Interjections and emojis in Nigerian online communication

World EnglishesVolume 40, Issue 4 p. 611-630 PAPEROpen Access Interjections and emojis in Nigerian online communication Mirka Honkanen, Corresponding Author Mirka Honkanen [email protected] University of Freiburg, Germany Correspondence Mirka Honkanen, Albert-Ludwigs-Universität Freiburg, Englisches Seminar, Rempartstraße 15, 79098 Freiburg im Breisgau, Germany. Email: [email protected]Search for more papers by this authorJulia Müller, Julia Müller University of Freiburg, GermanySearch for more papers by this author Mirka Honkanen, Corresponding Author Mirka Honkanen [email protected] University of Freiburg, Germany Correspondence Mirka Honkanen, Albert-Ludwigs-Universität Freiburg, Englisches Seminar, Rempartstraße 15, 79098 Freiburg im Breisgau, Germany. Email: [email protected]Search for more papers by this authorJulia Müller, Julia Müller University of Freiburg, GermanySearch for more papers by this author First published: 15 March 2021 https://doi.org/10.1111/weng.12544AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract This study investigates Nigerian and English interjections and emojis used for expressing surprise in Nigerian online communication. Interested in the factors influencing the choice between a shocked emoji and an English or Nigerian interjection, we apply automated emotion analysis and a language detection measurement we developed for an 840-million-word web forum corpus to test the influence of these and further variables on the choice of a surprise item. Our multinomial regression model suggests that the probability of Nigerian interjections is higher in messages containing Nigerian Pidgin, informal orthographically lengthened words, and positive emotions, while the shocked emoji occurs in negative contexts, in messages by established forum members, and alongside other emojis. The sad emoji, however, favors English-language interjections, as do high arousal and emotionally laden words. Moreover, we argue that interjections are not only a spoken phenomenon but occur in any type of communication characterized by interactivity and emotional involvement. 1 INTRODUCTION World Englishes have been shown to differ from British and American Englishes in terms of their discourse-pragmatic features. Englishes spoken in multilingual societies, such as Nigeria, have incorporated entirely new elements from indigenous local languages (Honkanen, forthcoming; Unuabonah & Daniel, 2020; Unuabonah & Oladipupo, 2018). This article focuses on a subclass of discourse-pragmatic features known as interjections and their usage in written Nigerian computer-mediated communication (CMC). We define interjections as linguistic elements that are not syntactically integrated but may stand on their own as utterances, and that do not contribute propositional or referential content but have emotive-cognitive functions. Interjections 'constitute a critically important mode of emotional expression'; however, studies on them in English linguistics are relatively scarce (Goddard, 2014, pp. 53–54). Our study investigates different interjections for expressing surprise and accompanying emotions in Nigerian CMC. The data come from a popular, informal, multilingual web forum frequented by Nigerians worldwide. Such platforms are characterized by high interactivity – a back-and-forth of (re-)actions between individuals and between messages. This study focuses on emotive reactions involving surprise. There is debate about whether surprise should be seen as an emotion or a reflex, and as primarily biological or social (Wilkinson & Kitzinger, 2006, pp. 150–151), but as a minimal definition, it can be described as a reaction to something unexpected. In expressing surprise, Nigerian web users have the choice between English elements (such as wow or oh my god), indigenous elements from Nigerian languages (such as chei or haba), and visual elements (the shocked emoji ). Interjections rarely have straightforward semantic equivalents cross-linguistically (Goddard, 2014, p. 59). Wierzbicka (1992) suggests they are 'among most characteristic peculiarities of individual cultures' (p. 160). Interested in the factors that influence the choice between English and Nigerian interjections of surprise, we apply automated emotion analysis and language detection to a large CMC corpus to test the influence of these and further variables on the choice of interjections. Other predictors include emotional arousal (gauged through the use of non-standardized punctuation and capitalization), textual factors such as message length, and user status in the community (estimated from the user's number of contributions). We restrict our investigation to elements in the message-initial position to ensure that most of them will be reactions to previous messages. This paper addresses the following