[Online] Social Self-Translation: the Real, the Illusory, and the Hyperreal

Translation Studies

ISSN: 1478-1700 (Print) 1751-2921 (Online) Journal homepage: https://www.tandfonline.com/loi/rtrs20

A preliminary theoretical investigation into [online] social self-translation: The real, the illusory, and the hyperreal

Renée Desjardins

To cite this article: Renée Desjardins (2019): A preliminary theoretical investigation into [online] social self-translation: The real, the illusory, and the hyperreal, Translation Studies, DOI: 10.1080/14781700.2019.1691048 To link to this article: https://doi.org/10.1080/14781700.2019.1691048

Published online: 09 Dec 2019.

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A preliminary theoretical investigation into [online] social self-translation: The real, the illusory, and the hyperreal Renée Desjardins School of Translation, Université de Saint-Boniface, Winnipeg, Canada

ABSTRACT KEYWORDS This article argues that “social translation”, “crowdsourced Translation; self-translation; translation”, and “user-generated translation” are in fact not social translation; social synonymous. Building on previous research, the term “social media; Instagram; Facebook translation” is used to refer to translation activity that takes place on various online social media and that engenders specific online social affordances. While crowdsourced translation in online settings continues to garner interest from translation scholars, very little has been said on the subject of self-translation in online and digital contexts, specifically with regard to social media. This article begins filling this gap by first defining [online] social self- translation and providing a taxonomy of different types of self- translation under this umbrella term. Examples are offered to illustrate the categories “real”, “illusory”, and “hyperreal”. Theoretically examining social self-translation sheds light on how self-translation phenomena occur online and how such activity can help translation studies scholars rethink the “self”, the “social” and, thus, self-translation and social translation.

Introduction To contextualize this article’s preliminary investigation, it is useful to consider the historical positioning of self-translation research. Although historically self-translation and self- translators have been marginalized in translation studies (TS) scholarship (e.g. Cordingley 2013; Grutman and Van Bolderen 2014; Hokenson and Munson 2007), this area has now garnered increased attention (e.g. Castro, Mainer, and Page 2017; Falceri, Gentes, and Manterola 2017). Some scholars, such as Anselmi (2012) have even advocated in favour of “self-translation studies”. Earlier research in this area usually favoured a comparative analysis of source and target texts, not dissimilar to early analyses in TS that focused on linguistic aspects of translated “products” to the detriment of other key elements, such as the context of production (translation), the identity or proﬁle of the translator, the translator’s agency during/within the translation process, and the translation brief. And while some “high-proﬁle” self-translators – e.g. Samuel Beckett, Nancy Huston, and Vladimir Nabokov – have garnered the attention of literary scholars and TS scholars alike, the argument has been made that this focus on established self-translators has obfus- cated an entire demographic of self-translators, i.e. those who, as Anthony Cordingley

CONTACT Renée Desjardins [email protected] © 2019 Informa UK Limited, trading as Taylor & Francis Group 2 R. DESJARDINS states, “translate” themselves every day, by choice or necessity (2013, 4), be it at family gatherings, in online settings, at work, etc. These individuals may not even consider themselves self-translators at all: for them, recourse to self-translation is an inherent part of being able to communicate with and within different social networks (both offline and online) in everyday contexts. This group of individuals constitute the key demographic in the observational analyses provided here: namely, those who may not view or define themselves as self-translators, but nonetheless leverage the practice on social media to communicate with their peer and professional networks. Cordingley identifies four main currents of self-translation research, which he also uses as section headers in Self-Translation (2013). These are studies that: (1) examine self- translation and literary history; (2) incorporate interdisciplinary perspectives (sociological, psychoanalytical, and philosophical insights in this specific case); (3) have evolved from the “cultural turn”, and draw from post-colonial perspectives; and, finally, (4) consider cosmopolitan identities/texts. This corpus of self-translation research is novel in that it departs from debates fixated on fidelity to an “original”, the creation of new “originals”, and author primacy. However, none of Self-Translation’s contributors tackle social self- translation in any explicit manner,1 which is surprising given that multilingual communication between online users is an inherent characteristic of online and digital spaces. Therefore, this preliminary investigation builds on interdisciplinary perspectives (translation studies and social media studies) and provides analyses that also examine different self-translation “profiles” on different social media platforms, which in turn sup- plements scholarship on self-translator profiles and cosmopolitan identities/texts. Other early and contemporary definitions of self-translation alike define self-translation as a “literary phenomenon” (Gentes and Van Bolderen 2018, n.p.), focusing more or less exclusively on literary works authored by and translated into another (human) language by the same individual, referencing, among others, Anton Popovič’s inaugural 1976 definition (which I explore in greater detail in a later section). Gentes and Van Bol- deren (2018, n.p.) argue that, “[a]s a literary phenomenon, self-translation […] involves an author translating their own literary work into another language and another text”, and that this definition, while not uncontested (Cordingley 2013), “remains the most common”. Chiara Montini (2010, 307) provides the following overview of the self-translation landscape in TS:

