Discourse Context & Media最新文献

This paper discusses the epistemological differences between the label ‘multimodal’ in computational and sociosemiotic terms by addressing the challenges of automatic detection of hate speech in racist memes, considered as germane families of multimodal artifacts. Assuming that text-image interplays, such is the case of memes, may be extremely complex to disentangle by AI-driven models, the paper adopts a sociosemiotic multimodal critical approach to discuss the challenges of automatic detection of hateful memes on the Internet. As a case study, we select two different English-language datasets, 1) the Hateful Memes Challenge (HMC) Dataset, which was built by the Facebook AI Research group in 2020, and 2) the Text-Image Cluster (TIC) Dataset, including manually collected user-generated (UG) hateful memes. By discussing different combinations of non-hateful/hateful texts and non-hateful/hateful images, we will show how humour, intertextuality, and anomalous juxtapositions of texts and images, as well as contextual cultural knowledge, may make AI-based automatic interpretation incorrect, biased or misleading. In our conclusions, we will argue the case for the development of computational models that incorporate insights from sociosemiotics and multimodal critical discourse analysis.

Community-building among groups of child abusers on the ‘dark web’ facilitates the large-scale distribution of indecent imagery and supports individuals in becoming more skilled, more dangerous offenders. Undercover police are tasked with posing as offenders to gather intelligence; however, we know little about the nature of these groups, and especially how one might approach them linguistically as an ‘authentically’ interested outsider. This study analyses rhetorical moves (Swales, 1990) in forum posts from child abuse-related dark web fora by self-identifying ‘newbies’ hoping to join established abuse communities. It identifies 12 distinct moves used in the pursuit to join online abuse communities and finds that expressions of competence and expertise are central to newbies’ attempts to gain community membership. ‘De-lurking’ is identified as a useful strategy in the performance of competence in online forums. These findings can support online undercover policing tasks as well as offender prioritisation.

In light of the growing emotionalization of public discourse, this article deals with the action of shame allocation in Israeli accountability interviews. A qualitative analysis of tokens of the Hebrew verb lehitbayesh ‘to be ashamed’ in political interviews was conducted using Discursive Psychology and Conversation Analysis methods. The findings show that in this public context the verb lehitbayesh is mostly not used to convey an emotional state, nor can its meaning be explained by the classic theoretical conceptualization of shame. Instead, lehitbayesh is mobilized to allocate shame to another actor, and portrays the allocator as morally superior and as someone who sacrifices for what is right. Lehitbayesh is part of the negotiations between journalists and politicians over the question of who is accountable for a transgressive act, what the desired response is, and who the relevant audience for the moral lesson is.

This paper observes the emergence of membership categories and their role in the construction of accountability in news interview interactions on two Arabic networks. It adopts a Membership Categorisation Analysis (MCA) to analyse how these categories contribute to the design of interviewers’ questions and interviewees’ answers. The data include twenty-eight hours of recorded Arab news interviews from four shows. The findings demonstrate that there are three interactional patterns in which membership categories are invoked. In the first pattern, interviewees engage in negotiating their incumbency or their political allies in accountable categories in interviewers’ questions. In the second pattern, interviewees display membership categories to obviate the shown accountability, while in the third pattern, they show negation of their incumbency of some categories even though they have not been explicitly included in these categories in interviewers’ questions.

The discursive mediation of “elite taste” is a central ideological production of food critics, cultural arbiters of what entails “good food,” as well as who entails a “good cook.” In this paper, we employ a mediatized critical discourse analytic approach to scrutinize one brand of “elite taste” as mediated by food critics in the Netflix series Chef’s Table. Specifically, we identify the role of the white critical voice in legitimizing and problematizing the featured chefs and their food. We present an in-depth analysis of two episodes of Chef’s Table and argue that the program is undergirded by a Debordian spectacle in which white critics attempt to blur the boundaries between chef, audience, and elite food, but ultimately reproduce extant distinctions evidenced in the consumption of food along classed, raced, and gendered materializations of “taste”.

