lengths. Another prominent corpus linguistics technique is identifying collocations. Collocation is the above-chance, frequent co-occurrence of two words within a pre-determined span – usually five words on either side of the word under investigation (the node). The statistical calculation of collocation is based on the frequency of the node, the collocates, and the collocation. The higher the MI score, the stronger the link between two items; an MI score of 3.0 or higher suggests evidence that two items are collocates (Hunston 2002: 71). A score closer to 0 indicates a likelihood that the two items co-occur by chance, and a negative MI score indicates that the two items do not co-occur.
Procedures and analysis
The material for the study consists of the comments left after Trump’s FB post about his proposal to ban Muslims from coming to the U.S. The choice of FB is dictated by its position as the dominant social networking site since 67% of American adults use this platform, compared to LinkedIn (20%) and Twitter (16%) (Rainie, Smith and Duggan, 2013). Although FB discussions evolve over time, the corpus reflects the state of the conversation at the time it was collected in January 2016. The corpus, nicknamed Ban-the-Muslims (BTM), started with 856,769, and then was reduced to 739,466 tokens, or 621,335 words, by the adjustments described below.
To ensure validity, it was necessary to separate the comments of Trump supporters from the writing of those who left critical remarks. However, software was unable do it, and manually sorting the two sets would have been prohibitively time-consuming. Instead, the researcher manually checked the concordance lines including all tokens of ‘Muslim’ and deleted comments that expressed a critical attitude toward Trump or his proposal. Even though the resulting corpus, almost definitely, still contains many comments made by people who joined the discussion to argue against the ban, those comments should not affect the concordances and constructions involving the lemmas discussed here. To reduce the influence of pre-compiled texts which were reposted multiple times, and concentrate on the spontaneous discussion, frequency lists and collocates were manually scanned, and if multiple postings of identical texts were detected, all but one occurrence were deleted from the corpus. Words written in foreign scripts were eliminated.
The results obtained from the BTM corpus were compared to the data from the COCA corpus. COCA was chosen because it is arguably the largest, well-balanced, and up-to-date corpus of the American variety of English freely available for research. Considering that COCA accumulates a very large sample of texts (approximately 20 million words) a year, evenly divided between several genres (20% each of spoken, fiction, popular magazines, newspapers, and academic texts), it serves as a good reference point of the national view on the issue discussed in the BTM. The choice was also influenced by the fact that COCA allows limiting searches to a particular year. This project needed to stay within the context of the BTM corpus, and Trump’s proposal came on 17th November 2015, so the year 2015 was used for COCA searches.
Two research questions were posed in the current study:
Is there any significant difference in terms of lexical frequencies and distributions between Trump supporters’ discourse and the national discourse regarding Muslim immigration?
What are the collocation patterns of the lexemes identifying Muslims in the corpus under analysis? Is there a significant difference in the image of Muslims shared by the focus discourse community and the general American public?
The analysis of the corpus proceeded as follows: first, the word frequency list was created to identify the most frequent and salient lemmas; then, collocations of the keywords were identified and examined; and, finally, the outcomes were compared with the data from COCA in order to identify any mismatch between the ideology of the group under analysis and the overall American attitude toward Muslims.
Results and Discussion
The Sketch Engine Word List tool was used to identify the most frequent words in the corpus. Unsurprisingly, the most frequent content/open class lemma was TRUMP, which was used 7,395 times (10,000.50 words per million or wpm). The next 24 were:
These 25 most frequent lemmas seem to draw a triangle of three main agents: Trump, the USA and its citizens, and Muslims. After that, the high-frequency words describe the needs, intentions, or actions of those agents.
Checking the frequency of these lemmas in the COCA corpus, we see that they are considerably less prominent there. The name TRUMP occurs only 3,064 times during the whole year 2015, so its wpm score is about 0.015; MUSLIMS are 1,882 or 0.009 wpm; and AMERICA/AMERICAN are 12,602 or 0.063 wpm. The frequency list showed that the BTM corpus compiled for this project is a good source of information about the attitudes toward Muslims and beliefs about them since the conversation revolves around Islam and Muslims in general and Muslim immigration to the U.S. in particular.