[M]ost of the articles or monographies on the subject concern the following: a single (or a few) author(s) such as Nabokov, Beckett or Julien Green; post-colonial studies […]; some writers issued from a linguistic minority […]; exiled or migrated subjects […]; a personal account of the self-translator’s experience; self-translation from dialects […]. In my work (Desjardins 2013, 2017a, 2019), I examine translation and social media, noting the relative absence of research that critically examines self-translation activity on online social media (OSM) platforms (Desjardins 2017a, 123). Furthermore, and particularly in connection with this issue’s focal theme, recent literature on crowdsourced, collaborative, and user-generated translation in online and digital settings (going beyond online social media) (e.g. Costales 2013; Cronin 2010; 2013; García 2010; 2015; McDonough Dolmaya 2011) has made no mention of self-translation as something many online content creators practice regularly, especially professional translators (e.g. self-translating a professional profile page on LinkedIn, producing translated TRANSLATION STUDIES 3 content for a website for their freelance practice, or producing self-translated bilingual content in a blog or academic profile page). This brief summary of extant literature indicates that self-translation has not, or at least not to a significant degree, been examined in online contexts or on online social media. However, self-translation, as a practice, product, and concept, has notable significance in digital and online contexts. According to 2017 data, multilingual online users seem to pivot to English2 (or other major internet languages, such as Chinese) to communicate messages on various social platforms and elsewhere in the digital world: the translation practices and products related to this phenomenon warrant investigation. However, by extension, the concept of self-translation also has relevance for various online social media practices (is there no better form of self-translation than the selfie? [Koustas 2018]). As part of the project of redefining or broadening self-translation within TS, and of expanding upon work done in Social Media Studies, the various theoretical overlaps between the concept of self-translation and online expressions of the self, such as selfies, blogging, content curation, I argue, should be examined. This exploratory work identifies points of convergence between the fields of TS and Social Media Studies (SMS), as previously suggested. Further, investigating how self-translation occurs on social media can shed new light on how user-generated content is produced, constructed, articulated, and disseminated on social platforms (Desjardins 2017b). It should be noted that the intention is not to examine the products of self-translation published online or on social platforms (akin to linguistic analyses), but to consider the various social affordances and different forms of sociality social media provide self-translators. The taxonomy is meant to facilitate reconceptualizations of self-translation in light of new(er) online phenomena on social platforms (i.e. evolving machine translation tools; new job profiles specific to the digital and gig economies). The goal, then, here is to begin a theoretical investigation of social self-translation practices, by first defining “[online]3 social self-translation” and by providing some related descriptive examples. I argue that [online] social self-translation can be divided into three categories: (1) the real, (2) the illusory, and (3) the hyperreal. Although this taxonomy is provisional, it may help those interested in online phenomena and self-translation to better classify their data and, in turn, more readily classify their findings. This provisional taxonomy could also serve as a basis for more refined iterations in future research. The goal of this research is not to present comprehensive and wide-ranging empirical data (though this is a longer-term objective), but rather to define and illustrate theoretical concepts that can then provide scaffolding for future empirical research. Table 1 provides an overview of the taxonomy4, while the next section provides more comprehensive expla- nations for each type of social self-translation proposed here.

Defining the terms Human-generated (online) social self-translation: The real Idefine “real” social self-translation as the practice of individuals translating their online social content, i.e. content they have authored (e.g. a Facebook post; a Twitter tweet; a bio note on Instagram; a Tinder profile), into another language by themselves rather than relying on another individual (or team of individuals) or entity (e.g. software, application) 4 R. DESJARDINS

Table 1. Social self-translation taxonomy. SOCIAL SELF-TRANSLATION TYPE MAIN FEATURES The Real . Human-based activity on social media (Figure 1) . Writer and translator are the same individual . No collaborative, crowdsourced, or hyperreal underlying structure The Illusory . Human-based activity on social media with machine intervention (Figures 2 and 3) . Machine intervention either intentional or unintentional (i.e. enacted by user or enacted by platform) . Gives illusion of self-translation to other users, especially in cases where platform prompts are not overt or disclosed to other users. . Collaboration with other users may be possible in the production of the self-translation, but this is not disclosed (empirically difficult to prove without interviewing subjects) The Hyperreal . Distinct from collaborative or crowdsourced translation in that it is premised upon a (Figures 4–7) vertical structure (production of self-translation supports one individual, e.g. an influencer, or one entity, e.g. a brand; the assisting team/individual is generally self- effacing or when explicitly referenced done so in relation to individual or brand); does not purport to “problem-solve”; is not inherently participatory (in the sense of a horizontal structure). . Self-translation is a human-produced simulation and co-production . Use of machine translation is unlikely

to do so. Said differently, real social self-translation is underpinned by human activity and intervention: specifically, it is self-translation by one and the same self. This definition echoes the one offered by Popovič (1976, 19) –“the translation of an original work into another language by the author himself”–and nearly every subsequent definition of the concept, notably thanks to Rainier Grutman’s encyclopedia entries (1998; 2009; 2019). However, three distinctions are worth noting: (1) Even while the context of literary translation is easily understood (given that the definition was published in A Dictionary for the Analysis of Literary Translation), Popovič (1976) makes no mention of the medium or media in which self-translation occurs. This is significant as digital technologies and online contexts have markedly changed how communication takes place. For instance, whereas print publication is affected by publication costs and market supply and demand, most online contexts provide spaces for users to produce content relatively inex- pensively5 and without the same literary market constraints. This gives online users the ability to self-translate with fewer traditional constraints. (2) I have deliberately omitted the word “original”, as this term has been readily contested in contemporary TS scholarship (e.g. Reynolds 2016, 59). Further, I would argue that the concept of virality, or “going viral” (e.g. Goel et al. 2015), which calls into question a definitive point of origin, is a more relevant way to investigate online translation phenomena. Mapping viral trends can provide insight as to how content “migrates” in spaces, such as social media platforms, where borders and the notion of nations behave very differently than they do offline. The concept of “original” is further problematic in online settings when we consider the ephemeral nature of user-generated content: users have the ability to edit, delete, filter, etc., as they please and can also re-appropriate content in a number of ways that defy original authorship, and that, for the most part, do so much more conspicuously than in offline literary translation. (3) The descriptor “social” (Fuchs 2017,6–8) is also significant. Shah (2015, 1) argues that the very essence of today’s media lies in its social affordances: TRANSLATION STUDIES 5

