How Do Pedophiles Tweet? Investigating the Writing Styles and Online Personas of Child Cybersex Traffickers in the Philippines (original) (raw)
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IEEE Access, 2020
Human trafficking is a global problem that strips away the dignity of millions of victims. Currently, social networks are used to spread this crime through the online environment by using covert messages that serve to promote these illegal services. In this context, since law enforcement resources are limited, it is vital to automatically detect messages that may be related to this crime and could also serve as clues. In this paper, we identify Twitter messages that could promote these illegal services and exploit minors by using natural language processing. The images and the URLs found in suspicious messages were processed and classified by gender and age group, so it is possible to detect photographs of people under 14 years of age. The method that we used is as follows. First, tweets with hashtags related to minors are mined in real-time. These tweets are preprocessed to eliminate noise and misspelled words, and then the tweets are classified as suspicious or not. Moreover, geometric features of the face and torso are selected using Haar models. By applying Support Vector Machine (SVM) and Convolutional Neural Network (CNN), we are able to recognize gender and age group, taking into account torso information and its proportional relationship with the head, or even when the face details are blurred. As a result, using the SVM model with only torso features has a higher performance than CNN. INDEX TERMS CNN, features detection, image classification, natural language processing, SVM.
Detection of pedophilia content online: A case study using Telegram
Iraqi Journal for Computer Science and Mathematics , 2022
Users of social media can consume a wide range of subjects and information, including pornography. Pornography usage as well as the difficulties linked with this sort of content have increased over time, particularly among teens. Now, another sort of pornographic content is popular: child pornography (CP). Controlling online CP has always been a difficult task for the international community. The introduction, growth, and use of information and communication technologies coincide with an increase in illegal activities. In terms of cyberspace, children's huge online presence as well as the emergence of CP as a business compel all governments to enact tough legislation and unite worldwide to combat this issue. Is social media assisting in the dissemination of this content? To understand this issue further, a study conducted in 2021 using Telegram data on the consumption of CP followed by a series of analyses show Telegram's potential effect on the spread and consumption of this type of content.
Supporting Law Enforcement in Digital Communities through Natural Language Analysis
Lecture Notes in Computer Science, 2008
Recent years have seen an explosion in the number and scale of digital communities (e.g. peer-to-peer file sharing systems, chat applications and social networking sites). Unfortunately, digital communities are host to significant criminal activity including copyright infringement, identity theft and child sexual abuse. Combating this growing level of crime is problematic due to the ever increasing scale of today's digital communities. This paper presents an approach to provide automated support for the detection of child sexual abuse related activities in digital communities. Specifically, we analyze the characteristics of child sexual abuse media distribution in P2P file sharing networks and carry out an exploratory study to show that corpus-based natural language analysis may be used to automate the detection of this activity. We then give an overview of how this approach can be extended to police chat and social networking communities.
Advanced Data Preprocessing for Detecting Cybercrime in Text-Based Online Interactions
Pattern Recognition and Artificial Intelligence, 2020
Social media provides a powerful platform for individuals to communicate globally. This capability has many benefits, but it can also be used by malevolent individuals, i.e. predators. Anonymity exacerbates the problem. The motivation of our work is to help protect our children from this potentially hostile environment, without excluding them from utilizing its benefits. In our research, we aim to develop an online sexual predator identification system, designed to detect cybercrime related to child grooming. We will use AI techniques to analyze chat interactions available from different social networks. However, before any meaningful analysis can be carried out, chats must be preprocessed into a consistent and suitable format. This task poses challenges in itself. In this paper we show how different and diverse chat formats can be automatically normalized into a consistent text-based format that can be subsequently used for analysis.
Don’t Want to Get Caught? Don’t Say It: The Use of EMOJIS in Online Human Sex Trafficking Ads
Proceedings of the 51st Hawaii International Conference on System Sciences, 2018
Technology has dramatically changed the way criminals conduct their illicit activities. Specifically, the Internet has become a major facilitator of online human sex trafficking. Traffickers are using these technologies to market their victims which presents new challenges for efforts to combat sex trafficking. This study used knowledge management principles and natural language processing methods to develop an improved ontology of online sex trafficking ads. The language of these ads is constantly evolving; therefore, this study explored the role of a new type of indicator, emoticons, to the ontology of human trafficking indicators.
