Generalizable prediction of academic performance from short texts on social media
This addresses the problem of generalizable attribute prediction for educators and researchers, though it is incremental by applying existing methods to new data.
The paper tackled predicting academic performance from short social media texts, showing that a model trained on Russian VK posts could generalize to rank schools/universities and predict performance from tweets, with continuous word representations enabling interpretability.
It has already been established that digital traces can be used to predict various human attributes. In most cases, however, predictive models rely on features that are specific to a particular source of digital trace data. In contrast, short texts written by users $-$ tweets, posts, or comments $-$ are ubiquitous across multiple platforms. In this paper, we explore the predictive power of short texts with respect to the academic performance of their authors. We use data from a representative panel of Russian students that includes information about their educational outcomes and activity on a popular networking site, VK. We build a model to predict academic performance from users' posts on VK and then apply it to a different context. In particular, we show that the model could reproduce rankings of schools and universities from the posts of their students on social media. We also find that the same model could predict academic performance from tweets as well as from VK posts. The generalizability of a model trained on a relatively small data set could be explained by the use of continuous word representations trained on a much larger corpus of social media posts. This also allows for greater interpretability of model predictions.