The hashtag has seen increasing attention in the linguistics literature, in recognition of its prevalence on social media and in other modes of communication. Here, we report on a diachronic analysis of the hashtag #wokeAF in English-language tweets posted between 2012 and 2022. First, we trace the use of the word woke from verb to adjective, with novel uses arising in African American Vernacular English. Such uses then spread into mainstream standard English, eventually being used in a novel construction: the intensifying expletive ([adjective + as + expletive]). Although examples of the intensifying expletive are listed in the Urban Dictionary, to our knowledge, this is the first linguistic analysis of the construction. Second, we analyse semantic interpretations and syntactic characteristics of the intensifying expletive #wokeAF, by documenting its occurrence in tweets spanning eleven years. Analysis of the discourse and the context in which the hashtag appears allows us to uncover its novel use as a collective noun, which in our data, is linked to a pejorative stance. In general, we find innovation in the semantic scope of the hashtag and versatility in its position and integration within tweets. Given the pervasiveness of the word woke in the public consciousness, as evidenced by its occurrence in the popular press, this article aims to fill a timely gap while providing an interesting example of language innovation online.

Existing feminist analyses of infant feeding practices have examined the promotion of long-term exclusive direct breastfeeding (DBF) as symbolic of “total motherhood” (Wolf 2011), where formula feeding is framed in contrast as “risky” (Murphy, 1999, Murphy, 2000, Brookes et al., 2016, Woollard, 2018). Discourses of expressing human milk (EHM), and their discriminatory potential, are currently under-researched. However, researchers note that rhetorical strategies that exclude EHM as a form of breastfeeding can reinforce the perceived normalcy of feeding at the breast and relegate breastmilk expression and formula feeding as “deviant” practices (Murphy, 1999, Hunt and Thomson, 2017; Rasmussen et al., 2017; Coffey-Glover, 2020; Anders et al 2022).

To that end, this study integrates Feminist Critical Discourse Analysis (Lazar, 2005, Lazar, 2007, Lazar, 2014) and corpus linguistics (e.g. Baker 2014) to examine discourses of breastmilk expression (EHM) in a corpus of online infant feeding promotional literature taken from seven organisations, with a particular focus on the largest two organisations in the corpus: La Leche League Great Britain (LLLGB) and the UK National Health Service (NHS). The analysis reveals language choices that marginalise EHM in servitude of “breast is best” (Murphy 1999), and specifically reinforce the message that ‘at the breast is best’. We show how EHM is marginalised in the texts via representations of exclusive DBF as the ‘gold standard’ of infant feeding, recirculating discourses of “total motherhood” (Wolf 2011), “natural mothering” (Bobel 2003) and “intensive motherhood” (Hays 1996).

Hate speech monitoring has become a challenging task for social media platforms. While efforts have been made to combat racism and other forms of hate speech, marginalised communities, such as the Roma are frequent targets of intense discrimination and online racist abuse. This article examines manifestations of Romaphobia, also known as anti-Roma racism, on YouTube in the context of 2016 UK Referendum on EU membership, when Roma along with other Eastern European migrants became demonized in the right-wing narratives of Brexit supporters. Drawing on Thematic Analysis and Critical Discourse Analysis, the article provides an in-depth account of how Romaphobia is discursively constructed in the commentaries of those watching YouTube documentaries about Eastern European migrants. In addition to drawing attention to various forms of overt and covert racism that fly under the radar in the content moderation process, the analysis shows that ‘entitlement racism’ is gradually becoming normalised in social media, with anti-Roma racism acquiring alarming levels of verbal violence which needs immediate attention.

This study explores the use of quotations in two contexts where online public discourses respond to social-political confrontations in China and the United States. We investigate the relationship between the form of marking quotations in online social media discourse and the functions that the quoted text accomplishes in the online interactive context in English and Chinese languages. In particular, we relate this to the affordances of the online context, taking account of the typographic indicators of quotation, such as the use of quotation marks. In the data analyzed, quotations indicate stances toward the quoted materials, toward people and ideas, or toward other participants in the comment sections. We describe four functions that draw attention to the communicative practice of quotation marking while showing how these are accomplished in their social, cultural, linguistic and technological contexts. We demonstrate how the literal and nonliteral meanings enlivened by quotations are exploited to display agreement or disagreement in online political comments.