Social media platforms use AI in content moderation to address the increasing volume of user-generated content. AI algorithms can analyze and filter large amounts of data in real-time, allowing platforms to maintain a safe and respectful online environment for their users[1]. The use of AI has enabled platforms to remove a significant portion of violating content before it is even viewed by a human[1]. For example, Instagram uses a combination of AI algorithms and human moderators to filter and remove content that violates its community standards, such as hate speech, bullying, and nudity[1]. AI can automatically analyze texts, visuals, and videos for toxic content, filter and classify inappropriate content, and help prevent it from being published[2]. AI-based content moderation offers benefits such as scalability, speed, and the ability to identify and remove harmful content more accurately[1]. However, there are challenges associated with AI content moderation, including the potential for bias and the need for human oversight to handle complex or sensitive cases[1].
AI content moderation is based on machine learning algorithms that use natural language processing (NLP) and platform-specific data to detect inappropriate user-generated content[3]. It can help relieve human moderators from repetitive and unpleasant tasks, improve safety for users, and streamline overall operations[2]. AI can also assist human moderators by filtering suspicious content for human review, thus making the content moderation process more efficient[2].
Hateful Attack Types
Some common types of hateful attacks in online communities include hate speech, cyberbullying, online harassment, and online hate speech. Hate speech often targets minority groups based on various characteristics such as race, ethnicity, religion, or sexual orientation[6][7]. Cyberbullying involves systematically attacking individuals through electronic channels, such as name-calling and shaming[8]. Online harassment encompasses predatory and oppressive behavior on the Internet, including sending threatening or offensive messages[8]. Online hate speech, which fulfills the legal definition of hate speech, is also a prevalent form of online attack, often targeting specific groups or individuals with the intention to offend, discriminate, or abuse based on certain characteristics[8]. These types of attacks can have serious real-life consequences and are a form of gender-based violence[10].
To detect these types of attacks, it’s important to be aware of the language used and the impact it has on the targeted individuals or groups. Hate speech and online hate speech often involve discriminatory or offensive language targeting specific characteristics of individuals or groups. Cyberbullying and online harassment typically involve repeated and harmful behavior aimed at causing distress to the target. Recognizing the intent and impact of the communication is crucial in identifying these harmful online behaviors.
Testing It
For this test we quickly and off the cuff created 10 statements, some of which contained hateful attacks and some of which contained friendly jibes. Could AI tell the difference? Here’s the result:
1. Mister Disgusto is our local cyber-troll who has nothing better to do than insult people.
- AI: This is an insult needing moderation. It’s a negative characterization of someone and should be addressed with moderation.
2. I love the way you look like a trashcan filled with beautiful rotting fish
- AI: This is an insult needing moderation. The statement is clearly intended to be hurtful and should be addressed accordingly.
3. Please go away and get some sleep.
- AI: This can be seen as a friendly jibe. While it’s a bit direct, the underlying message seems to be one of concern for the person’s well-being.
4. Wow. You really know how to tell a killer smile from a killer grin.
- AI: This can be seen as a friendly jibe. It’s a playful comment on someone’s expression and can be taken in good humor.
5. Screw you buddy, if you come anywhere near me I will jack you up and it will be on TV.
- AI: This is an insult needing moderation. The language used is aggressive and threatening, and should be addressed with moderation.
6. I have the right screw for you buddy, if you come over I will help you repair your TV set.
- AI: This can be seen as a friendly jibe. It’s a lighthearted play on words and can be interpreted as a friendly offer of assistance.
7. You smell as good as you type.
- AI: This can be seen as a friendly jibe. It’s a playful comment that may be intended as a lighthearted joke.
8. I’m going to sue you and you will lose everything.
- AI: This is an insult needing moderation. The threat of legal action and potential harm is serious and should be addressed with moderation.
9. Stop acting like a soup Nazi about what type of carbohydrates I should consume.
- AI: This can be seen as a friendly jibe. It’s a playful reference to a popular culture term and may be intended as a lighthearted comment.
10. Your relatives were Nazis and you still think you can control everyone, har!
- AI: This is an insult needing moderation. The statement contains offensive language and should be addressed with moderation.
Results Discussion
I’m a bit surprised. No mistakes from my point of view. What else is there to say? In the real world, things get a lot more complicated. Perhaps 100 statements would be a more fair test, but in this one simple test case an AI language model was capable as an objective front line moderator of human generated sentences.
Conclusion
In conclusion, social media platforms use AI in content moderation to handle the growing volume of user-generated content. While AI offers benefits such as scalability and speed, it also presents challenges that require human oversight to ensure fair and effective content moderation.
Citations:
[1] https://aicontentfy.com/en/blog/role-of-ai-in-content-moderation-and-censorship
[2] https://www.forbes.com/sites/forbestechcouncil/2022/06/14/the-growing-role-of-ai-in-content-moderation/?sh=58d6e4984a17
[3] https://www.techtarget.com/searchcontentmanagement/tip/Types-of-AI-content-moderation-and-how-they-work
[4] https://mailchimp.com/resources/ai-content-moderation/
[5] https://labelyourdata.com/articles/ai-content-moderation
[6] https://www.un.org/en/hate-speech/impact-and-prevention/targets-of-hate
[7] https://www.cfr.org/backgrounder/hate-speech-social-media-global-comparisons
[8] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6763199/
[9] https://www.sciencedirect.com/science/article/abs/pii/S2352250X21002505
[10] https://canadianwomen.org/the-facts/online-hate-and-cyberviolence/