From understanding media as a “medium”–a presence that allows for things to pass through, to media as a connector, building specific interactions between identified entities, to media as a network, enabling new modes and nodes of circulation and distribution through transfer of traffic – media has always been imagined and conceived of as social. And yet, there seems to be a way by which new media and digital media reinforce sociality as something that is new and unique to our mediated lives and relationships. While I recognize that digital media and social platforms are not inherently novel in affording humans ways to socialize, the scale and the speed at which humans can communicate via these media are relatively unprecedented (Standage 2013). Although “social translation” may recall “crowdsourced translation” or “collaborative translation” (O’Brien 2011 and Perrino 2009), I take a different position here. “Social” refers to the social affordances of online and digital contexts that do not necessarily overlap with crowd-based activities or phenomena or with collaborative translation activities per se.6 For instance, if a user posts a translated status update on Facebook, this is a form of social action on a social platform (meant for varying degrees of user engagement). That said, this user did not necessarily collaborate with others in the writing, editing, translating, etc. of this status update. As such, there is nothing inherently “crowdsourced” or “collaborative” in this type of social (translation) activity. There seems to be a trend in TS of conflating crowdsourced translation, collaborative translation, social translation, and user-generated translation. The terms do overlap to some degree, but researchers and practitioners should be mindful of the nuances between each type of translational activity. Returning to the previous example, one could say that a user translating their Facebook status update is ostensibly par- ticipating in both social and user-generated translation (also referred to as UGT; Perrino 2009): “social” in terms of the context of the social platform (i.e. Facebook) and in terms of communicating to/with others within a social ecosystem (e.g. Fuchs 2017;Standage 2013), but also “user-generated” in that the user is generating the content/translation. However, in this particular type social self-translation, there is nothing collaborative or crowdsourced in the writing of the post or in the process of translating the post (one user; one self). As such, crowdsourcing or collaborative frameworks have little applicability; however, the relevance of crowdsourcing and collaborative translation will be re-examined in the third part of the taxonomy (i.e. “the hyperreal”). This example also demonstrates how concepts like social self-translation, crowdsourced translation, and collaborative translation are distinct and why conceptual taxonomies are required to understand and study online translation and self-translation phenomena. I add the term “real” to social self-translation to describe instances of self-translation that are human-generated/self-generated. While “user” is an applicable term, the challenge lies in distinguishing between human users/translators and algorithmic or machine “users” (e.g. bots, artificial intelligence). I could have opted for the term “human”, but did not feel human functioned as effectively from a theoretical standpoint and would have raised other philosophical issues on what it means to be human.7

Automatic machine translation and self-translation on social platforms: The illusory Over the years, different social platforms have integrated automatic machine translation tools to facilitate multilingual user engagement. In 2011, Facebook recognized users’ 6 R. DESJARDINS interest in translated content and so the social giant partnered with Microsoft’s Bing to provide automated translations of user-generated content (Constine 2011, 2016).8 From 2011 to 2015, Facebook continued to refine its own machine learning translation system, which leveraged crowd input to improve its overall translation output. Then, in December 2015, the platform shifted away from Bing, relying on its own automatic machine translation system to automate and provide translations for its user base (ibid.). How different social platforms present translated content to users seems to vary depending on users’ linguistic profiles, geolocation, and the languages in which they regularly post and interact with other users. In other words, the rollout of systems can be inconsistent among users; in Facebook’s case, however, this is unsurprising given that the platform has regularly tested features on specific test markets, demographics, or user sets9 (and, historically, Facebook has rarely disclosed this type of testing publicly from the outset10). Similarly, Instagram also offers automatic machine translation to its user base. Insta- gram’s automated translation feature was launched in 2016 and facilitates the translation of (photo) captions and the translation of user comments (separate from the caption11,12). Twitter, too, leveraged Microsoft’s Bing system to provide its user base with tweet translations (Shu 2015). However, unlike Facebook’s system, which has sometimes provided translated content unbeknownst to users, Instagram and Twitter have cue buttons (e.g. “See Translation” and “See Original”) that feature more prominently, thereby “alerting” the user in more obvious ways to machine involvement. In situations where embedded machine translation is not immediately apparent and “postures” (Grutman 2018)as self-translation, this is the social self-translation I have elected to classify as “illusory” (see Table 1). Essentially, the illusion or posturing lies in cases where cue buttons or translation alerts are either not provided and/or where readers of the content could be “duped” into thinking the translation was produced by the same user. The illusory label is also applicable in cases where users have used automatic machine translation outside the social platform (i.e. a Google Translate translation imported by “copy/paste” innocuously). It is important to note that, generally speaking, embedded automatic machine translation on social platforms is not used by those producing content. Thus, if users have the ability to write in more than one language and if they are also actively interested in seeing their content appear multilingually,13 they may proceed with self-translating in the way I have defined it under the label “real”.14 Alternately, users may outsource the translation process in any number of ways, from professional human translation to automatic machine translation on another website (e.g. Google Translate followed by a “copy/ paste” onto Facebook or Instagram15) and still present these translations as their own. Embedded automatic machine translation on social platforms is meant for consumers of content, i.e. those who cannot read the original post or caption without resorting to this type of translation “service”. This creates a simulation of self-translation: even though the embedded automatic machine translation system acts as an intermediary, this intermediary becomes invisible, and those reading the translation will readily associate the translation with the author of the post or caption rather than with the mediating automatic translation system. What we have then is a kind of pseudo-self-translation (Toury 1995,40–52; Uribarri Zenekorta 2013). Research is ongoing, but preliminary data on user comments provides insight into how users perceive these types of automatic translations. Direct responses to the source-text TRANSLATION STUDIES 7 post/caption published in different languages would suggest that users are bypassing the translation system and assuming that the translation was produced by the author of the post, even though they are likely aware – to some degree and in some cases – of machine intervention.16 My focus here is not to comment on the quality of machine output or on the degree of sophistication of these tools. Rather, I am interested in how automatic machine translation contributes to (or maybe even impedes) social self-translation activity. The way self-translation has been theorized to date usually starts from the premise that the “self” is human (recall the anthropocentric nature of Popovič’sdefinition and of the earlier classification). However, what about situations in which a user’s content is being translated as though it were their own (i.e. under the same name, timestamp, profile) by proxy of automatic machine translation? Is this not still a form of self-translation, given consumers’ perception and reception of that content? After all, as descriptive translation studies (DTS) has demonstrated, even when the (self-)translation status is “factually wrong” (Toury 1995, 50), it is nonetheless often “functionally effective” (ibid). For instance, when translated posts or captions are cited, these translations are not identified as “belonging” to Facebook’s translation system; rather, the translations are implicitly attributed to the pro- ducer/author of the source content. It may be useful to think of this type of illusory social self-translation along the lines of Baudrillard’s(1981) concept of simulation.17 These illusory, and often automated, social self-translations have the appearance of being real in every way, and increasingly so as technology and machine learning improves. These self-translations seem so “real” to other end-users: they are real-fakes; real-fakes produced by machines.