research question: which factors influence the choice of an expression of surprise as either an English-language interjection, a Nigerian interjection, or a shocked emoji in Nigerian CMC? A particular challenge in our study comes from the non-standardness of our data. We investigate informal, multilingual interactions combining Nigerian English, Nigerian Pidgin, ethnic languages, non-standardized punctuation and capitalization, emojis, slang, and so on. We address some of the difficulties in applying language detection and emotion analysis to pidgin-language data. The great advantage of written CMC data is how they grant access to an unusually large number of interjection types and tokens. While some scholars have suggested interjections to be a fundamentally spoken phenomenon (Goddard, 2014, p. 56; Stange, 2016, p. 1), our data show that under the right conditions, they occur amply in writing as well. 2 BACKGROUND Nigerian English contextualized English was introduced in Nigeria by Christian missionaries, traders, and colonists from Britain, and it was made the language of education, politics, and other official contexts in the 19th century (Jowitt, 2019, pp. 7–8). Estimates of the number of English speakers have varied dramatically, and Jowitt now questions whether it is still justified to call English an 'elitist' language in Nigeria (2019, pp. 8–10). For most Nigerians, however, English is a second language, while one of the hundreds of indigenous languages serves as the mother tongue. The three most widely spoken ethnic languages are Hausa, Yoruba, and Igbo. A further important piece in the Nigerian linguistic puzzle is the English-lexifier contact language Nigerian Pidgin (NigP), which is the most widely spoken language in the country (Faraclas, 2013, p. 177). NigP enjoys no political support or recognition, but it is widely used in informal, interethnic encounters, and regionally, increasingly even learned as a first language (Faraclas, 2013, p. 177). One context where NigP has been gaining ground is social media, where it becomes commodified, stylized, and deterritorialized (Heyd, 2016, pp. 105–106). The first treatments of Nigerian English (NigE) discussed its lexical, grammatical, phonological, and suprasegmental features (Bámgbóṣé, 1982; Kujore, 1985), thus helping to legitimize this postcolonial variety of English. Current world Englishes scholars continue to work on NigE morphosyntax (Alo & Mesthrie, 2008; Jowitt, 2019; Taiwo, 2020), phonology and prosody (Gut, 2008; Jowitt, 2019), and lexicon (Blench, 2005; Kperogi, 2015). However, recently, there has been an increasing interest in describing the discourse-pragmatic peculiarities of English usage in Nigeria as well. Some of this research addresses classic topics of pragmatics, such as politeness or speech acts, for example on the internet (Chiluwa et al., 2014), while others investigate specific discourse-pragmatic features. This study contributes to the latter emerging tradition. Previous work on discourse phenomena in NigE has compared the frequencies and distribution of discourse markers across contexts to British English (Gut & Unuabonah, 2019; Unuabonah, 2019; Unuabonah & Gut, 2018), described the extended functions of English elements (Adegbija & Bello, 2001; Fuchs et al., 2013; Ogoanah, 2011), and explored the usage of indigenous discourse-pragmatic elements (Honkanen, forthcoming; Unuabonah & Daniel, 2020; Unuabonah & Oladipupo, 2018). Most studies are based on the Nigerian component of the International Corpus of English (Greenbaum, 1996; Gut, 2012), which allows comparisons across various spoken and written genres, and across varieties. However, the size of the corpora in the ICE family – 1 million words each – might not suffice for investigating less frequent lexico-pragmatic usages. The present study focuses on one subclass of discourse-pragmatic features – interjections. Interjections Interjections have always been seen as formally marginal linguistic items (Ameka, 1992, p. 112) because of their phonological aberration and syntactic independence. However, they have important functions in 'express[ing] a speaker's mental state, action or attitude or reaction to a situation' (Ameka, 1992, p. 106), and they have begun to receive more attention from linguists as well. Defining them has proven difficult, however, and several scholars have suggested interjections should be treated as a category with a prototype structure of central and marginal members (Aijmer, 2004, pp. 101–102; Gehweiler, 2010, p. 315). We take a syntactic and pragmatic view when we define interjections as elements that may constitute utterances by themselves and that express speakers' emotive or cognitive states (Goddard, 2014, p. 54). The term interjection has been used both as a word-class label and as a functional description, while some prefer exclamation