These days you can create a comprehensive portrait of someone based on their social media activity—their posts, photos, likes, comments, and hashtags. For example, you can determine a person’s age, gender, race, personality traits, electoral preferences, and much more. But is it possible to calculate more complex, multi-dimensional characteristics, such as academic success?
Machine learning has proved that it is possible.
The new model, created by computational social scientist Ivan Smirnov of HSE University, Russia, predicts the academic success of Russian high school students with an accuracy of 94%. The model generates its predictions based on users’ distinctive vocabulary and speech patterns, and the predictions have strongly correlated with students’ Unified State Exam (USE) scores.
Ivan Smirnov has created a computer model that can distinguish high academic achievers from lower ones based on their social media posts. The prediction model uses a mathematical textual analysis that registers users’ vocabulary , characters and symbols, post length, and word length.
Every word has its own rating (a kind of IQ). Scientific and cultural topics, English words, and words and posts that are longer in length rank highly and serve as indicators of good academic performance. An abundance of emojis, words or whole phrases written in capital letters, and vocabulary related to horoscopes, driving, and military service indicate lower grades in school. At the same time, posts can be quite short—even tweets are quite informative. An article detailing the study’s results was published in the journal EPJ Data Science.
News Source: HSE