Human-produced social “self”-translation: The hyperreal The final category in the taxonomy comprises translations that are produced by humans for other humans, under the guise of self-translation. Much like the previous category, these “self”-translations are also real-fakes; however, they are not produced by automatic machine translation systems (though they can be supported by computer-assisted machine translation tools). Here, I take inspiration from Eco’s(1975/1986) concept of the hyperreality, which refers to something that presents “as being more real than the real” (Macey 2000, 192–193). Because “real” self-translation is involved in this process (the real self-translator being the account owner), in addition to support provided by other human agents, this self-translation is also, to varying degrees, “faked”. As a remin- der, “social” still refers to the larger context of social media, to the idea of sociality that underpins social media activity, and to the social affordances specific to these media. Many social media influencers have come to understand the value of creating multilingual content. Given that many brands reward influencers based on engagement (i.e. hard sales of products, but also soft engagement such as likes, retweets, shares), it stands to reason that in order to achieve more engagement, an influencer would want to find ways of reaching more audiences; therein lies part of the relevance of studying social self-translation phenomena. While these influencers could opt to rely on automated systems, this is not always the best strategy for creating engagement because the translations do not always register as “authentic”.18 For instance, when certain influencers and brands originally relied on embedded systems like Microsoft’s Bing, commentators were quick to remark on how unidiomatic and robotic the translations were (e.g. Constine 8 R. DESJARDINS

2016). Indeed, the concept of authenticity is highly relevant in influencer strategy: the more distance an influencer creates between them and their audience (e.g. via clunky translations, poorly curated content, spamming, going “off-brand”), the less engagement they are likely to receive, which then translates to loss of sponsorships and, in some cases, loss of capital gains (either in product or in revenue). Conversely, strategies that serve to create the perception (if not reality) of a more authentic connection between the influencer and other social platform users tend to create more engagement.19 However, if an influencer is not fluent in the language of sought-after consumers (i.e. their followers) and does not wish to leverage an automated system (for previously men- tioned reasons), what other forms of “self”-translation can they mobilize? I propose the term “hyperreal social self-translation” to refer to the complex process whereby multiple individuals generate a seemingly “single” human-produced real-fake self-translation. In this instance, the source text is translated by another appointed individual (not the self, stricto sensu) or a team asynchronously (usually the source text is produced first and followed by the translation). My use of the term “team” instead of “crowd” is purposeful and it serves to distinguish this type of self-translation from crowdsourced translation and collaborative translation. Crowdsourcing is premised upon leveraging the crowd’s expertise to expedite problem-solving or product-to-market (Jiménez-Crespo 2017). In the case of hyperreal self-translation, no call is made to a crowd: a team of experts, rather, is assembled intentionally (unlike the amorphous and anonymous “crowd”) and team members are not usually individually identified as translators or content creators/contributors (said differently: the content still appears to be produced by the account owner/ influencer). Instead of a team, it is also possible to have a scenario in which another (sole) individual is appointed and tasked to curate and create content. In the case of the team, there is also no issue of scalability, which is also often the mark of crowdsourced initiatives. The purpose of the team in this iteration of self-translation is to maximize authenticity, engagement, and, by extension, the brand appeal of the influencer (or individual/ self) in question. Moreover, hyperreal self-translation is not collaborative in the sense that it is not about creating an inherently participatory process, as is the case with fan-subbing projects or collaborative revision work in Google docs, for instance. The team’s/individual’s involvement is intentionally self-effacing to reinforce influencership and/or the authenticity of the account owner. Finally, unlike the collaborative model in which a horizontal structure is usually preferred, calls for a vertical modelling: the team works to support the positioning of the influencer “self”. There are multiple options for how this “self-effacing” team might be configured: for instance, it could consist of (a) content expert(s) or strategist(s) alongside a translator or translation team, just as it could consist of a bilingual social media team or bilingual social media specialist. In any case, the team’s work is done behind the scenes, meaning that it occurs without the influencer necessarily accessing or intervening in the translation process. When the content is uploaded, followers are presented with a bilingual caption that is presented in bi-text format and that appears as though it were translated by the influencer in question. The significant difference between this hyperreal form of self-translation and the “more authentic” (“real” per our taxonomy) social self-translation presented above is that, in the former, it is rarely the author of the post who produces the self-translation received/consumed by the end-user (hence the term “hyperreal”), whereas in the latter (“real”), the individual producing the content is the same individual who translated it. TRANSLATION STUDIES 9

