Prevalence and Impacts of Image-Based Sexual Abuse Victimization: A Multinational Study

• URL: http://arxiv.org/abs/2503.04988v1
• Date: Thu, 06 Mar 2025 21:33:39 GMT
• Title: Prevalence and Impacts of Image-Based Sexual Abuse Victimization: A Multinational Study
• Authors: Rebecca Umbach, Nicola Henry, Gemma Beard,
• Abstract summary: Image-based sexual abuse (IBSA) refers to the nonconsensual creating, taking, or sharing of intimate images.<n>This study examines prevalence of, impacts from, and responses to IBSA via a survey with over 16,000 adults in 10 countries.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image-based sexual abuse (IBSA) refers to the nonconsensual creating, taking, or sharing of intimate images, including threats to share intimate images. Despite the significant harms of IBSA, there is limited data on its prevalence and how it affects different identity or demographic groups. This study examines prevalence of, impacts from, and responses to IBSA via a survey with over 16,000 adults in 10 countries. More than 1 in 5 (22.6%) respondents reported an experience of IBSA. Victimization rates were higher among LGBTQ+ and younger respondents. Although victimized at similar rates, women reported greater harms and negative impacts from IBSA than men. Nearly a third (30.9%) of victim-survivors did not report or disclose their experience to anyone. We provide large-scale, granular, baseline data on prevalence in a diverse set of countries to aid in the development of effective interventions that address the experiences and intersectional identities of victim-survivors.

