Multitask Learning for Emotionally Analyzing Sexual Abuse Disclosures (2021)

Abstract: The #MeToo movement on social media platforms initiated discussions over several facets of sexual harassment in our society. Prior work by the NLP community for automated identification of the narratives related to sexual abuse disclosures barely explored this social phenomenon as an independent task. However, emotional attributes associated with textual conversations related to the #MeToo social movement are complexly intertwined with such narratives. We formulate the task of identifying narratives related to the sexual abuse disclosures in online posts as a joint modeling task that leverages their emotional attributes through multitask learning. Our results demonstrate that positive knowledge transfer via context-specific shared representations of a flexible cross-stitched parameter sharing model helps establish the inherent benefit of jointly modeling tasks related to sexual abuse disclosures with emotion classification from the text in homogeneous and heterogeneous settings. We show how for more domain-specific tasks related to sexual abuse disclosures such as sarcasm identification and dialogue act (refutation, justification, allegation) classification, homogeneous multitask learning is helpful, whereas for more general tasks such as stance and hate speech detection, heterogeneous multitask learning with emotion classification works better.

## introduction the #metoo movement foot_1 was started as an initiative to empower women against long-standing issues related to sexual abuse at workplaces, public spaces, and private organizations @xcite . the usage of a dedicated hashtag #metoo on media platforms signified a social support system for women from different sections of society. the movement initiated discussions on many socially stigmatized issues that were missing from the virtual space @xcite . such conversations invited various reactions on the web, involving support to the cause of the movement and even outright bullying. while many users took part in the vilification of the survivors, the movement also saw opposition by factions of the society that felt threatened by the impact of social media in raising awareness about the scale of everyday sexual harassment faced by women in workplaces and institutions @xcite . in many instances, the public disclosures of survivor-narrated incidents involved widespread use of hate-language and online trolling, both against the victims and alleged oppressors @xcite . the #metoo movement also led to people coming out with allegations, refutations, and justifications about traumatic experiences as they transitioned to active participants in the mainstream conversation @xcite . a closer look at the online posts about the #metoo movement revealed that sarcasm was often used as a thin veil in such discussions to humorously mask disapproval, wit, and personal attacks @xcite . the complex narratives present in the conversations on stigmatized issues like sexual abuse create an opportunity for researchers to study how people express their opinions on a sensitive topic in an informal social setting. it also offers a chance to social media regulators for fostering social inclusion, community integration, and improving the individual perception of being supported by others. this paper aims at categorizing the posts related to the #metoo movement on the basis of stance (support or opposition), hate-speech, sarcasm, and dialogue acts (allegation, refutation, or justification of sexual misconduct). we focus our analysis on a publicly available dataset that is created in the backdrop of mass instances of sexual harassment disclosures and includes nuanced labels to identify accompanying linguistic behaviors. existing literature has emphasized that the text's emotional attributes have a high correlation with dialogue narratives describing instances of sexual harassment @xcite . prior works @xcite have mostly focused on label specific detection of linguistic narratives related to sexual harassment disclosures in isolation by exploiting lexical features @xcite . however, subtle intricacies present in the discussion of sexual abuse disclosures often reflect the speaker's affective and psychological state, which are overlooked by feature-engineered models. for instance, part (a) of figure 1 shows a tweet expressing support towards the #metoo movement but in a tone that might be difficult for naive neural learning models to capture without context. part (b) of figure 1 presents a tweet in which the author has an initial positive outlook, which later reverses to disgust for the subject. the lack of context about the event and contrasting qualifications describing the oppressor makes the correct classification of the sexual harassment disclosure label extremely challenging for traditional classifiers without emotional labels' additional supervision. moreover, apart from their inherent complexity, conversations related to the #metoo movement also pose a challenge of emotional ambiguity. this work is the first attempt at joint modeling of narratives related to sexual abuse disclosures and emotion classification to learn the patterns of their interaction via parameter sharing techniques offered by multitask learning (mtl). the affective features, which result from a joint learning setup through shared parameters, will encompass the text's emotional content that is likely to be predictive of narratives corresponding to sexual abuse disclo-sures. more specifically, we formulate an mtl framework for multi-label classification of narratives related to sexual abuse disclosures (stance, hate-speech, sarcasm, dialogue acts) and emotional classification in the context of the #metoo movement. mtl @xcite allows two or more related tasks to be learned jointly. this facilitates the transfer of inductive bias and better generalization across related tasks on account of shared representations of linguistic features. ## problem description we aim to analyze different perspectives of the complex narratives pertaining to the #metoo movement on social media platforms. specifically, given a tweet text, we formulate for it a multi-label multiclass classification problem with definitions taken from previous works @xcite • stance detection: determining the opinion of the author of a tweet, regarding a particular target of interest @xcite . stance detection is categorized into three classes: support for when the author favors the #metoo movement or it's cause; opposition, representing opposing stance or indifference towards the movement; or neither, when the text does not have a clear viewpoint @xcite . ## ethical concerns and discussion analyzing social media data of individuals discussing sexual harassment disclosures and exploitation in public spheres necessitates the need to safeguard the ethics and privacy of individuals (tusinski @xcite . we address these: generalization we acknowledge that the limitations of the experiments might get amplified due to the highly subjective nature of this challenging problem. therefore it would not be fair to conduct a population-centric analysis based on inferences from this work. confidentiality individual consent was not sought from social media users as the data was publicly available. disclosure of sexual harassment information on public forums may have been met with public backlash and apathy. therefore the social reputation of the accuser and the accused would be at a peril @xcite . hence, the authors were aware not to make any automated interventions, as any attempts to contact individuals could be seen as personally intrusive and might also repeal their social information @xcite . bias & discrimination social support discussions on social media platforms gave victims the liberty to describe their instances of sexual exploitation and abuse @xcite . the authors are aware of the potential inevitable sampling biases that may be present in the data. importance has to be placed on mitigating the bias against certain minority groups, which might get amplified due to the sensitive nature of social discussions @xcite . ## conclusion in this work, we have proposed a flexible crossstitched multitask learning framework for the de-tection of narratives linked with sexual abuse disclosure on social media. our methodology takes advantage of the affective features from emotions and related tasks to encourage knowledge transfer and attain auxiliary knowledge. qualitative and quantitative results demonstrate how joint optimization of stance detection and sarcasm identification benefit each other, indicating their relatedness and dependence on each other. similarly, we observe that tasks like hate-speech classification and stance labeling benefit from each other and from emotion detection, thus reinforcing the benefit of joint linguistic learning between the related tasks. in the future, we aim to explore how this joint learning paradigm can be effectively leveraged for improving performance on downstream tasks like emotion analysis, identifying suicidal tendencies among abuse survivors. application from this work also has utility for problems such as identification of patterns of reported sexual harassment narratives, hate speech detection, the spread of rumors and fake news, and entity extraction for digital vigilantism @xcite .