Descriptive examples Ethical considerations20 In order to illustrate each type of online social self-translation (the real, the illusory and the hyperreal), I have elected to use specificexamplesfromdifferent social media accounts. Ana- lysing online social data necessarily implies that my work involves humans and human data (social media users and content producers; user-generate content). However, this is not to the degree that I have interviewed, controlled or tested these individuals (there has been no direct contact or interaction with the human subjects/individuals). In other words, I am not working with human participants.21 I use public user-generated content as examples while bearing three considerations in mind. First, users who have public social media profiles and social media influencers who have public social media profiles have agreed to the platforms’ terms and conditions, which explicitly means agreeing to the public dissemination of specific content and implicitly means providing consentforthepublictoaccesstheircontent. Secondly, I restrict my analysis to content that is publicly available, meaning that any material profile parameters that are private or that can lead to identification beyond what is reasonably presented as public has not been used or has not been censored to avoid identification. Finally, I believe that engaging directly with these users in order to obtain explicit consent to use their public data would likely (if not certainly) skew the observational data that I am trying to study in its original context of production (as per article 10.3 of the Canadian Tri-Council Policy Statement). In review, it was suggested that I contact account owners to verify specific hypotheses. This will be done in future research but sufficient online and offline evidence exists to support the claims: these include, for instance, qualitative evidence in the form of account-owner/user interactions, account-owner biographies, public profile parameters, posting history, followship and engagement metrics. I also leverage my professional experience as a social media content creator.

Methodological considerations As research is ongoing, the descriptive examples that follow constitute only a small corpus. I have conducted a scan of various influencer profiles from different social communities, using some of the categories of social media influencers that are described in endnote 22 (second link). I have elected to use examples from different areas of interest (fitness/wellness, vlogging, music/entertainment), using different types of influencers (a fitness influencer or “fitfluencer”, a vlogger, and a celebrity from the entertainment industry) as a strategy for addressing different types of self-translation that occuracrossthesocialmediathematicspec- trum. In future research, the collection strategy would be refined and the corpus extended.

Example 1. The real: Massy arias (aka MankoFit), fitness influencer/fitfluencer, United States. This is an example of what I have definedasrealonlinesocialself-translation.Here,thepoint is not to provide extensive empirical evidence nor is it to analyse the quality of the ST – the point is to illustrate the taxonomy classifications. Massiel Arias, who also goes by the name of MankoFit, Massy Arias, and who posts under the Instagram handle @massy.arias, was one of the early “fitfluencers” of Instagram. To date, she has 2.5 million followers and has partnered 10 R. DESJARDINS with various brands from Target to CoverGirl. However, her ascent to influencer status was in part due to the fact that she was quick to catch on to and tap in to the value of bilingual/translated content the early years of her personal account. Unlike other “fitfluencers”,suchas @kaylaitsines22 who has always posted exclusively in English and who, generally speaking, resonates with heterosexual, cisgendered Caucasian women, Arias – a Dominican-born resi- dent of the United States – spoke to “Othered” audiences early on, primarily women of colour and the Latinx communities: she has, for instance, used her posts to reference the Black Lives Matter movement and has mobilized hashtags to reference and engage different communities involving women of colour. Moreover, many people – and women in particular – have cele- brated her for offering, through her muscular physique, what they consider to be a healthy alternative to visual representations of strength (as compared to Itsines and other women fitfluencers whose purported strength is embodied by an aesthetic that disproportionately pri- vileges small or svelte silhouettes).23 Although Arias’s mass appeal with these audiences could be explained by her visual content alone, I contend that self-translation has also functioned as a key strategy and one that has been generally ignored by social media researchers examining influencership/influencer marketing. Recipient of a 2016 Hispanic Heritage Award for her influence within Latinx communities,24 Arias’s Latin roots are thematically central to her posts, where she readily engages the Spanish language. In fact, tracing back to her early posts, she consistently presents material bilingually in Spanish and English. One of the idiosyncrasies of the 3,574 posts published by the time this article was written is the recurrent bitext format of the captions, in which the English version is typically presented first, followed by the Spanish. Although it is not clear which of these is the source text and which is the target, Arias has claimed authorship of her early content in interviews and Instagram “stories”, including the self- translated captions, and for this reason, I label her self-translations as real online social self-translations (see Figure 1).

Figure 1. Public Instagram post (self-translation/bitext) by ﬁtﬂuencer @massy.arias (15 April 2016). TRANSLATION STUDIES 11

Example 2. The illusory: German vlogger’s Facebook status update To illustrate illusory social self-translation, I have chosen an example from the Facebook page of a German vlogger. In recent years, Facebook has modified how translated content is presented to users. Prior to 2018,25 if a user posted a status update in a language in which the recipient user was not fluent (or did not regularly use on Facebook), Facebook would automatically prompt an illusory self-translation in bitext format, akin to the layout seen in Figure 2. Features that are now more visually salient (“Hide original”; “Rate this translation”) used to feature less conspicuously than they do currently, at the time of writing (2019).26 While it is true that buttons such as “Hide original” and “Rate this translation” clearly signal to the reader that some sort of intermediary enacted the translation, there is no overt mention of the automatic machine translation system (e.g. “Translated by Bing” or “Translated by Facebook”). Moreover, when these features were less salient in the recent past, it is likely that most lay users paid little attention to them (unless the idioma- ticity of the text was particularly dubious or amusing), focusing instead on the content of the post itself. An important additional factor contributing to the presumed focus away from the markers of translation is the busy and ever-changing nature of the Facebook newsfeed: not only is the newsfeed a kind of repository of user posts, advertisements, news trends and notifications but it is also a very dynamic and distracting one, with updates occurring regularly, thereby reducing the inclination or opportunity for users to focus on those smaller details. More recently, Facebook has modified this layout. Instead of showing both texts in bitext or multilingual format (as in Figure 2), the platform now displays the illusory self-translation exclusively (i.e. leaving out the corresponding text in the initial language), which means that users can read the content without realizing it has been produced via [machine or outsourced] translation, unless they pay particular attention to the buttons below the text, which are nearly as discreet as in their previous iteration (see Figure 3). Equally notable is that users are not necessarily responsible for launching or instigating the translation (unlike on Instagram, where users still, in 2019, have to actively push the “See Translation” button to access content in their preferred language): if Facebook can

