English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

It has already been established that digital traces can be used to predictvarious human attributes. In most cases, however, predictive models rely onfeatures that are specific to a particular source of digital trace data. Incontrast, short texts written by users $-$ tweets, posts, or comments $-$ areubiquitous across multiple platforms. In this paper, we explore the predictivepower of short texts with respect to the academic performance of their authors.We use data from a representative panel of Russian students that includesinformation about their educational outcomes and activity on a popularnetworking site, VK. We build a model to predict academic performance fromusers' posts on VK and then apply it to a different context. In particular, weshow that the model could reproduce rankings of schools and universities fromthe posts of their students on social media. We also find that the same modelcould predict academic performance from tweets as well as from VK posts. Thegeneralizability of a model trained on a relatively small data set could beexplained by the use of continuous word representations trained on a muchlarger corpus of social media posts. This also allows for greaterinterpretability of model predictions.

Generalizable prediction of academic performance from short texts on social media