for the latter (Ameka, 1992, pp. 102, 108). Considering how interjections are 'spontaneous, emotional reactions to a situation or to a sudden realisation after internal reflection' (Gehweiler, 2010, p. 316), it makes sense that they are frequently used to express surprise. According to Norrick (2009), interjections often 'signal both surprise and either positive or negative emotional involvement' (p. 867). This shows their functional 'overlap' of signaling both cognition – changes in information states – and emotion (Goddard, 2014, p. 54). Surprise in CMC Surprise is the cognitive-emotional reaction to a situation that departs from one's expectations. The cognitive part is the reception of new, unexpected information. The emotive 'valence' of surprise can be positive, negative, or neutral (Ascone, 2015, p. 405). Examining how Italian learners of English express surprise on Facebook, Ascone (2015, p. 384) sees surprise as very short-lived and involving a momentary loss of control. However, she does not consider that this does not apply similarly to text-based communication, where one has more time to devise one's response. CMC writers do express surprise as well, but this should be seen as a conscious choice rather than an automatic reaction. In fact, Wilkinson and Kitzinger (2006) argue that even in face-to-face conversation, 'expressions of surprise […] are not involuntary spontaneous emotional eruptions but interactionally organized performances' (p. 152). This study explores six Nigerian interjections that convey surprise, comparing them to 20 English-language alternatives and the shocked emoji, all introduced below. It is not possible to list or automatically detect all ways of expressing surprise because it relies not only on lexical means but also syntactic constructions (Krawczak & Glynn, 2017). Even the class of interjections, focused on here as a central device for conveying surprise, is 'open-ended' (Norrick, 2009, p. 876). Our study includes the most prominent items mentioned in previous studies (Goddard, 2014; Unuabonah & Daniel, 2020). While the ICE corpora are too small for investigating interjections in world Englishes quantitatively, data sets curated from informal online interactions show more promise. In the most significant predecessor to this study, Unuabonah and Daniel (2020) utilize the Corpus of Global Web-based English (GloWbE Corpus; Davies, 2013) to examine the functions, orthography, sentential position, and collocations of five Nigerian interjections: haba, kai, chei, chai, and mtchew. The first four are included in this study too, as their basic meaning is to express surprise (Unuabonah & Daniel, 2020, pp. 69–71). Studies on interjections have tended to be qualitative and focus on functions; this holds true even for research based on large CMC data sets which would permit quantification, such as Unuabonah and Daniel (2020), or Lockyer's (2014) Twitter study. Our study departs from this tradition, striving to detect preference patterns based on a data set much larger than what could be investigated qualitatively. 3 DATA AND METHODOLOGY The Nairaland corpus The study explores interjections and surprise in written Nigerian CMC. The data stem from the web forum Nairaland,1 which is a popular, non-thematic social media platform aimed at Nigerians in Nigeria and the global diaspora. It represents pseudonymous, asynchronous communication that is mainly text-based but includes visual elements such as emojis, memes, and photographs. NigE is the dominant language, but NigP occurs amply; see Honkanen (2020, pp. 48–52) for a detailed account of the forum. The data were downloaded by computer scientist Daniel Alcón López at the University of Freiburg (Mair & Pfänder, 2013); the legality of the undertaking was verified before starting the collection process. The data consist of 843.1 million words (19.2 million messages) of unedited, topically diverse, linguistically non-standardized written interactions between over 300,000 forum members from 2005–2014. Our focus on large patterns rather than individual messages or users mitigates the ethical concerns related to using public CMC data for research. The corpus is not publicly available, but interested fellow scholars are encouraged to contact the authors. In this project, we use the programming language Python (Python Software Foundation) to extract the relevant observations and calculate the chosen measures. The non-standardness of the data poses challenges to corpus linguistic methods. One of these is orthographic variation, due to several related causes. CMC users manipulate spelling to convey prosodic information. NigP is a primarily oral language without steady orthographic norms. Interjections are a mainly oral phenomenon, and '[c]onventional spelling is not […] always adequate to represent the[ir] pronunciation' (Goddard, 2014, p. 