Figure 2. German vlogger’s Facebook status update (April 2018). 12 R. DESJARDINS

Figure 3. German vlogger’s translated post as it appears to Facebook users who have deliberately or inadvertently identified English and not German as their profile preference (April 2018). detect a user’s language profile or preferences, it simply automates the illusion of self- translation. Thus, from the perspective of a content creator who has in fact opted to write a post involving one text and its self-translation into another language (“real” social self-translation), this automation would ostensibly duplicate not only the two texts but also the very decision to self-translate, by preemptively making the decision on that user’s behalf. The examples in Figures 2 and 3, I argue, are simulations: illusory social self-translations that are specific to online and social media and that are possible only because of the media on which they occur. Without sophisticated instantaneous algorithms, seamless illusions of self-translation activity could not and would not be possible. Moreover, as Facebook continues to perfect its technology, and as machine learning comes closer to matching human-produced output (or even outperforming it), it can be hypothesized that, in the near future, it will be increasingly difficult to discern which self-translations are produced by multilingual users and which posts are produced exclusively by machine translation. Could we envision a time when Facebook might omit the buttons and features that signal machine translation altogether in all instances? After all, what purpose do the buttons have if not to help Facebook refine its algorithms and translation output?

Example 3. The hyperreal: Céline Dion, French-Canadian singer and celebrity, Canada Céline Dion is an internationally renowned singer from Quebec, Canada. Her Instagram account (@celinedion), which consistently contains bilingual (English-French) captions, is rather unique in its approach to content curation insofar as it has not been consistent in identifying the authorship/translatorship of its content. In some cases, such as in Figure 4, the captioning appears to be the work of Dion herself, with posts featuring a number of idiosyncratic markers. Given that this post appears to have been written and translated by the star herself, judging by the use of the first-person and the presence of Dion’s signature (i.e. “Céline xx”), the example in Figure 4 could, and indeed would, be classified as a “real social self-translation”. Elsewhere on the same account, however, it becomes evident that the content has actually been curated and translated by someone else – most likely a team of experts or perhaps a single content curator. The caption in the post in Figure 5, for instance, makes this explicit with its “Team Céline” sign-off. TRANSLATION STUDIES 13

Figure 4. Céline Dion poses with another pop icon, Adele (26 January 2018).

In order for us to better understand the ambiguity (and indeed interest) surrounding the authorship/self-translatorship of @celinedion Instagram posts, it is instructive to bear in mind the two preceding variations while considering content from the early days of this account. Figures 6 and 7, below, provide meaningful examples.

Figure 5. A post in which caption content is signed “Team Céline” (17 November 2017). 14 R. DESJARDINS

Figure 6. First post on the @celinedion account (no caption signature) (27 October 2015).

What the last two examples share with one another but not with the first two is that both are unsigned. Yet the question of probable authorship and translatorship in each of the two posts is decidedly distinct. On the one hand, the caption in Figure 6 seems more likely to have been authored by Dion. One of the main bases for this assertion is that the photograph accompanying the caption is a selfie, with Dion looking directly at her audience. In addition to the fact that self-portraiture can be conceptualized as a form of intersemiotic self-translation (Desjardins 2017b), selfies seem to be most commonly mobilized by the very subject-object of the image (and, in turn, least commonly mobilized by those who do not occupy this doubled position vis-à-vis the photograph). By contrast, while nothing prevents the professional photograph (by @desnisetrescello) in Figure 7 to have been posted and “captioned” by Dion, the image does not carry the same semantic weight as the selfie as far as suggesting the identity of the caption’s author and/or translator is concerned. Thus, whereas this caption, taken in isolation, reads as though it were written and self-translated by Dion, the text does not seem as likely to be Dion’s when it is considered alongside the photograph and in comparison with similar posts from the same time period on the @celinedion account. One of the inherent challenges of social media data is that it evolves very rapidly in response to the ephemeral nature of the media themselves. Researchers are therefore rarely on solid footing, and I am no exception. While initial @celinedion account posts were directly in line with my conceptualization of hyperreal self-translation, whereby a team of humans effectively creates pseudo-self-translations on behalf of an influencer, later posts have followed a different trend, with @celinedion posts now signed “Céline xx” (as in Figure 4, where Dion has produced the captions herself) or “Team Céline” (as in Figure 5, in which the social media or marketing team attends to the different linguistic versions of the caption). These markers offer guidance to the audience with respect to how to interpret the nature of the translations, either as Dion’s direct words or as those of (or mediated by) her team. The markers are also a likely response to users demanding more transparency and authenticity on behalf of influencers and celebrities and of platforms reforming their policies in light of increased scrutiny (we can recall Mark Zuckerberg’s 2018 Senate hearing on the issue of Facebook’s involvement in electoral disinformation). TRANSLATION STUDIES 15

Figure 7. Early post on the @celinedion account (no signature) (4 November 2015).

But these markers or other cues should not take away from the hyperreal self-translation of the account’s early days or more recent times. Anachronistically, the ambiguity of the authorship and, more relevantly, of the self-translatorship of earlier posts is actually heightened, precisely because of the hindsight afforded by “signed” captions such as those in Figures 4 and 5: we know there is now a va-et-vient between signature types and, therefore, between real social self-translation (e.g. Céline xo the human influencer) and online social allograph translation (i.e. Team Céline, the hyperreal Céline). What is more, it might be argued that the posts signed by Team Céline do not so easily evade the scope of hyperreal self-translation. Considering this premise does, however, involve a shift in and expan- sion of the notion of self – one that, perhaps paradoxically, insists on a vertical plurality that works in favour of the “Céline” self (and not a more horizontal “participatory” model as with collaborative translation). This proposition invites us, first of all, to consider “Céline Dion” (in this case) as a polysemous entity: most obviously, there is Céline Dion the individual (the human being and influencer who has a unique biography, biology, and psychology) and Céline Dion the brand. The @celinedion Instagram account variously represents both of these identities, often simultaneously and in other moments separately. More meaningfully, however, is the second level of the vertical plurality, whereby the Céline Dion brand-self is represented not only by Céline Dion individual-self but also by an entire team of people, who share a common set of goals, intentions, and audience. This could be where captions signed “Team Céline”, which may appear to be claiming “otherness” in relation to Céline Dion, are in fact functioning as “same”, with their bilingual captions serving as a variation of on the hyperreal online social self-translation: a collective behind the individual, with the self still somehow intact.