Having identified the focus terms for further investigation, the collocation function of Sketch Engine was used to compile a list of collocations of the lemma MUSLIM. The collocates were arranged according to the overall frequency of the collocation in the corpus, but they had to have an MI of 3 or higher to ensure collocational significance. They also had to be lexical rather than functional and to appear in the corpus a minimum of twice in order to be included. The results were later compared to the list of collocations of the lemma MUSLIM from COCA texts from the year 2015.
Ban-the-Muslims and COCA Comparison
A query for MUSLIM produced 3,683 concordance lines (4,981wpm) in the BTM corpus. Below is the list of 50 most frequent collocates. The number before the word indicates its rank by frequency, the number after it is the raw frequency of the collocation in the corpus, and the number in parenthesis – the MI score.
Several of the frequent collocates have negative connotations. The third most common one, the verb to ‘ban’, shows the attitude of the speakers who want to keep Muslims out and away from their country. The fourth most common collocate is ‘terrorist’ and the tenth is to ‘kill’. Lower on the list, one encounters the words ‘radical’, ‘problem’, ‘hate’, and ‘bad’, and the list of the 50 most frequent collocated words finishes with ‘sharia’ (which carries an extremely negative connotation in the discourse community under analysis), ‘bombers’, and ‘attack’.
Several of the words carry a positive connotation, such as ‘good’ or ‘peaceful’; however, the examination of the concordance lines shows that they are mostly used in the sentences stating that all Muslims need to be banned because it is difficult to determine which ones are good and peaceful and which ones are not. Some collocates, such as ‘Koran’ or ‘Islam’, refer to Muslims’ religion. A prominent group of verbs frame Muslims as an outgroup by focusing on their perceived desire to enter the USA (‘come’, ‘want’) and the need to prevent them from doing that (‘ban’, ‘stop’, ‘let’, ‘allow’, ‘keep’).
Comparing the semantic profile of the lemma MUSLIM in COCA corpus, we encounter the following 50 most frequent lemmas collocating with it:
Looking at the common collocates of the word Muslim obtained from the COCA collection of texts from 2015, it is easy to see that the discussions mentioning Muslims were more evenhanded and did not carry much negativity toward Muslims. Many of the collocates are related to geographical or ethnic concepts (‘France’, ‘French’, ‘Bosnian’, ‘countries’, ‘British’, ‘Arab’, ‘European’, ‘Western’, ‘Hindu’, ‘Hindus’, ‘German’, ‘Roma’, ‘Turkish’) or religion (‘Christians’, ‘Christian’, ‘Jews’, ‘Sunni’, ‘Muslims’, ‘Islam’, ‘Muslim’, ‘religious’, ‘religion’, ‘Jewish’, ‘prayers’, ‘faith’, ‘Islamic’, ‘non-Muslims’, ‘Shiite’, ‘cleric’, ‘devout’). The only troubling collocate that was frequently accompanying the words ‘Muslim’ or ‘Muslims’ was ‘brotherhood’, undoubtedly referring to the Muslim Brotherhood organization considered a terrorist group by the U.S. However, the lemmas ‘Christian’ and ‘community’, occur more frequently than ‘brotherhood’: the two religious groups, ‘Christians’ and ‘Muslims’, are mentioned together twice as often as the ‘Muslim Brotherhood’, and the ‘community’ and ‘communities’ are used together with ‘Muslims’ almost 1.5 times as often as ‘Muslim Brotherhood’.
The most frequent word on the list with a clear negative connotation is ‘radical’, and there are only 11 instances in the 20-million-word corpus when ‘Muslim’ and ‘radical’ were used within 5 words from each other. The other negative words, such as ‘fatalism’, ‘extremism’ and ‘slave’ are even less frequent (at 10 uses each).
The comparison of the collocate lists from BTM and COCA shows that the group discussing the proposal to ban Muslims deviated from the all-American tone at the time of the speech. Not only were they (unsurprisingly given the topic of conversation) much more focused on Muslims and Islam, but they had a substantially more negative stance toward them, and much of the discussion revolved around the need to protect the American citizens from the perceived threat of the influx of Muslims. The collocates of these negative lemmas in BTM constitute 21.7 % of all the 3,595 collocations of the 50 most frequent lemmas, while in COCA they represent only 4.8% of the 1,017 collocations.