54). In order to account for this irregularity, we employ regular expressions in detecting interjections and NigP elements. Dependent variable: Interjections of surprise Goddard (2014) chooses to disregard CMC corpora when he states that '[m]ost standard corpora are heavily weighted towards written texts, where interjections are rare except in reported […] speech' (p. 56). In our clearly non-standard corpus, the use of interjections is a common practice: the investigated items occur over 250,000 times, and we only consider the message-initial position. An item is included in our sample only when it occurs among the first five words of a message. This is to ensure a focus on these interjections as reactions to previous messages rather than as other instances of emotional expression. Studies from different subfields of linguistics have 'given prominence to the starting points of utterances' (Norrick, 2009, p. 870). In Ascone's (2015, pp. 394–395) English and Italian Facebook data, 69 out of 80 instances of expressing surprise begin with a 'reaction' element, while only five utterances contain a reaction in some other position. Unuabonah and Daniel (2020, pp. 69–71) discover that interjections chai, haba, and kai each prefer a 'prospective' position, preceding a comment clause. The two-to-four-word buffer allows for the common practice of chaining several interjections (see examples (1–2), and (6) below), as well as, for example, message-initial vocatives or laughter (examples (4–5)). Tables 1 and 2 provide an overview of the investigated items. We systematically consider multiplication of letters in the regular expression used for extraction; other spelling variants are listed. Interjections of surprise include both 'primary interjections' not related to existing words (wow, oh, chai) and 'secondary interjections' based on lexical words (good lord, no way) (Ameka, 1992, p. 105). Some are multi-word interjectional phrases, often with oh as the first part. Many English-language interjections stem euphemistically from religious vocabulary. The Nigerian items are either primary interjections from local languages, now used across ethnic boundaries, or NigP innovations. The fact that interjections are 'usually followed by an exclamation mark' in writing (Stange, 2016, p. 6) is taken advantage of to improve the search precision: some secondary interjections are only included when immediately followed by an exclamation or question mark (see Table 2). For example, good lord and Jesus Christ occur in Nigerian discourse as referential noun phrases as well, and oh has further functions, such as marking reception of a previous message (Heritage, 1984). Each of the English interjections is identified in the Oxford English Dictionary (OED Online, 2020a) as being used as an exclamation of surprise. Some potential further items occasionally used to express surprise were excluded. For example, the class of expletives – represented here by the many religious euphemisms – could be extended (Gehweiler, 2010, pp. 321–323). However, items such as oh shit seem to express primarily feelings other than surprise (such as anger, frustration, or disappointment) and are not very common on Nairaland (oh shit appears message-initially 149 times). The third and final type of item considered in addition to Nigerian and English interjections is the animated shocked emoji . It is one of the 14 facial emojis available on Nairaland and used amply by its members. Through the HTML version of the forum, we could extract instances of this pictorial representation of surprise as well. Other visual means of expressing surprise, such as with memes, are not considered in this study. Examples (1–6) show some of these elements being used to signal surprised, emotional reactions. The relevant items are underlined and NigP words translated in all examples. TABLE 1. Nigerian interjections of surprise Interjection Frequency Etymology Comments chai, chei, choi 27,835 Igbo 'surprise, pain and shock', 'no dialectal difference' between chai and chei in Igbo (Unuabonah & Daniel, 2020, p. 71) haba(h) 14,121 Hausa 'strong surprise linked with disbelief', 'strong negative emotions' (Unuabonah & Daniel, 2020, pp. 69–70) kai 10,156 Hausa 'strong surprise', 'pity, sadness and emotional pain' (Unuabonah & Daniel, 2020, p. 70) na(h) w(h)a(h) (o) 38,650 from Hausa na wahala 'that is trouble' to NigP (Unuabonah, 2020, p. 171) 'expression of surprise' (naija lingo, 2020a) o(l) boy/boi, oh boi 13,156 NigP spelling distinguishes these from English oh boy shu(o), sho(u) 1,839 NigP from Waffi and Sapele (Oribhabor, 2010) surprise – disgusted, amazed, or excited (naija lingo, 2020b) TABLE 2. English-language interjections of surprise Interjection Frequency Comments blimey 218 British English, from (God) blind/blame me (OED Online, 2020b) geez(e), jeez(e) 5,196 from Jesus (OED Online, 2020d); gee excluded because it occurs as part of two popular Nairalanders' usernames (by) golly 29 from God (OED Online, 2020f) good(ness) gracious 121 euphemism for God (OED Online, 2020g) (oh) my goodness 1,182 euphemism for God (OED Online, 2020g) good grief 77 good lord! 93 Jesus Christ! 