Conclusion In this article, I have sought to present a theoretical exploration of online social self-translation, addressing the lack of scholarship on how self-translation and online social media intersect, delineating the distinctions between several related concepts (online social self- 16 R. DESJARDINS translation, crowdsourced translation, collaborative translation, user-generated translation), and providing a preliminary and provisional framework and taxonomy for broaching the central topic, the specificities and sociality of self-translation on social media. What emerges from the theoretical reflection is that the medium for self-translation proves pivotal, with digital and online communication creating contexts not only encouraging but indeed compelling us to think about self-translation in a number of new and meaningful ways: can the self be at once singular and plural as with hyperreal self-translation? What can be said of the self when it relies on machine intervention to produce illusory self-translations? For one, OSM immediately shifts our scholarly focus from the literary to the more pragmatic:27 online social self-translation (be it “real” or “hyperreal”) on Instagram is used to confirm a singer’s tour dates (Figure 7); and “illusory” online social self-translation in a vlogger’s Facebook post serves to provide details about the local weather (Figure 3). Given the overwhelming number of users and the massive volume of content items (e.g. tweets, status updates) published on OSM platforms, turning scholarly attention to social self-translation more broadly also facilitates the reconceptualization of self-translation as a social activity: whereas in the literary sphere self-translation is typically a peripheral practice (in spite of it being far more frequent than what is commonly assumed, as Santoyo [2005] and Maria Recuenco [2011, 194–197] argue), self-translation in social media spaces becomes much more of a common, everyday practice. This is particularly true when all three proposed variations of the practice (the “real”, the “illusory”, the “hyperreal”) are taken into account, but it remains true even when we consider only the “real”, and the factors contributing to this – such as explicit and implicit norms regarding the length of posts – deserve careful attention. The theoretical investigation also exposes different ways of conceiving of the self, demonstrating how online social self-translation complicates extant equations between author and (self-)translator: as seen in the example of hyperreal online social self-translation, there are instances where the self can be both multiplied (many Céline Dions: x, x1,x2, … ) and pluralized (Team Céline = Céline: x = y + z+a + b+c + d + … ). This work seeks to provide a valuable springboard for re-considering a host of well- developed self-translation concepts and ideas (e.g. Recuenco’s 2011 typology, Dasilva’s 2015 “transparency”), including the self-translator’s agency and authority (Grutman and Van Bolderen 2014, 324), notions related to the temporal distance between source and target text production (Grutman 2009: “simultaneous” vs. “consecutive” self-translation), and typologies for how multilingual editions of self-translation (Gentes 2013) might be understood. It bears recognizing that, methodologically, the corpus consists of figures who reside in Canada and the United States and whose social self-translation content exclusively incor- porates languages that occupy certain positions within those spaces and speak to particular power dynamics within those spaces (i.e. English, French, Spanish, as partly influenced by our linguistic repertoire and geolocation). As more research of this kind surfaces, it will be very important to diversify the linguistic variety of the studies.28 Meanwhile, subsequent research could build on this provisional taxonomy and analyse different social media users and influencers to map self-translation trends and to investigate the connections between self-translation, social media affordances, and marketing strategies. TRANSLATION STUDIES 17

Notes 1. The use of “social” will be addressed further on. 2. This assertion is based on English’s status as one of the most common online lingua francas (Ostler 2010; Statista 2019).There is also the fact that most websites, apps, and social platforms are launched and supported in English first, followed by the addition of other supported languages over the years. For instance, it took nearly seven years from the launch of Instagram for the platform to support right-to-left languages like Hebrew and Arabic (Tepper 2017). It should be noted that non-English platforms do exist (Sina Weibo and Baidu Tieba) and have considerable user bases, but this does not suggest that recourse to English in the development of these platforms is inescapable. Most programming languages are premised upon English keywords and coding libraries, which means that some appli- cations, software, and websites (such as those used in the Chinese market, for instance) may not interface with users in English, but are still underpinned by English-based programming languages. Reports have suggested the inherent biases (linguistic or otherwise) that can find their way into programming languages and algorithms, which likely impacts users and which supports inherent Anglocentrism in online communication (Eder 2018; Knight 2017). 3. For the sake of concision and readability, I will subsume “online” in “social” in this article. In my previous work (Desjardins 2017a), I explain the importance of the epithet “online” in relation to social media. This is why “online” has been bracketed. 4. Note that the taxonomy functions similarly to that of biological taxonomies, in which the “genus”, here, is “social self-translation”, and each type (real, illusory, hyperreal) is classified as a “species”. 5. The concept of “cost”, here, is understood as a financial cost. There is no denying that there is a “price” to sharing personal content on online social platforms, including the price of sharing one’s personal data, compromising one’s confidential information, and the cost of “play labour”. For more on these debates, see Fuchs (2017). 6. My use of “social”, “sociality”, and “social affordances” is based upon Fuchs (2017) as well as Standage (2013). 7. Arguably, the same could be said for “real”: what constitutes “reality”? Given that the taxonomy is partly inspired by Baudrillard’s concepts of simulation and simulacrum, terminologi- cally, “real” seemed more appropriate. This could also pave the way for a discussion on reality vs. hyperreality in translation praxis in online settings. 8. This seems to align with Castro, Mainer, and Page’s(2017, 10) chronology of self-translation scholarship: “It is only in recent times, due to growing multilingualism of contemporary societies and the internationalisation of English, that the frequent and recurrent practice of self-translation has become more visible through a process of ‘coming out’”. 9. https://www.theguardian.com/technology/2014/jun/29/facebook-users-emotions-news- feeds. 10. This may change following the 2018 Senate hearings in which Mark Zuckerberg, Facebook’s CEO, was asked to testify about Facebook’s user privacy and security practices, as well as the increasing dissemination of misinformation on the platform. Although the subject of the hearings related to the way that Facebook tested specific platform features, the issue of users’ mental health – which intersects with discussions about privacy and testing that occurs unbeknownst to users – was also a discussion point. 11. https://www.instagram.com/p/BG9uEMNhQXK/. 12. http://www.adweek.com/digital/instagram-will-start-letting-you-translate-captions-and-bios- other-languages-172198/. 13. As Grutman (2009, 257) underscores, self-translation is no accident; self-translators “choose to create in more than one language”, and “their conscious awareness of this option cannot be overstated” (emphasis added). 14. Some researchers might argue that what has been referred to as real social self-translation in this paper is in fact multilingual and/or heterolingual writing. I maintain this is still a valid form of self-translation. 18 R. DESJARDINS

