ABSTRACT
Compared to racist and ethnicist discourses, literature on sexist discourses – both off and on-line – as hate speech is relatively underinvestigated. This is partly due to the tendency to minimise accusations of sexism and to reframe misogyny as ‘acceptable’ by constructing it as a form of humour. We decided to focus on slut-shaming, one of the most virulent forms of hate speech, which has always existed but was boosted by social media, becoming a stable low-cost ingredient of today’s rape culture. We propose to consider online slut-shaming as a form of ‘technology-facilitated sexual violence’, where digital technologies are used to facilitate both virtual and face-to-face sexually based harms. According to feminist analysis of sexual violence, this would be a matter of power rather than sex: sex would be the weapon, not the motive. We have tested this research hypothesis by focusing on the Italian reception of the MeToo campaign, triggered by Asia Argento’s denunciation. More specifically, two Different Twitter corpora produced within the same five months period were examined by means of a quantitative and a qualitative methodology.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes on contributors
Francesca Dragotto is Associate Professor of Linguistics. Her vast fields of interests include semantics, lexicon and pragmatics, both synchronic and diachronic. In recent years, her interests have been focusing on text analysis, considered as a cognitive, cultural and social whole, where each speaker re-builds their representation of the world and the social roles and norms interacting in its context.
Elisa Giomi is Associate professor at Roma Tre University, where she teaches Sociology of Communication and Television Storytelling. She also teaches Media in a double degree in Cultural Leadership with Groningen University. She is a member of the editorial board of AG. AboutGender-International Journal of Gender Studies and she has coordinated several international research projects in the field of gender and the media. Elisa has authored and co-authored books and chapters included in internationally-circulated collections and articles that have appeared in either national and international peer-reviewed journals.
Sonia Maria Melchiorre is Researcher of English Language for Media and Communication. She has published in several countries on English language and literature (from XVIII to XXI century) and women’s studies. She has also taught gender studies at the American University Corsortium (USAC) for several years. Her most recent research focuses on the language performances of lesbian characters in American and European TV series.
ORCID
Francesca Dragotto http://orcid.org/0000-0002-7792-0177
Notes
1 The authors have worked on every part of this paper together and share the views here presented. But as far as academic requirements are concerned Francesca Dragotto takes official responsibility for sections 2.1, 3, and 4; Elisa Giomi for sections 2.2, 5, and 6; Sonia Maria Melchiorre for section 1 and the general editing in English as language expert.
3 Sexist comments deleted from FB were eventually retrieved through an accurate research among the many sources which copied and quoted them (i.e. http://libernazione.it/beppe-non-sei-a-disagio/). Subsequently, a portion of them was analysed with the aim to achieve a word-cloud. To follow, the word list sorted by rank frequency, and partially normalised in order to sum up diatopic or diastratic variants: 8 faccia (face), 8 troia (whore, slut, bitch, dog), 6 zoccola (whore, slut, skunk), 6 pompin-/bocchin- (blow job), 5 merda/merdaccia (shit/shitty), 4 boldracca/baldraccha/baldraca (slut - pun with Italian vowels), 3 puttana (prostitute), 3 brutta (ugly), 2 succhiare (sucking), 2 vecchia (old), 2 lesbica (lesbian, dyke), 2 calci (kicks), 2 bocca (mouth, trap), 2 culo/kulo (ass).
4 The scraping was conducted by phd Mario Mastrangelo, text statistics analyser. As a Python, the unofficial GetOldTweets library (https://github.com/Jefferson-Henrique/GetOldTweets-python) was used, for its ability to provide much older tweets than the official library.
5 We chose to analyse only these tweets for reasons related to the intentionality behind the individual communication process.
6 The clusters were based on a subset of 278 graphic forms and have the following numerosity: CL 1: 47; CL 2: 23; CL 3: 115; CL 4: 93. The clusters were determined using the SPAD software, in particular using the hierarchical classification (RECIP procedure) – from the factorial coordinates deriving from the analysis of lexical correspondences (CORBIT procedure) – which follows Ward's aggregation criterion with 10 iterations of mobile center consolidation.
7 In its simplest form, linguistic competence, whose theory was proposed by Noam Chomsky, is defined as the native speakers’ ability to recognize and formulate well-formed sentences (Cfr. Abdulrahman & Ayyash, Citation2019; Barman, Citation2014; Thornbury, Citation2006).
8 Metalinguistic competence (awareness) can be defined as the ability to reflect on the use of language.
9 These are the categories obtained by the mingling of the previous one: 7 = 6 (neutral) + 4 (hate speech); 8 = 2 (sarcastic feeling) + 4 (hate speech); 9 = 1 (show off) + 3 (positive feeling); 10 = 1 (show off) + 3 (positive feeling) + 5 (disclosure); 11 = 1 (show off) + 4 (hate speech); 12 = 1 (show off) + 6 (neutral, mainly off topic); 13 = 2 (sarcastic feeling) + 3 (positive feeling); 14 = 1 (show off) + 2 (sarcastic feeling); 15 = 3 (positive feeling) + 6 (neutral, mainly off topic).