Researchers have created an algorithm that can tell—with 85 percent accuracy—whether a Twitter account is home to a bot or (worse) a corporate shill.
You know Twitter spam when you see it—but wouldn’t it be nice if you didn’t have to see it? Unfortunately, email-style filters, which analyze message contents, are of little help. Due to the rigors of 140-character communication, even legitimate tweets tend to read like Nigerian phishing scams, while the hucksters often hide their pitches in links. So Twitter simply puts the onus on users to report offending accounts.
But a fascinating recent study from Imperial College London suggests a new approach. Borrowing some tricks from computational neuroscience, coauthors Gabriela Tavares and Aldo Faisal have come up with an algorithm that can tell—with 85 percent accuracy—whether a Twitter account is home to a bot or (worse) a corporate shill instead of a regular person.
It’s all in the timing. By analyzing the timestamps on 165,000 tweets, the researchers found that these three user types—individuals, companies, and robots—have very distinct activity patterns. Think of it as temporal fingerprinting. The approach could eventually be used to create more effective filters for all kinds of social networks.