Research

[SUMMARY] Scientists have invested enormous resources into replicating existing findings. One replication study on average consumes 300 days. In response, we propose a new machine learning approach of estimating a study’s replicability from the texts in its original manuscript using algorithms. This method could assess the likelihood of replication success for any study with a manuscript within seconds.

[Summary] This research investigates the implication of expressing one’s authentic vs. idealized personality on social media. The degree of authenticity is measured by the proximity between an individual’s self-reported personality (true self) and the personality inferred from Facebook Likes and status updates (expressed self). Combining a lab experiment and online big data, we found that authentic self-expression is associated with greater well-being.

[SUMMARY] This project answers a classic question: Are romantic partners or friends similar in personality? Decades of research using self-reports of personality found no evidence. However, I identified a bias called the reference-group effect in the self-report method, which obscures similarity among close companions. To circumvent this bias, I assessed participants’ personalities based on their Facebook behaviours. The results showed that couples, or friends, are more similar in personality than previously thought.

[SUMMARY] This research presents machine learning models that could infer people’s personalities from their social media profiles. Our analysis, based on a large database of Facebook users, showed that personality inferred from digital behaviours is more accurate than human judgements. This work demonstrates the potential of machine algorithms as psychological assessment tools. Algorithm-based methods are automated and therefore cheaper, more reliable, and more scalable.