Cyberbully Algorithm: Will It Work?

November 1, 2019

Given the paradoxes of human expression, teaching algorithms to identify harmful speech on social media has proven a difficult task. One group of researchers, though, has made a breakthrough—EurekAlert declares, “New Algorithms Can Distinguish Cyberbullies from Normal Twitter Users with 90% Accuracy.” The news release explains:

“Effective tools for detecting harmful actions on social media are scarce, as this type of behavior is often ambiguous in nature and/or exhibited via seemingly superficial comments and criticisms. Aiming to address this gap, a research team featuring Binghamton University computer scientist Jeremy Blackburn analyzed the behavioral patterns exhibited by abusive Twitter users and their differences from other Twitter users. ‘We built crawlers — programs that collect data from Twitter via variety of mechanisms,’ said Blackburn. ‘We gathered tweets of Twitter users, their profiles, as well as (social) network-related things, like who they follow and who follows them.’ The researchers then performed natural language processing and sentiment analysis on the tweets themselves, as well as a variety of social network analyses on the connections between users. The researchers developed algorithms to automatically classify two specific types of offensive online behavior, i.e., cyberbullying and cyberaggression. The algorithms were able to identify abusive users on Twitter with 90 percent accuracy. These are users who engage in harassing behavior, e.g. those who send death threats or make racist remarks to users.”

Of course, 90 percent accuracy means 10 percent slips through, so we still have a way to go. Also, for a bully to be detected, they have to have already acted badly, and no algorithm can undo that damage. Blackburn says his team is working on “pro-active mitigation techniques” that could help. I am curious to see what that will look like. Stay tuned.

Cynthia Murrell, November 1, 2019

Written by Stephen E. Arnold · Filed Under News, Text processing

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Stephen E. Arnold monitors search, content processing, text mining and related topics from his high-tech nerve center in rural Kentucky. He tries to winnow the goose feathers from the giblets. He works with colleagues worldwide to make this Web log useful to those who want to go "beyond search". Contact him at sa [at] arnoldit.com. His Web site with additional information about search is arnoldit.com.