15. The use of machine translation could be conceived as a human-computer collaboration, and, by extension, as collaborative translation, though this is not the argument I wish to make here. That said, such a collaborative “relationship” does align with the thought-provoking concept of “augmentation” proposed by Davenport and Kirby (2015) (see Desjardins 2017a). 16. Users must “activate” the translation feature by clicking “See Translation” (Instagram), or there is an indication that the user is not being presented with the original content, in which case a “See Original” tag is prompted (Facebook; Instagram). 17. I leverage Baudrillard’s work, although I employ his terms in relation to the object of study, self-translation, rather than as a lens to understand historical periods. Simulation,asitis defined and used here, refers to “the technique of imitating the behaviour of some situation or process [i.e. self-translation] […] by means of a suitably analogous situation or apparatus” (http://csmt.uchicago.edu/glossary2004/simulationsimulacrum.htm). Online and digital contexts have facilitated the imitation of self-translation through embedded automatic machine translation (what I call “the illusory”), i.e. an analogous apparatus that permits and imitates “real” self-translation. Simulacrum is defined as “mere image”, as the “appearance of a certain thing, without possessing its substance or proper qualities” (ibid). In some cases of online social self-translation, we argue that there is the appearance of self-translation (simulation/simulacrum), but without all the usual requirements or parameters of self-translation per more traditional or conservative definitions (e.g. one human producing two texts, in two different languages, in a rather sequential manner). Simulation and simulacrum do not necessarily obfuscate human intervention (in the case of “illusory” translations, humans still intervene in the sense of setting their language parameters and activating geolocation, to name only these examples). 18. The concept of “authenticity” is central to influencership and influencer marketing (e.g. Audrezet, de Kerviler, and Guidry Moulard 2018). 19. Although she does not talk about online social media specifically, Oswald (2012) does discuss the importance of idiomatic translation in the context of brand semiotics and online marketing strategies. Her discussion, I argue, applies to influencer strategies as well. The review of current literature on the subject of influencer strategy has yet to indicate in-depth studies that connect influencer marketing and translation. This research could be a step towards bridging that gap. 20. For a more detailed account of social media research ethics, see Fuchs (2017,59–60). 21. I argue that this work therefore falls within the purview of observational work that poses minimal risk, as stipulated in article 10.3 of the Canadian Tri-Council Policy Statement (http://www.pre.ethics.gc.ca/eng/policy-politique/initiatives/tcps2-eptc2/chapter10-chapitre10/). 22. https://www.forbes.com/sites/clareoconnor/2017/04/10/forbes-top-influencers-inside-the-rise- of-kayla-itsines-the-internets-workout-queen/#582a0362673f and https://www.forbes.com/ top-influencers/#8b9ca9372dde. 23. https://www.theguardian.com/lifeandstyle/2015/apr/20/fitspo-strong-skinny-social-media- food-abs-better-living-body-fascism. 24. http://hispanicheritage.org/massy-arias-receive-wellness-award-target-hispanic-heritage- awards/. 25. Facebook announced more efficient translation in January 2018, which seems to coincide with the difference in layout I am referring to: https://thenextweb.com/facebook/2018/01/ 24/facebook-rolls-faster-accurate-translations/. 26. For instance, the text of the features would appear in a paler colour and smaller font, and they would still be placed in a non-prominent location. 27. This should not suggest that literary self-translation does not take place on social media (on the contrary!) or that literary pursuits do not serve pragmatic purposes; the point, rather, is that the principal goal of or motivation for literary writing is far less commonly utilitarian, and that non-literary self-translation far outweighs literary self-translation on OSM. 28. It is worth noting that the Idle No More movement in Canada mobilized self-translation strategies from our taxonomy in their content creation with an eye to giving “visibility” to Indigenous languages, even though current research does not necessarily address the topic of language choice or translation head-on (Raynauld, Richez, and Boudreau Morris 2018). TRANSLATION STUDIES 19

Acknowledgements The author would like to thank Trish Van Bolderen (University of Ottawa) for her thoughtful comments and insights in the preparation of this manuscript.

Disclosure statement No potential conﬂict of interest was reported by the author.

Note on contributor Renée Desjardins is an associate professor at the Université de Saint-Boniface in Winnipeg, Canada. She is the author of Translation and Social Media: In Theory, in Training, and in Professional Prac- tice (2017). She has been researching and writing about translation and social media for a decade and has published on the subject in a number of outlets, including The Routledge Encyclopedia of Translation Studies and The Routledge Handbook of Translation and Pragmatics.

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