Looking at the collocates of ISLAM, one can see that it was used 1,236 times in the BTM corpus or 1,671 times per million. The following comprise the 50 most frequent collocates:
The list reflects the fact that the discussions refer to Islam as a totalitarian ideology that exerts full control over the followers and ‘requires complete submission’ to it in all spheres of life. Muslims are said to want it to be integrated in the ‘government’ of the countries they live in as the ‘law’. Islam is argued to be ‘radical’, disposed to waging a ‘war’ and ‘killing’, and is associated with ‘ISIS’. Its quintessence is the ‘sharia’ law that is ‘antithetical’ to ‘democracy’ and the American way of life. People following this religion need to be ‘banned’ and ‘prohibited’ from ‘entering’ the U.S.
ISLAM in COCA:
Unlike the lemma MUSLIMS, ISLAM in the COCA in 2015 displayed more negativity and rejection. There are more geographical names, such as Indonesia, Jerusalem, Pakistan or the words related to the various sections of Islam (‘Shia’ and ‘Sunni’), and references to Islam’s holy prophet (‘prophet’, ‘Muhammad’). However, the more numerous and frequent group bears a strong negative association with this religion: ‘radical’, ‘war’, ‘suicide’, ‘insulting’, ‘hate’, ‘terrorism’, ‘hatred’, and so on. They represent 41.6% of the 567 collocations of the 50 most frequent lemmas in COCA, while in BTM the negative collocations formed only 22% of the total 1,978 collocations with the lemma ISLAM.
KORAN/QURAN in BTM:
The Muslims’ holy book, according to the people discussing it on Trump’s FB page, is an instruction manual for ‘killing’ ‘non-Muslims’ or ‘infidels’, encouraging ‘jihad’, ‘war’, ‘murdering’ and ‘beheading’ the ‘enemies’, and spreading ‘rape’, ‘death’, and ‘violence’.
QURAN/KORAN in COCA:
Quran/Koran in COCA was mentioned much less frequently. Because the minimum collocation count to be included in this analysis was set at two, there are only 29 collocates that were found in COCA in the year 2015 with an MI of 3 or above. Many of them concentrate on the religious practices related to Islam’s Holy Scripture: ‘memorize’, ‘recite’, ‘read’, ‘exegesis’, as well as its features, such as ‘narration’. There is a mention of desecrating of a Quran and burning it or tearing a page out of it and the name of a notorious Christian leader, ‘Williams’, who stirred controversy with his plans to burn the Quran. Checking the concordances shows that the Quran is a victim of antagonism and hostility since there were proposals to ban it and attempts to desecrate it.
Overall, in BTM the negative collocations added to 17.7% of the total 904, and in COCA only 7.9% of the 76 collocations can be interpreted as presenting the Quran in a negative way, and such collocations as ‘desecrate’, ‘burning’, and ‘tear’ which constitute another 7.9% show the Quran as a victim of hatred and abuse.
SHARIA/SHARIAH in BTM:
In the BTM corpus, ‘Sharia’ is depicted as a scary attribute of Islam that all Muslims want to follow themselves and want to impose and force on everyone else. It is a set of barbaric laws that are not compatible with democracy and are aimed at establishing an Islamic ‘Caliphate’. It also supports ‘killings’, suppression of ‘women’, deals with ‘theft’, and is used by ‘radicals’ and ‘terrorists’.
SHARIA/SHARIAH in COCA:
The concept is much less salient for the general American public who produced the texts included in COCA 2015 collection. Because the minimum frequency of the collocation was set at 2, the collocation tool identified only 19 collocates to the lemma sharia/shariah:
Not only are the collocated terms significantly less frequent, they also do not share the negative tone characteristic for the BTM corpus in relation to Sharia. The COCA corpus basically notes that Sharia is a set of laws obeyed by the followers of Islam and leaves it at that. A word with a clear negative connotation is ‘censor’; the others might be used in a negative sense in a particular context but are not as clearly and universally agreed upon as negative, such as ‘kill’, for example.
Only 3.3% of the collocates of the lemma SHARIA in BTM are clearly negative out of the 726 collocations, though the tone of the concordances is quite negative. However, among the 87 collocations from COCA, only 2.3% are perceptibly negative.
