Children's safety is now a top priority for online communities. Governments around the world have passed regulations requiring online platforms to implement effective measures to protect minors against adult content and predatory behavior. Laws like the EU’s Digital Services Act, the UK’s Online Safety Bill, and the US’ routinely amended COPPA have made child safety a moral obligation and a legal mandate.
While personal data collection and access to adult content are pretty straightforward things to disable on underage users, predatory acts like child grooming and solicitation of child sexual abuse material (CSAM) often involve more complex behavior that intentionally subverts content moderation. These acts are especially damaging to online communities and should be prioritized for detection and removal.
According to studies by the University of New Hampshire's Crimes Against Children Research Center, 1 in 7 minors have reported being contacted by an online predator and 1 in 25 have been coerced into physical contact through an online solicitation. Last year, the National Center for Missing & Exploited Children received a record-high 29 million reports of online child sexual exploitation, spurred by criminals preying on the increased presence of children on poorly moderated online channels.
Online platforms must protect minors in their communities by investing in more sophisticated means of detecting child grooming and other predatory behavior.
What is child grooming?
Child grooming is a process in which online predators normalize sexually themed communication with minors to ultimately coerce them into sexual acts. Even worse, it isn't solely confined to online environments – child grooming can escalate and lead to offline abuse if it's not stopped by platforms and reported to law enforcement authorities.
Groomers typically follow an incremental playbook to gain their victims' trust in order to exploit them:
- Befriending: Initially, predators contact the victim, sympathize with them, and support them as a friend. At this stage, their online interactions would appear to be harmless and even positive in some cases.
- Desensitizing: After befriending their victim, predators gradually introduce sexual themes into their conversations to acclimate the child to more adult-oriented topics. Such interactions may still appear harmless at this stage since it's possible to discuss sexual subjects without triggering a basic sexual content filter.
- Isolating: The predator will begin casting doubt on their victim's closest relationships, such as their parents or best friends. They'll suggest the child isn't worthy of those relationships or that those people aren't good for the child, and insist they're the only option for companionship. Chat interactions may still appear legal at this stage.
- Coercion: After gaining the child's full trust by leveraging personal info that they've learned over time, predators will pressure their victim to send footage of themselves in sexually compromising situations. Sometimes, the predator will attempt to meet the child offline to perform sexual acts. Even if caught at this stage, the grooming process will already have caused a horrific amount of harm to the child.
Online child grooming doesn't just cast an unfavorable light on the platforms involved. It also traumatizes a child for life. It's an unimaginable offense, and online platforms must be ready to properly address and stop it in their communities.
What is CSAM?
Child sexual abuse material (CSAM) is colloquially known as child pornography, although that term has been discouraged since pornography implies consensual production. It refers to any footage (Photographs, videos, etc.) that depict children engaged in sexual activity or any other sexually explicit conduct.
The creation, distribution, and possession of CSAM is illegal and considered a very serious form of child exploitation and abuse. Law enforcement agencies around the world work to identify and prosecute individuals involved in CSAM. As RAINN notes, every sexually explicit photo or video of a child is hard evidence that the child has been a victim of sexual abuse.
CSAM can either be illegally traded in online communities or solicited first-hand through grooming by child predators. Even worse, studies have shown that the majority of people who possess and distribute CSAM also commit offline sexual offenses against children. Given its severity of harm, online platforms must prioritize the detection and removal of CSAM, along with identifying users who post it and reporting them to law enforcement.
Child safety online
Since it's a more complex behavior, child grooming cannot adequately be addressed with basic content moderation technology like keyword lists or profanity filters.
Instead, it requires an AI detection system powered by large language models that have been trained on vast sets of user-generated content (UGC). This enables the AI to better parse conversational language and learn the nuances that are indicative of child grooming. Law enforcement authorities and the United Nations also have helped create global databases for AI systems to learn how to spot online child exploitation.
How does Spectrum Labs' AI detect child grooming and CSAM?
Data Vault
Spectrum Labs' child grooming detection model has been trained on the world's largest data vault of UGC from a wide range of platforms all across the globe. This helps ensure highly accurate natural language processing that can faithfully interpret online interactions.
Partnership with SOSA
Additionally, Spectrum Labs has partnered with Safe from Online Sex Abuse (SOSA) to develop AI models for recognizing the language and methods used by online predators to target children.
With SOSA’s expertise, Spectrum Labs has equipped its Contextual AI with the most extensive datasets and behavior models to recognize the subtle signs of child grooming.
CSAM Grooming, CSAM Discussion, and Underage Detection Models
Since grooming doesn't happen instantly, the CSAM detection model analyzes time, context, and metadata, which involves parsing conversations over an extended period (hours, days, or weeks) in order to recognize the telltale nuances of grooming.
Along with conversational history, Spectrum Labs considers key contextual metadata that usually includes:
- The age of the participants
- Whether a participant is underage/minor
- Mentions of personal information
- Long-term rapport-building
- Mentions of sexual content
Since all these signals, besides the adult-minor age discrepancy, could exist in normal adult conversations, age is given the highest weight in identifying a conversation as grooming.
By identifying underage/minor and sexual content signals in conversations, the CSAM models can spot discussions about posted CSAM (“uhh… she looks underage”) and identify early-stage child grooming behaviors that seek to obtain sexually explicit footage from minors. In fact, weighing the age of both the user and recipient of chat messages (alongside a full slate of relevant metadata) helps ensure that Spectrum Labs' detection model can spot grooming with utmost precision and minimal false positives or missed instances.
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Example Phrase:
“Is your mom home?”
- Example Context:
Male, 22 years old, profile less than 30 days old at 3pm on a weekday messaging a female, 9 years old, in a private chat; no prior chat history between users.
User-Level Moderation
Because child grooming and CSAM cause extraordinary harm, they must be prioritized for removal and referral to authorities. Spectrum Labs' solutions feature user-level moderation that assigns a severity score to users' behavior, so CSAM reporting can be fast-tracked to the top of moderators' queues for actioning.
From there, moderators can take actions like banning the perpetrator from their community, informing the victim and their parents that they were targeted by an online predator, and reporting the perpetrator to law enforcement authorities. Time is crucial to this process in order to minimize harm to the child and the rest of the community.
To learn more about how to protect minors on your platform, check out Spectrum Labs' product sheet.