315 no way! 715 oh! 14,999 different 'kind[s] of change[s] of state' (Heritage, 1984, p. 324); 'core meaning "surprise"' (Aijmer, 2004, p. 105) oh dear/dia 1,615 oh boy 2,932 omg, oh (my) god/gawd/gosh, (om)gosh, omgawd 23,184 abbreviated or mincing variants of oh my god (OED Online, 2020e) oh my! 733 oh my word 101 oh no 2,702 really? 9,894 wha(t)! 2,229 'guess what!', 'so what!', 'no matter what!', 'you know what!' excluded w(h)oa(h) 2,464 variant of interjection ho (OED Online, 2020h) wow 50,268 (1) HABA! O boy! [shocked emoji] Just like that?! [confused emojis] (2) O boy, Geeeeeeezzzzzzz, see how them kill Nigeria [shocked emoji] [URL] (3) shuooooooooooooo na wetin dey happen here now 'what's happening here now' (4) @Post Unbelievable! Chai! I am so ashamed (5) LOL!!! Wow, so many rumors flying around. (6) Oh my goodness my gosh! What are those?! Predictors in the multinomial regression model In our multinomial regression model, we attempt to predict the choice of the element of surprise as either an English-language interjection, a Nigerian interjection, or a shocked emoji by considering the following factors: language choice of the message, the sentiments expressed, other emojis used in it, its arousal score, the status of the member as central or peripheral on the forum, and the length and average word length of the message. Language choice is represented by the Pidgin Index (PI), a measurement developed for this study. It makes a distinction between NigP and other languages, the latter category containing English and a low number of messages in ethnic languages. The PI is the ratio of NigP words to all words in a message. Implementing classic n-gram-based language detection (Cavnar & Trenkle, 1994) – using frequencies of character strings in training data to assign a text to a language – to measure the presence of NigP would be problematic. Our two languages of interest share large parts of their lexicon, and hence n-grams, too. Finding and selecting monolingual training data is difficult for languages that lack an orthographic canon. On Nairaland, '[e]ven the most competent users do not seem to aim at writing maximally pure NigP stripped of any influence from standardized English' (Honkanen, 2020, p. 241). These data are multilingual at the text-level and assigning each message to a single language would misrepresent them. These factors complicate language identification (Řehůřek & Kolkus, 2009, pp. 359–360). Hence, we opted for a variant of the word-based 'dictionary method' (Řehůřek & Kolkus, 2009, pp. 360–361). In lieu of extracting distinctive Pidgin words from a corpus, we employ manually collected lists of words and multi-word units, compiled relying on the two most comprehensive online dictionaries of NigP (Babawilly, 2000; naija lingo, 2020c) and our familiarity with NigP usage on Nairaland. We created one list of over 300 unambiguously Pidgin lexical and grammatical words and phrases (wey, comot, sidon, carry go, kill me die) and another one of over 200 'bivalent' words (Woolard, 1999) that are very common in Pidgin but also occur in English (im, make, dat, must). Three words preceding and following any bivalent word are checked, and if they include any clearly Pidgin words, the ambiguous item is coded as Pidgin. The PI, a figure between 0 and 1, gives the portion of words identified as Pidgin. Naturally, we expect messages containing NigP to favor Nigerian interjections. One interesting characteristic of surprise is that it can have either a positive or negative valence. We investigate whether negative or positive sentiments in the message favor the use of indigenous or English interjections. For emotion scores, we make use of the NRC Word-Emotion Association Lexicon (Mohammad & Turney, 2013) that contains crowd-sourced annotations for 14,182 English words. In addition to a generally positive or negative emotional value, lexemes are coded for eight emotions: joy, sadness, anger, fear, trust, disgust, surprise, and anticipation. We extended this lexicon by adding synonyms from the WordNet® database (Princeton University, 2010) implemented in the Natural Language Toolkit (Bird et al., 2009), as well as NigP words, informal items such as CMC abbreviations, and some common words missing from the original dictionary (such as nice, great, thank, available, wish, best). Some entries needed adjustment to fit the Nigerian cultural context; black is not negative, nor is colonial neutral. Thus, the lexicon was extended by 582 word forms. Each message was lemmatized using the TextBlob library (Loria, 