Challenge: Despite substantial efforts to reduce gender disparities in online social contexts, gender gaps persist and negatively affect women through online harassment.
Approach: They propose a new dataset and method for identifying supportive replies and new methods for inferring gender from text and name to examine the disparity in support across millions of online interactions.
Outcome: The proposed model shows that identifying as a woman is associated with higher rates of support, but also higher rates disparagement.

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Modeling the Differential Prevalence of Online Supportive Interactions in Private Instant Messages of Adolescents (2025.findings-naacl)

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Challenge: Approximately two-thirds (68%) of American teenagers aged 13-17 have reported that social media make them feel as though they have people who will support them during challenging times.
Approach: They propose to use the Social Support Behavioral Code to detect and model gender-based and pair-or-group disparities in online supportive interactions among adolescents.
Outcome: The proposed model can be used to model gender-based and pair-or-group disparities in supportive interactions among adolescents.
RtGender: A Corpus for Studying Differential Responses to Gender (L18-1)

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Challenge: Prior work on linguistic gender difference and communications about gender has focused on language about or portraying persons of a particular gender.
Approach: They present a multi-genre corpus of 25M comments from five socially and topically diverse sources tagged for the gender of the addressee and 30k annotations for sentiment and relevance of these responses.
Outcome: The proposed dataset shows that responses to women are more emotive and about the speaker as an individual (rather than about the content being responded to).
Gender and Racial Fairness in Depression Research using Social Media (2021.eacl-main)

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Challenge: Existing studies show that social media behavior can indicate mental health of an individual . previous studies have raised concerns about possible biases in models produced from such data, but no study has investigated how these biase recur with demographic groups.
Approach: They analyze the fairness of depression classifiers trained on Twitter data with respect to gender and racial/ethnic demographic groups.
Outcome: The proposed model performs better for gender and racial/ethnic groups than other models and provides recommendations on how to avoid biases in future research.
Condolence and Empathy in Online Communities (2020.emnlp-main)

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Challenge: Using computational tools, we examine the dynamics of condolence online.
Approach: They develop computational tools to analyze 11.4M distress expressions and 2.8M condolence offerings in a massive dataset of 11.4 million people.
Outcome: The proposed model reveals that condolence features differ from those seen in interpersonal settings and that the features of condolance individuals find most helpful differ from the features seen in social media.
Modeling Users and Online Communities for Abuse Detection: A Position on Ethics and Explainability (2021.findings-emnlp)

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Challenge: Abuse on the Internet is an important societal problem of our time.
Approach: They propose to use user and community information to enhance detection of abusive language . they propose to propose properties that an explainable method should aim to exhibit .
Outcome: The proposed methods leverage user and community information to enhance detection of abusive language.
Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation (2021.findings-emnlp)

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Challenge: Recent studies have found evidence of gender bias in machine translation and coreference resolution models using mostly synthetic diagnostic datasets.
Approach: They propose a semi-automatic method to vastly extend synthetic, small diagnostic datasets to include grammatical patterns indicating stereotypical and non-stereotypical gender-role assignments.
Outcome: The proposed method extends the existing dataset to 108K diverse English sentences.
An Expert Annotated Dataset for the Detection of Online Misogyny (2021.eacl-main)

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Challenge: Existing studies have found that misogynistic content is pervasive on some Reddit communities, but a training dataset for misogorical classification has not been created with the data.
Approach: They propose a hierarchical taxonomy and an expert labelled dataset to enable automatic classification of online misogynistic content.
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A Just and Comprehensive Strategy for Using NLP to Address Online Abuse (P19-1)

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Challenge: Current methods to detect online abuse focus on a narrow definition of abuse to detriment of victims seeking validation and solutions.
Approach: They argue that the NLP community needs to make three substantive changes to tackle both more subtle and more serious forms of abuse.
Outcome: The proposed approach would address the problem of abuse in a more inclusive and productive way.
Robustness and Reliability of Gender Bias Assessment in Word Embeddings: The Role of Base Pairs (2020.aacl-main)

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Challenge: Existing methods to quantify gender bias in word embeddings are not robust and cannot identify common types of bias.
Approach: They propose to quantify gender bias by using cosine similarity to a pair of gender words and using analogies.
Outcome: The proposed methods are not robust and cannot identify common types of bias, while analogies are unsuitable indicators.
Toward Gender-Inclusive Coreference Resolution (2020.acl-main)

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Challenge: a recent study shows that coreference resolution systems can be harmful to binary and non-binary trans and cis stakeholders.
Approach: They propose to use gender-based crowd annotations to investigate coreference resolution biases . they use a dataset to examine the complexity of gender in crowd annotation systems .
Outcome: a new study shows that without acknowledging and building systems that recognize gender, we build systems that lead to many potential harms.

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