Social media has completely changed how modern societies interact. It is far easier for bad actors to communicate, and that’s a problem for families, government and businesses. With the large volume of communications out on the internet, it’s just about impossible to filter out the communications that cause concern without completely slowing down the system or heavily controlling it as do some more rigid governments. Artificial intelligence (AI) has a number of techniques that can help mitigate those risks.
The first, and most obvious challenge is that of language. Natural language processing (NLP) is the AI tool for understanding words. Language is complex and deep learning systems have made major advances in understanding, but there’s still work to be done. Advancements in real-time processing enable AI to analyze and sort large volumes of data quickly, improving the identification of problematic content. This capability helps maintain efficient systems while effectively highlighting potential threats without unnecessary delays. Real-time processing ensures that AI remains responsive and able to adapt to the fast-paced dynamics of social media environments. One of the challenges is sarcasm. When speaking, most people can understand when another person is sarcastic. That’s done through listening to the tone of the words. That tone doesn’t exist in most typed text so what becomes harder for people becomes even harder for machines.
Then there’s the large use in social media of images and emojis. Understanding images in the concept of threats is difficult. Vision, a key subsegment of AI, is a technology arena that is working to better understand images.
Another social media challenge is analyzing the large volumes of messages coming from multiple sources. Identifying the same person, groups, or message style on Facebook, Instagram, reddit and a host of other sources of information is critical for providing AI plays a significant role in identifying bots on social media by recognizing patterns typical of automated accounts. Machine learning algorithms assess account behaviors, like posting frequency and interaction consistency, providing an effective means to filter out non-authentic engagements. This helps maintain the integrity of communications on platforms, ensuring a more genuine experience for users.
