Can Machine Learning Help Identify Radicalization Among Students?

Student radicalization on college campuses is a growing concern, with the same processes happening even in high schools now. Education institutions often are wonderful places for lively discussion about the world, however at times students can become ill informed via online spaces. While computers are supposed to be used in such institutions for educational purposes, students often surf the web there. There are also cases where personal devices (laptops and mobile devices) are used at school for browsing activity. What would seem like a harmless activity may be isolating, polarizing, and radicalizing students.

Can Machine Learning Help Identify Radicalization Among Students?

Numerous studies over the years have concluded the speed at which people can become polarized online. Researchers from the Kellogg School of Management in collaboration with the IMT School for Advanced Studies Lucca produced a comprehensive study of Facebook and YouTube users. What they discovered was the speed at which people became polarized, which was often at their 50th like or comment. YouTube and Facebook are just two places students visit and fall into ideological silos. Twitter, Reddit, Tumblr, and many other social spaces are breeding grounds for radicalization. Thankfully as technology is advancing, identifying and preventing radicalization may become more streamlined. >> Click to Continue reading on Education Technology Insights

Isaac Kohen

Isaac Kohen

Isaac Kohen started out in quantitative finance by programming trading algorithms at a major hedge fund. His time spent in the financial world and exposure to highly sensitive information triggered his curiosity for IT security. He worked as an IT security consultant for several years where he spearheaded efforts to secure the IT infrastructure of companies with masses of confidential data. When Isaac first entered the industry, IT norms were to prohibit and lock out as many people as possible to protect data. He found that this was a very ineffective way of solving the issue because it made it hard for many people who wanted to cause no harm, to do their jobs. He decided to focus on algorithms targeting user behavior to find outliers within the companies he consulted with to help detect insider threats.

Isaac can be contacted at ikohen@teramind.co

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