# N ot AW hore! A Computational Linguistic Perspective of Rape Culture and Victimization on Social Media (2020)

Abstract: The recent surge in online forums and movements supporting sexual assault survivors has led to the emergence of a ‘virtual bubble’ where survivors can recount their stories. However, this also makes the survivors vulnerable to bullying, trolling and victim blaming. Specifically, victim blaming has been shown to have acute psychological effects on the survivors and further discourage formal reporting of such crimes. Therefore, it is important to devise computationally relevant methods to identify and prevent victim blaming to protect the victims. In our work, we discuss the drastic effects of victim blaming through a short case study and then propose a single step transfer-learning based classification method to identify victim blaming language on Twitter. Finally, we compare the performance of our proposed model against various deep learning and machine learning models on a manually annotated domain-specific dataset.

## introduction global statistics indicate that 35% of women worldwide have experienced sexual violence at some point in their lives 1 . popular hashtags like '#metoo', '#sexualharassment' on twitter have encouraged victims to share their stories of sexual assault and formally report them. however, the backlash faced by the victims has been staggering. victims of sexual assault are often held culpable for the assault, and are attacked on social forums by extremists. with the rise of such crimes, it is important to devise a computational framework that can identify and prevent online victimization of sexual assault survivors who choose to report the crime. ambiguous interpretations of rape culture and victim blaming makes manually sorting and identifying such information an arduous task. ## related work prior research has shown sexual assault is a crime that women are most afraid of @xcite . oftentimes, victims of sexual assault are subjected to humiliation, blaming because of which, reasonable doubt is created about their credibility @xcite . popular theories such as the "just world" @xcite theory and the "invulnerability" @xcite theory explain the psychological motivation behind victim blaming. the "just world" theory states that people get what they deserve and deserve what they get, thereby shifting the blame of the crime to the victim while the "invulnerability" theory states that people blame the victim to project their own sense of invulnerability. victim blaming, therefore, stems from an individual's personal sense of insecurity and acts as a silencing function for most victims who are discouraged to disclose their personal stories or seek any help online @xcite . since victims fail to obtain the required medical assistance, they become highly susceptible to emotional difficulties that manifest as depression in the short term and acute psychological difficulties in the long run @xcite . in addition to this, chronosystem factors like past instances of victimization and sexual revictimization affect the mental health outcomes of the survivor often leading to suicidal behaviour, substance use, depression etc. @xcite . with the advent of social media, a new medium has presented itself for victim blaming to occur. social platforms like twitter, facebook and reddit provide a space to publicly post comments and present an insight into community opinions for researchers and social scientists. due to its increasing popularity, twitter is being used for research in opinion mining @xcite , keyword extraction (biswas, 2019), hate speech detection @xcite etc. it has been widely used for research on sexual violence @xcite as well as suicidal ideation using linear and ensemble classifiers @xcite . research has also been focused on hate speech detection for twitter using deep learning techniques, classifying tweets as sexist, racist or neither @xcite . @xcite have used naive bayes, random forest and j48 for detection of cyberbullying. due to the similarities between victim blaming and cyberbullying, we have used naive bayes as one of our baseline models. @xcite analyzes discussions on domestic abuse across social media, using lstm, naive bayes, logistic regression, and svm which has been used as a baseline against our proposed method because of its good performance. ## dataset construction creating our victim-blaming dataset entails a two step process: collection of data and data annotation. due to the lack of prior work, we create a custom dataset by crawling english tweets from twitter using the twitter api 3 that mentioned major hashtags related to sexual harassment. a total of 4,242 tweets were scrapped from november 6, 2019 to november 19, 2019 which contained 'metoo. also, 413 tweets were scrapped containing 'sexualharassment. we further observed that victim blaming tweets contained derogatory terms like 'whore' therefore, we used common words related to sexual harassment (rape, slut, whore) for increasing the number of positive samples. a total of 732 tweets were added after scrapping tweets from november 16, 2019 to november 29, 2019. tweets are unstructured and noisy in nature due to the use of informal language prevalent on social media @xcite . for pre-processing them, stop words are removed along with non alphanumeric text as they provide little information. url's, emojis like ':)' and the symbol '#' are removed using regex. usernames are also removed for anonymity. the tweets are then tokenized to run the various models. tokenization and removal of stop words is done using nltk. the average 3 https://developer.twitter.com/ number of characters in a tweet is 33 foot_1 . after pre-processing, we remove tweets which have less than 33 characters. the final dataset contains 5,070 tweets out of which 1562 were classified as positive samples. under the guidance of a social scientist, a coding scheme to identify victim blaming language is devised, taking into account gender related discourse and campaigns as well as psychological analysis of rape culture and victimization from social media. we follow a two-phase annotation process for the classification of each tweet in the final dataset. in phase 1, two annotators, a psychology student and a social science student identify tweets which contain features listed in ## the victim blaming language model nlp problems are solved using shallow machine learning models or deep learning models along with manually crafted features which fail to capture the linguistic information properly due to dimensionality problems @xcite . twitter follows a unique semantic and linguistic format @xcite that makes it imperative to devise frameworks that incorporate the specific linguistic tweets label she went with them and got drunk. she slept with them and when they called her a whore accused them of rape 1 she is a dumb british whore who filed fake claim 1 harvey weinstein is a very evil man, but some of the celebrity women apart of the #metoo movement that were "sexually assaulted" knew exactly what they were doing. 1 another perverted democratic! let's see if hollywood will say something or if #metoo is only for republicans. 0 they definitely raped her. why else would they call her a whore? 0 women don't admit they lost their power to men via rape for the fun of it. if a woman says she was raped, believe her. 0 we propose a simple yet effective classification method based on single step transfer learning @xcite . state-of-the-art transfer learning methods employ language models (lm) trained on generic corpora with additional fine tuning of lms for task specification. in our method, we combine the task-specification and language modelling with the help of an auxiliary loss function that is adjusted during training for task and linguistic adaptation. this prevents catastrophic forgetting and allows our model to be trained on a social media specific large corpora for e.g.: twitter or reddit standard datasets and then be adapted to target tasks on domain specific smaller corpora. @xcite theorizes that the prevention of catastrophic forgetting for machine translation tasks is because the language model objective acts as the regularizer that limits the loss of generalizable features and evidence for the same is presented in their work. we intuitively inferred that a similar approach would be effective in capturing the subtle topics of victim blaming on twitter due to the additional language modelling step that guides the training across the text classification task. ## conclusion and future work in this work, we established the need to devise a computationally effective method to identify victim blaming language on twitter. to achieve this, we proposed a single step transfer learning based classification method that effectively captures the unique linguistic structures of twitter data and victim blaming language. on a manually annotated dataset, our proposed approach could achieve significant improvement over existing methods that rely on custom textual features and popular deep learning based methods. the prevalence of rape culture and the subsequent victim blaming on unsolicited social media forums like twitter has not been studied from a computational linguistic perspective before. our work, therefore presents an extensive study of popular text classification methods on a niche' dataset with victim blaming semantics and further presents the significance of using a simple transfer learning approach to capture twitter semantics on a limited dataset. we anticipate that this study encourages further research on how victims of sexual assault are portrayed on social media. our future agenda includes further bifurcating and exploring the specific types of victim blaming and the efficacy of the proposed approach on such a multi label classification task. we plan to explore the different weighting factors for the language modelling loss and classification loss described in section 4 to determine if weighting factors can help customize the auxiliary loss for different tasks.