Notes on " Real men don’t hate women’: Twitter rape threats and group identity"

What this study is ABOUT:

In this study, they look at the tweets about Caroline Criado-Perez after her petition to the Bank of England to have Elizabeth Fry’s image on the UK’s £5 note replaced with the image of another woman was successful. The petition challenged the Bank of England’s original plan to replace Fry with Winston Churchill, which would have meant that no woman aside from the Queen would be represented on any UK banknote. Criado-Perez was exposed to ongoing misogynistic abuse on Twitter, a microblogging social network, including threats of rape and death.

Data and Methodology:

This study uses the tweets, mentions, and retweets extracted from Twitter in the account of Caroline Criado-Perez.

They combine corpus linguistic method with discourse analysis. This kind of hybrid studies involve testing hypotheses or comparing findings from qualitative analysis of a particular language variety against quantified observations in reference corpora or other comparable specialised corpora (Baker, 2006:15--17).

Data Analysis:

They try to find out key words by looking at the most frequent words. Keyword analysis is performed by comparing a frequency wordlist generated from one corpus against a frequency wordlist of another corpus, allowing the observation of words that are statistically most and least frequent. Such words are referred to as positive or negative keywords and, unlike a frequency wordlist, positive keywords allow the analysis of linguistic saliency rather than simple frequency (Baker, 2006:125). Most frequent lexical words: twitter, abuse, women, people, threats, think, rape, good, men, thank, know, support, woman, right, hope, thanks, trolls, sorry, time, love Most frequent topics:

Sexual aggression: abuse, rape, threats, trolls Gender: men, women, woman Mental processes: hope, know, love, think Politeness markers: sorry, thank, thanks

They exclude all word classes but nouns, verbs, and adjectives and they think that results, in this way, give them a better idea about the discourses within the corpus.

Moreover, they make collocation analysis. Collocation analysis is important as the meaning of a word or any linguistic unit might not be contained in itself, but could be embedded in it associations with other words. A collocation analysis of each of the frequent terms that make up the topic of (sexual) aggression---abuse, rape, threats, and trolls---was implemented to assess the meanings of these words as they occurred in context and how they shaped/were shaped by words with which they co-occurred. This was done by using the collocation function in AntConc and employs the Mutual Information (MI) statistical measure. Although other measures exist (log-likelihood, z-score), they draw on MI as it assesses both how closely words associate (by measuring frequency of co-occurrence) but also how strong those associations are (by measuring the likelihood that those two words occur together versus in isolation) (cf. Cantos Go ́mez, 2013:204--208).

The corpus was searched for the sexually aggressive terms: abuse, rape, threats, trolls.

Results:

Abuse and threats occurred frequently alongside other nouns. Some classes were specific to abuse (e.g. domestic abuse, child abuse, gendered abuse) and threats (e.g. bomb threats, death threats) but some were shared (e.g. criminal threats/abuse, cyber abuse/threats).

Adjectives expressing evaluation such as awful, cowardly, disgraceful, despicable, graphic, hateful, and horrendous were also prominent collocations, indicating the kinds discourse prosodies that may have been triggered when abuse and threats occurred as a collocate of women.

One of the strongest collocations is the n-gram rape threats. It occurs 1419 times in total, accounting for 43.69% of all 3248 instances of rape. Although rape may semantically imply a form of behaviour, when talked about in the corpus, rape is frequently positioned as being primarily a form of threat. Rape also collocates very frequently with other threat lemma---‘‘a group of wordforms that are related by being inflectional forms of the same base word’’ (McEnery and Hardie, 2012:245)---including threat, threats, threatening, threatened, threaten. This suggests a stable discourse prosody in which the semantics of rape are conflated with that of threat.

They study three board groups of Twitter users identified in the corpus: high –risk, low-risk, and no-risk.

High-risk users were defined as Twitter profiles that contained evidence of: intent to cause fear of (sexual) harm; harassment; and potentially illegal behaviour.

Low-risk users were defined as Twitter profiles that contained evidence of: offensive material; insults; ridicule; no (linguistic) evidence of intent to cause fear or threat of (sexual) harm; and spamming (as opposed to harassment).

No-risk users were defined as Twitter profiles that contained evidence none of the above.