REFUGEE in BTM:
For the participants of the discussion of whether Muslims should be ‘banned’ from the U.S., the refugees are the ‘Muslims’ from ‘Syria’ and very likely ‘terrorists’ associated with ‘ISIS’. It is difficult to properly ‘vet’ them and ensure that they are safe to ‘enter’ the United ‘States’. The immigration ‘processes’ are very important in preventing them from coming to this country, ‘illegal’ ‘immigration’ needs to be ‘halted’, and legal immigration needs to be ‘paused’.
REFUGEE in COCA:
Refugees in COCA do not bear the negative connotations they have in BTM. The discussions rotate around the geographical locations, the names of large refugee camps, the names of the international organizations assisting refugees and the officials of those organizations (e.g., Guterres is the United Nations High Commissioner for Refugees) in charge of such programs. Some social events such as birth or marriage or the particulars of the escape from one place to another, such as ‘arrive’, ‘resettle’, and similar are included. If there are words with negative connotation, such as ‘crisis’, they are not mentioned as the effects of the flow of refugees. On the contrary, they are the cause of the displacement.
Altogether, 10.6% out of the 452 collocations in BTM were negative, while none of the 365 collocations in COCA can be seen as showing a critical attitude toward refugees.
IMMIGRANT in BTM:
Immigrants in BTM are ‘immoral’, ‘unlawful’ people who cause problems, insult and hate Americans, and commit terror acts. They need to be ‘stopped’, ‘excluded’, ‘denied’ entry, ‘banned’, ‘deported’, and should never have been allowed in the U.S in the first place.
IMMIGRANT in COCA:
While the COCA corpus texts also associated immigrants with such problems as crossing the border, staying in the country illegally, crime, and killings, they are spread out over a broader semantic range and mention the countries of origin, while discussing other issues, such as smuggling, scholarships for students who are not legal immigrants, and advocates and activists helping them, in addition to mentioning the problems. In the BTM, 28.2% of the 651 collocations are negative, but in COCA the frequency of negative collocations is substantially lower and the percentage is 23.1%.
The discursive constructs of MUSLIM as presented in the responses to the proposal to ban Muslims from entering the U.S. externalize the xenophobic ideological base of the U.S. population whose opinions are voiced by then-presidential-candidate Trump. The topic of Muslim immigration provoked a heated discussion among Trump supporters, and the conversations exhibit a high level of animosity toward Muslims and concern for the safety of the USA. The examination of the frequency of the keywords allows us to describe the conversation as hateful and paranoid about Muslims.
The discourse of the BTM corpus presents Muslims as dangerous, predisposed to terrorism, and as people who ought to be kept away from the USA in order to protect the American citizens. The data obtained from the COCA corpus, which aggregates a large amount of text balanced between spoken, fiction, popular magazines, newspaper, and academic genres from the year 2015 does not show nearly as much negativity and rejection, with the exception of the lemma ISLAM. The general American discourse reflected in COCA demonstrates a lower level of animosity towards immigrants and refugees compared to BTM. On the contrary, discussions there often reflect concern for people forced to flee their home countries because of wars or unrest and contemplate means to help them.
The topics and attitudes prominent in the BTM corpus do not appear as salient in the COCA corpus. This may indicate that the views of Trump’s supporters, who use his FB page to express them, are not shared by the broader American public as reflected in the COCA texts. It is possible to argue that despite claiming to be a silent majority (Crowley 2016), the group whose opinions are revealed in the discussions on Trump’s FB page is, in fact, a vocal minority, and that their ideology does not represent the US mainstream. Even though racist, xenophobic, and prejudiced voices emboldened by the leadership of Trump have become even louder in the years of his presidency, Trump’s supporters are still a fraction of the U.S. population. Thus, their claims to be the “silent majority” might be illusory and caused by their tendency to consume news from select few outlets and to socialize with groups that are homogenous in terms of political opinion (Baysha 2020; Pariser 2011; Schwarz and Shani 2016; Stroud 2010). Such networking behavior gives users an illusion of agreement and dominance in terms of worldviews and ideology since they rarely encounter dissonant voices. Thus, it is crucially important for researchers to know the limitations of their data sources and to be judicious in drawing conclusions from them.
The author has no competing interests to declare.
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