New AI algorithms can spot online trolls: Study

New artificial intelligence (AI) algorithms can monitor online social media conversations as they evolve, which could lead to an effective and automated way to spot online trolling in the future, according to researchers, including those of Indian-origin.

Prevention of online harassment requires rapid detection of offensive, harassing, and negative social media posts, which in turn requires monitoring online interactions.

Current methods to obtain such social media data are either fully automated, and not interpretable or rely on a static set of keywords, which can quickly become outdated.

Neither method is very effective, according to Maya Srikanth, from California Institute of Technology (Caltech) in the US.

"It isn't scalable to have humans try to do this work by hand, and those humans are potentially biased," Srikanth said.

"On the other hand, keyword searching suffers from the speed at which online conversations evolve. New terms crop up and old terms change meaning, so a keyword that was used sincerely one day might be meant sarcastically the next," she said.

The team, including Anima Anandkumar from Caltech, used GloVe (Global Vectors for Word Representation) model that uses machine-learning algorithms to discover new and relevant keywords.

Machine learning is an application of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

GloVe is a word-embedding model, meaning that it represents words in a vector space, where the "distance" between two words is a measure of their linguistic or semantic similarity.

Starting with one keyword, this model can be used to find others that are closely related to that word to reveal clusters of relevant terms that are actually in use.

For example, searching Twitter for uses of "MeToo" in conversations yielded clusters of related hashtags like "SupportSurvivors," "ImWithHer," and "NotSilent."
For example, in an online Reddit forum dedicated to misogyny, the word "female" was used in close association with the words "sexual," "negative," and "intercourse."
In Twitter posts about the #MeToo movement, the word "female" was more likely to be associated with the terms "companies," "desire," and "victims."