Related papers

• FaceSaliencyAug: Mitigating Geographic, Gender and Stereotypical Biases via Saliency-Based Data Augmentation [46.74201905814679]
  We present an approach named FaceSaliencyAug aimed at addressing the gender bias in computer vision models. We quantify dataset diversity using Image Similarity Score (ISS) across five datasets, including Flickr Faces HQ (FFHQ), WIKI, IMDB, Labelled Faces in the Wild (LFW), UTK Faces, and Diverse dataset. Our experiments reveal a reduction in gender bias for both CNNs and ViTs, indicating the efficacy of our method in promoting fairness and inclusivity in computer vision models.
  arXiv  Detail & Related papers  (2024-10-17T22:36:52Z)
• Understanding Help-Seeking and Help-Giving on Social Media for Image-Based Sexual Abuse [28.586678492600864]
  Image-based sexual abuse (IBSA) is a growing threat to people's digital safety. In this paper, we explore how people seek and receive help for IBSA on social media.
  arXiv  Detail & Related papers  (2024-06-18T00:23:00Z)
• Modeling the amplification of epidemic spread by individuals exposed to misinformation on social media [41.31724592098777]
  We employ an epidemic model that incorporates a large, mobility-informed physical contact network and social media data.<n>We simulate various scenarios to understand how epidemic spreading can be affected by misinformation spreading through one particular social media platform.<n>We estimate the additional portion of the U.S. population that would become infected over the course of the COVID-19 epidemic in the worst-case scenario.
  arXiv  Detail & Related papers  (2024-02-17T18:01:43Z)
• Non-Consensual Synthetic Intimate Imagery: Prevalence, Attitudes, and Knowledge in 10 Countries [0.0]
  Deepfake technologies have become ubiquitous, "democratizing" the ability to manipulate photos and videos. One popular use of deepfake technology is the creation of sexually explicit content, which can then be posted and shared widely on the internet. This article examines attitudes and behaviors related to "deepfake pornography" as a specific form of non-consensual synthetic intimate imagery (NSII)
  arXiv  Detail & Related papers  (2024-01-26T21:51:49Z)
• Into the LAIONs Den: Investigating Hate in Multimodal Datasets [67.21783778038645]
  This paper investigates the effect of scaling datasets on hateful content through a comparative audit of two datasets: LAION-400M and LAION-2B. We found that hate content increased by nearly 12% with dataset scale, measured both qualitatively and quantitatively. We also found that filtering dataset contents based on Not Safe For Work (NSFW) values calculated based on images alone does not exclude all the harmful content in alt-text.
  arXiv  Detail & Related papers  (2023-11-06T19:00:05Z)
• Centering the Margins: Outlier-Based Identification of Harmed Populations in Toxicity Detection [40.70358114333233]
  We operationalize the "margins" of a dataset by employing outlier detection to identify text about people with demographic attributes distant from the "norm" We find that model performance is consistently worse for demographic outliers, with mean squared error (MSE) between outliers and non-outliers up to 70.4% worse across toxicity types. It is also worse for text outliers, with a MSE up to 68.4% higher for outliers than non-outliers.
  arXiv  Detail & Related papers  (2023-05-24T05:15:36Z)
• Coronavirus statistics causes emotional bias: a social media text mining perspective [4.042350304426975]
  This paper proposes a deep learning model which classifies texts related to the pandemic from text data with place labels. Next, it conducts a sentiment analysis based on multi-task learning. Finally, it carries out a fixed-effect panel regression with outputs of the sentiment analysis.
  arXiv  Detail & Related papers  (2022-11-16T03:36:13Z)
• "COVID-19 was a FIFA conspiracy #curropt": An Investigation into the Viral Spread of COVID-19 Misinformation [60.268682953952506]
  We estimate the extent to which misinformation has influenced the course of the COVID-19 pandemic using natural language processing models. We provide a strategy to combat social media posts that are likely to cause widespread harm.
  arXiv  Detail & Related papers  (2022-06-12T19:41:01Z)
• Minority Stress Experienced by LGBTQ Online Communities during the COVID-19 Pandemic [7.999100019665959]
  The COVID-19 pandemic has disproportionately impacted the lives of minorities, such as members of the LGBTQ community. We use a pre-pandemic and a during-pandemic dataset to identify Twitter posts exhibiting minority stress. We find that anger words are strongly associated with minority stress during the COVID-19 pandemic.
  arXiv  Detail & Related papers  (2022-05-19T12:26:59Z)
• Country Image in COVID-19 Pandemic: A Case Study of China [79.17323278601869]
  Country image has a profound influence on international relations and economic development. In the worldwide outbreak of COVID-19, countries and their people display different reactions. In this study, we take China as a specific and typical case and investigate its image with aspect-based sentiment analysis on a large-scale Twitter dataset.
  arXiv  Detail & Related papers  (2020-09-12T15:54:51Z)
• Subclass Contrastive Loss for Injured Face Recognition [79.14062188261163]
  We address the problem of injured face recognition and propose a novel Subclass Contrastive Loss (SCL) for this task. A novel database, termed as Injured Face (IF) database, is also created to instigate research in this direction.
  arXiv  Detail & Related papers  (2020-08-05T08:30:29Z)
• CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations [85.14435479181894]
  CelebA-Spoof is a large-scale face anti-spoofing dataset. It includes 625,537 pictures of 10,177 subjects, significantly larger than the existing datasets. It contains 10 spoof type annotations, as well as the 40 attribute annotations inherited from the original CelebA dataset.
  arXiv  Detail & Related papers  (2020-07-24T04:28:29Z)
• Impact of the State of Emergency Declaration for COVID-19 on Preventive Behaviors and Mental Conditions in Japan: Difference in Difference Analysis using Panel Data [0.0]
  During the COVID-19 epidemic in Japan, Internet surveys were conducted to construct panel data. Results: The declaration led people to stay home, while also generating anger, fear, and anxiety. The effect on women was larger than that on men.
  arXiv  Detail & Related papers  (2020-05-23T08:06:36Z)
• #MeToo on Campus: Studying College Sexual Assault at Scale Using Data Reported on Social Media [71.74529365205053]
  We analyze the influence of the # trend on a pool of college followers. The results show that the majority of topics embedded in those # tweets detail sexual harassment stories. There exists a significant correlation between the prevalence of this trend and official reports on several major geographical regions.
  arXiv  Detail & Related papers  (2020-01-16T18:05:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.