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Internet security and technology policy research regularly uses technical indicators of abuse in order to identify culprits and to tailor mitigation strategies. As a major obstacle, readily available data are often misaligned with actual…
We present an analysis of the representation of gender as a data dimension in data visualizations and propose a set of considerations around visual variables and annotations for gender-related data. Gender is a common demographic dimension…
The increased proliferation of abusive content on social media platforms has a negative impact on online users. The dread, dislike, discomfort, or mistrust of lesbian, gay, transgender or bisexual persons is defined as…
Have we become more tolerant of dating people of different social backgrounds compared to ten years ago? Has the rise of online dating exacerbated or alleviated gender inequalities in modern courtship? Are the most attractive people on…
In healthcare, thousands of safety incidents occur every year, but learning from these incidents is not effectively aggregated. Analysing incident reports using AI could uncover critical insights to prevent harm by identifying recurring…
Hate speech is ubiquitous on the Web. Recently, the offline causes that contribute to online hate speech have received increasing attention. A recurring question is whether the occurrence of extreme events offline systematically triggers…
Online social media platforms are turning into the prime source of news and narratives about worldwide events. However,a systematic summarization-based narrative extraction that can facilitate communicating the main underlying events is…
Online sexism has become an increasing concern in social media platforms as it has affected the healthy development of the Internet and can have negative effects in society. While research in the sexism detection domain is growing, most of…
The past two decades have seen an upsurge of interest in the collective behaviors of complex systems composed of many agents entrained to each other and to external events. In this paper, we extend concepts of entrainment to the dynamics of…
The COVID-19 pandemic has accentuated socioeconomic disparities across various racial and ethnic groups in the United States. While previous studies have utilized traditional survey methods like the Household Pulse Survey (HPS) to elucidate…
The automatic detection of hate speech online is an active research area in NLP. Most of the studies to date are based on social media datasets that contribute to the creation of hate speech detection models trained on them. However, data…
Online social networks have become a crucial medium to disseminate the latest political, commercial, and social information. Users with high visibility are often selected as seeds to spread information and affect their adoption in target…
In recent years, cognitive and mental health (CMH) disorders have increasingly become an important challenge for global public health, especially the suicide problem caused by multiple factors such as social competition, economic pressure…
An-ever increasing number of social media websites, electronic newspapers and Internet forums allow visitors to leave comments for others to read and interact. This exchange is not free from participants with malicious intentions, which do…
Online interpersonal harm, such as cyberbullying and sexual harassment, remains a pervasive issue on social media platforms. Traditional approaches, primarily content moderation, often overlook survivors' needs and agency. We introduce…
In emergency medicine, timely intervention for patients at risk of suicide is often hindered by delayed access to specialised psychiatric care. To bridge this gap, we introduce a speech-based approach for automatic suicide risk assessment.…
With the widespread online social networks, hate speeches are spreading faster and causing more damage than ever before. Existing hate speech detection methods have limitations in several aspects, such as handling data insufficiency,…
Fear is a universal concept; people crave it in urban legends, scary movies, and modern stories. Open questions remain, however, about why these stories are scary and more generally what scares people. In this study, we explore these…
The increasing integration of machine learning algorithms in daily life underscores the critical need for fairness and equity in their deployment. As these technologies play a pivotal role in decision-making, addressing biases across…
Thousands of documents are made available to the users via the web on a daily basis. One of the most extensively studied problems in the context of such document streams is burst identification. Given a term t, a burst is generally…