arXiv subjects

Francesca Stevens

Publications and source records attributed to Francesca Stevens.

4 recordsLinked to original sources

arXiv2024

Gendered Inequalities in Online Harms: Fear, Safety Work, and Online Participation

Online harms, such as hate speech, trolling and self-harm promotion, continue to be widespread. There are growing concerns that these harms may disproportionately affect women, reflecting and reproducing existing structural inequalities within digital spaces. Using a nationally representative survey of UK adults (N=1992), we examine how gender shapes exposure to a variety of online harms, fears surrounding being targeted, the psychological impact of online experiences, the use of safety tools, and comfort with various forms of online participation. We find that while men and women report roughly similar levels of absolute exposure to harmful content online, women are more often targeted by contact-based harms including image-based abuse, cyberstalking and cyberflashing. Women report heightened fears about being targeted by online harms, more negative psychological impact in response to online experiences, and increased use of safety tools, reflecting more engagement with personal safety work. Importantly, women also say they are significantly less comfortable with several forms of online participation, for example just 23% of women are comfortable expressing political views online compared to 40% of men. Explanatory models show direct associations between fears surrounding harms and comfort with particular online behaviours. Our findings show how online harms reinforce gender inequality by placing disproportionate psychological burden and participation constraints on women. These results are important because with much public discourse happening online, we must ensure all members of society feel safe and able to participate in online spaces.

cs.CY↗

arXiv2024

Understanding gender differences in experiences and concerns surrounding online harms: A short report on a nationally representative survey of UK adults

Online harms, such as hate speech, misinformation, harassment and self-harm promotion, continue to be widespread. While some work suggests that women are disproportionately affected by such harms, other studies find little evidence for gender differences in overall exposure. Here, we present preliminary results from a large, nationally representative survey of UK adults (N = 2000). We asked about exposure to 15 specific harms, along with fears surrounding exposure and comfort engaging in certain online behaviours. While men and women report seeing online harms to a roughly equal extent overall, we find that women are significantly more fearful of experiencing every type of harm that we asked about, and are significantly less comfortable partaking in several online behaviours. Strikingly, just 24% of women report being comfortable expressing political opinions online compared with almost 40% of men, with similar overall proportions for challenging certain content. Our work suggests that women may suffer an additional psychological burden in response to the proliferation of harmful online content, doing more 'safety work' to protect themselves. With much public discourse happening online, gender inequality in public voice is likely to be perpetuated if women feel too fearful to participate. Our results are important because to establish greater equality in society, we must take measures to ensure all members feel safe and able to participate in the online space.

cs.CY↗

arXiv2024

Understanding engagement with platform safety technology for reducing exposure to online harms

User facing 'platform safety technology' encompasses an array of tools offered by platforms to help people protect themselves from harm, for example allowing people to report content and unfollow or block other users. These tools are an increasingly important part of online safety: in the UK, legislation has made it a requirement for large platforms to offer them. However, little is known about user engagement with such tools. We present findings from a nationally representative survey of UK adults covering their awareness of and experiences with seven common safety technologies. We show that experience of online harms is widespread, with 67% of people having seen what they perceived as harmful content online; 26% of people have also had at least one piece of content removed by content moderation. Use of safety technologies is also high, with more than 80\% of people having used at least one. Awareness of specific tools is varied, with people more likely to be aware of 'post-hoc' safety tools, such as reporting, than preventative measures. However, satisfaction with safety technologies is generally low. People who have previously seen online harms are more likely to use safety tools, implying a 'learning the hard way' route to engagement. Those higher in digital literacy are also more likely to use some of these tools, raising concerns about the accessibility of these technologies to all users. Additionally, women are more likely to engage in particular types of online 'safety work'. We discuss the implications of our results for those seeking a safer online environment.

cs.CY↗

arXiv2023

DoDo Learning: DOmain-DemOgraphic Transfer in Language Models for Detecting Abuse Targeted at Public Figures

Public figures receive a disproportionate amount of abuse on social media, impacting their active participation in public life. Automated systems can identify abuse at scale but labelling training data is expensive, complex and potentially harmful. So, it is desirable that systems are efficient and generalisable, handling both shared and specific aspects of online abuse. We explore the dynamics of cross-group text classification in order to understand how well classifiers trained on one domain or demographic can transfer to others, with a view to building more generalisable abuse classifiers. We fine-tune language models to classify tweets targeted at public figures across DOmains (sport and politics) and DemOgraphics (women and men) using our novel DODO dataset, containing 28,000 labelled entries, split equally across four domain-demographic pairs. We find that (i) small amounts of diverse data are hugely beneficial to generalisation and model adaptation; (ii) models transfer more easily across demographics but models trained on cross-domain data are more generalisable; (iii) some groups contribute more to generalisability than others; and (iv) dataset similarity is a signal of transferability.

cs.CL↗