Can We Automate the Analysis of Online Child Sexual Exploitation Discourse?

• URL: http://arxiv.org/abs/2209.12320v1
• Date: Sun, 25 Sep 2022 21:18:50 GMT
• Title: Can We Automate the Analysis of Online Child Sexual Exploitation Discourse?
• Authors: Darren Cook, Miri Zilka, Heidi DeSandre, Susan Giles, Adrian Weller, Simon Maskell
• Abstract summary: Social media's growing popularity raises concerns around children's online safety. Research into online sexual grooming has often relied on domain experts to manually annotate conversations. We test how well-automated methods can detect conversational behaviors and replace an expert human annotator.
• Score: 18.20420363291303
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Social media's growing popularity raises concerns around children's online safety. Interactions between minors and adults with predatory intentions is a particularly grave concern. Research into online sexual grooming has often relied on domain experts to manually annotate conversations, limiting both scale and scope. In this work, we test how well-automated methods can detect conversational behaviors and replace an expert human annotator. Informed by psychological theories of online grooming, we label $6772$ chat messages sent by child-sex offenders with one of eleven predatory behaviors. We train bag-of-words and natural language inference models to classify each behavior, and show that the best performing models classify behaviors in a manner that is consistent, but not on-par, with human annotation.

Related papers

• A Fuzzy Evaluation of Sentence Encoders on Grooming Risk Classification [5.616884466478886]
  Children are becoming increasingly vulnerable to the risk of grooming in online settings. Predators evade detection using indirect and coded language. Previous studies have fine-tuned Transformers to automatically identify grooming in chat conversations.
  arXiv  Detail & Related papers  (2025-02-18T06:26:46Z)
• A Survey on Pedophile Attribution Techniques for Online Platforms [0.0]
  We provide a review of the methods of pedophile attribution used in social media platforms. We examine the effect of the size of the suspect set and the length of the text on the task of attribution. We find that few studies have proposed tools to mitigate the risk of online sexual predators.
  arXiv  Detail & Related papers  (2025-01-14T18:25:07Z)
• A Turing Test: Are AI Chatbots Behaviorally Similar to Humans? [19.50537882161282]
  ChatGPT-4 exhibits and personality traits that are statistically indistinguishable from a random human subjects. Their behaviors are often distinct from average and human behaviors. We estimate that they act as if they are maximizing an average of their own and partner's payoffs.
  arXiv  Detail & Related papers  (2023-11-19T16:44:09Z)
• Learning to Influence Human Behavior with Offline Reinforcement Learning [70.7884839812069]
  We focus on influence in settings where there is a need to capture human suboptimality. Experiments online with humans is potentially unsafe, and creating a high-fidelity simulator of the environment is often impractical. We show that offline reinforcement learning can learn to effectively influence suboptimal humans by extending and combining elements of observed human-human behavior.
  arXiv  Detail & Related papers  (2023-03-03T23:41:55Z)
• Understanding How People Rate Their Conversations [73.17730062864314]
  We conduct a study to better understand how people rate their interactions with conversational agents. We focus on agreeableness and extraversion as variables that may explain variation in ratings.
  arXiv  Detail & Related papers  (2022-06-01T00:45:32Z)
• Aligning to Social Norms and Values in Interactive Narratives [89.82264844526333]
  We focus on creating agents that act in alignment with socially beneficial norms and values in interactive narratives or text-based games. We introduce the GALAD agent that uses the social commonsense knowledge present in specially trained language models to contextually restrict its action space to only those actions that are aligned with socially beneficial values.
  arXiv  Detail & Related papers  (2022-05-04T09:54:33Z)
• How Do Pedophiles Tweet? Investigating the Writing Styles and Online Personas of Child Cybersex Traffickers in the Philippines [0.0]
  We investigate how child sex peddlers spread illegal pornographic content and target minors for sexual activities on Twitter in the Philippines. Results of our studies show frequently used and co-occurring words that traffickers use to spread content as well as four main roles played by these entities that contribute to the proliferation of child pornography in the country.
  arXiv  Detail & Related papers  (2021-07-21T05:26:52Z)
• Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data [74.60507696087966]
  Mental health conditions remain underdiagnosed even in countries with common access to advanced medical care. One promising data source to help monitor human behavior is daily smartphone usage. We study behavioral markers of daily mood using a recent dataset of mobile behaviors from adolescent populations at high risk of suicidal behaviors.
  arXiv  Detail & Related papers  (2021-06-24T17:46:03Z)
• Analysing Cyberbullying using Natural Language Processing by Understanding Jargon in Social Media [4.932130498861987]
  In our work, we explore binary classification by using a combination of datasets from various social media platforms. We experiment through multiple models such as Bi-LSTM, GloVe, state-of-the-art models like BERT, and apply a unique preprocessing technique by introducing a slang-abusive corpus.
  arXiv  Detail & Related papers  (2021-04-23T04:20:19Z)
• Can You be More Social? Injecting Politeness and Positivity into Task-Oriented Conversational Agents [60.27066549589362]
  Social language used by human agents is associated with greater users' responsiveness and task completion. The model uses a sequence-to-sequence deep learning architecture, extended with a social language understanding element. Evaluation in terms of content preservation and social language level using both human judgment and automatic linguistic measures shows that the model can generate responses that enable agents to address users' issues in a more socially appropriate way.
  arXiv  Detail & Related papers  (2020-12-29T08:22:48Z)
• I love your chain mail! Making knights smile in a fantasy game world: Open-domain goal-oriented dialogue agents [69.68400056148336]
  We train a goal-oriented model with reinforcement learning against an imitation-learned chit-chat'' model. We show that both models outperform an inverse model baseline and can converse naturally with their dialogue partner in order to achieve goals.
  arXiv  Detail & Related papers  (2020-02-07T16:22:36Z)

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.