2020) available for Python, prior to its contents being checked against the NRC lexicon. We improve the accuracy of these word-based scores by taking the sentential context into consideration. Checking three words to the left and to the right, we look for valence shifters, intensifiers, downtoners, and adversative conjunctions in a procedure similar to Rinker's (2015) sentimentr package. We use the same categories and word lists but complement them with non-standard items; see Table 3. TABLE 3. Modifications to the emotion scores Category Examples Effect on score valence shifters can't, neva, nothing, etc. sign reversed intensifiers completely, a lot, 4 sure, etc. score doubled downtoners almost, kinda, sort of, etc. score halved adversative conjunctions despite, altho, whereas, etc. score halved Finally, the scores were normalized by the number of words per message contained in the NRC lexicon. Additionally, the ratio of emotionally loaded to neutral words per message was recorded. The positive and negative emotions across a message are combined into an overall sentiment score; the process is described in Section 3.4. In addition to surprise, Unuabonah and Daniel find the ethnic interjections haba, kai, chai, and chei to 'express strong negative emotions' (2020, pp. 70, 75); the same applies to NigP na wa (Unuabonah, 2020, p. 158). Of the English interjections, only oh no is clearly negative. Hence, our hypothesis is that messages with negative sentiments favor Nigerian exclamations. However, the other two Pidgin interjections, o'boy and shuo, have not been investigated in previous studies, and their influence is yet unknown. According to Ascone (2015), emoticons and punctuation 'play a specific role in the computer-mediated expression of surprise and, in particular, in the expression of surprise valence and intensity' (p. 385). Our analysis takes these into consideration. Nairaland supports 14 unique facial emojis, which are used frequently, in nearly half (47.3%) of the analyzed posts, and were coded for the abovementioned emotions by us. The presence of these emojis in the same message is considered as a further factor to predict the choice of interjection. The sentiment score and the emojis gauge the evaluation of surprise as positive or negative. In order to measure its intensity, we assigned an arousal score to each message based on the number of exclamation and question marks and words written entirely in capital letters (excluding single-character words such as I) (Pandey, 2018). Specifically, the sum of capitalized words and question and exclamation marks was divided by the sum of all words and instances of sentence-final punctuation, resulting in an arousal score between 0 and 1. In the sociolinguistics of CMC, the researcher rarely has access to full sociodemographic information about individuals. We know that most Nairalanders are young Nigerians, predominantly male, Nigeria-based, and not from the lowest socioeconomic stratum, but we have no reliable, comprehensive data at the individual level. Instead of such traditional sociolinguistic variables, Androutsopoulos (2013) suggests considering 'environment-specific categories such as regulars/novices or admin[istrator]s/normal users' (p. 245). We include, as a predictor, the status of the user in the online community, measured by their total number of forum contributions. We hypothesize that individuals not familiar with Nigerian languages, such as diasporic or non-Nigerians, contribute fewer messages and use English-language interjections, and that members use more Nigerian interjections with increasing familiarity with the forum norms. Finally, the number of words and average word length in the message are included as measures. Writing on Nairaland is highly heterogeneous, ranging from casual and interactive to carefully constructed argumentation. Longer messages might be less dialogic, more formal, and develop more complex arguments. The messages with the highest average word length, however, are in fact short messages containing extreme orthographic manipulation, such as multiplied letters or lacking whitespace characters (see (7)); this is typical of informal, interactive, and emotional writing. (7) Lollllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll omg! my belle ooooooooooooooooooooo 'my stomach' Data cleaning and statistical modeling We cleaned the data and performed our statistical analyses in R (R Core Team, 2019) with the help of the tidyverse package (Wickham et al., 2019). Our extraction script found 343,194 instances of the investigated elements (see Table 1 and Table 2) in message-initial position. We excluded multi-posted advertisement spam such as (8) below; this reduced the data set by 5.7 per cent, leaving us with 323,745 relevant lines. (8) WOW! YOU CAN ADVERTISE YOUR CAR INVENTORY TO MILLIONS LOCAL BUYERS IN