Detecting the Usage of Vulgar Words in Cyberbully Activities from Twitter
International Journal on Advanced Science, Engineering and Information Technology, 2020
Nowadays, nearly all people utilize the device which is connected to Internet. People are accustomed to the use information technology devices in their daily life to interact with other people. Currently, many social media platforms such as Facebook, Twitter, Instagram, and YouTube are becoming popular. This study selected Twitter platforms, which is started to gain popularity. By the rapid growth of users signing up for Twitter accounts, at the same time, cybercrime started to bloom each year in social media platforms. Cyberbully is one of the cybercrime practices which had caused a significant impact on the targeted victims. The victims experienced social pressure, which they need to bear each day while the bullies stayed free behind the veil of anonymity. This study aims to identify the common vulgar words used by the cyberbullies on Twitter. Also, this study is subject to produce essential features of Twitter based on the collected tweets. The evaluation in this study includes the occurrences of the vulgar word perpetrated by the cyberbullies from Twitter. This study detected the usage of vulgar words in cyberbully activities on Twitter platform. A list of vulgar words were extracted and evaluated from a corpus of 50 Twitter users who posted a various number of tweets. The vulgar words detection in the tweets enable the tracking process of the cyberbully activities. In the evaluation section, we discussed how the usage of the vulgar words would define the user's earnestness in doing the cyberbully activities in the Twitter. This study shows there are users with a low number of tweets have a high number of vulgar words occurrences, while other users with high numbers of tweets but less number of vulgar words occurrences. The information collected in this study is expected to assist marking users with a high number of vulgar words occurrences who tend to have high possibilities in doing cyber-bully activities.
Uncovering Indicators of Commercial Sexual Exploitation
Journal of Interpersonal Violence, 2017
It is estimated that annually 100,000 to 300,000 youth are at risk for sex trafficking; a commercial sex act induced by force, fraud, or coercion, or any such act where the person induced to perform such an act is younger than 18 years of age. Increasingly, such transactions are occurring online via Internet-based sites that serve the commercial sex industry. Commercial sex transactions involving trafficking are illegal; thus, Internet discussions between those involved must be veiled. Even so, transactions around sex trafficking do occur. Within these transactions are innuendos that provide one avenue for detecting potential activity. The purpose of this study is to identify linguistic indicators of potential commercial sexual exploitation within the online comments of men posted on an Internet site. Six hundred sixty-six posts from five Midwest cities and 363 unique members were analyzed via content analysis. Three main indicators were found: the presence of youth or desire for yo...
Detecting Child Grooming Behaviour Patterns on Social Media
Online paedophile activity in social media has become a major concern in society as Internet access is easily available to a broader younger population. One common form of online child exploitation is child grooming, where adults and minors exchange sexual text and media via social media platforms. Such behaviour involves a number of stages performed by a predator (adult) with the final goal of approaching a victim (minor) in person. This paper presents a study of such online grooming stages from a machine learning perspective. We propose to characterise such stages by a series of features covering sentiment polarity, content, and psycho-linguistic and discourse patterns. Our experiments with online chatroom conversations show good results in automatically classifying chatlines into various grooming stages. Such a deeper understanding and tracking of predatory behaviour is vital for building robust systems for detecting grooming conversations and potential predators on social media.
Social media have grown up as something hallucinogenic. They offer millions of pleasures by having people's fingertips to control through smart phones. People may interact to each other for various motivations and purposes without knowing who they are talking to in fact although they know the name of the interlocutor shown in the social media account. This leads to cybercrime because people often miss to validate it. This research would like to investigate why people close their eyes to verify the person they are talking to in the social media and how the interlocutors enable to ensure that they are the same person as in the speakers thought. By having descriptive qualitative method with interview as the major for collecting data, the research results some signposts. Addressing, tone, and spelling and punctuation are linguistics features that the doer of cybercrime must have as a key to crack the security without any violence. The doer copies the way people have the account of social media to ensure the interlocutor through a private chat.