As artificial intelligence (AI) applications become more pervasive, it is critical to understand
how knowledge workers with different levels and types of experience can team with AI for …

A growing number of people are working as part of on-line crowd work. Crowd work is often
thought to be low wage work. However, we know little about the wage distribution in practice …

How can a single person understand what's going on in a collection of millions of
documents? This is an increasingly common problem: sifting through an organization's e …

Meaning in life is tied to the stories people tell about their lives. We explore whether one
timeless story—the Hero's Journey—might make people's lives feel more meaningful. This …

Topic models aid analysis of text corpora by identifying latent topics based on co-occurring
words. Real-world deployments of topic models, however, often require intensive expert …

Topic models are a useful and ubiquitous tool for understanding large corpora. However,
topic models are not perfect, and for many users in computational social science, digital …

Despite their recognized limitations, bibliometric assessments of scientific productivity have
been widely adopted. We describe here an improved method to quantify the influence of a …

The rise of big data—data that are not only large and massively multivariate but concern a
dizzying array of phenomena—represents a watershed moment for the social sciences …

Topic models are statistical models for learning the latent structure in document collections,
and have gained much attention in the machine learning community over the last decade …

Statistical topic models can help analysts discover patterns in large text corpora by
identifying recurring sets of words and enabling exploration by topical concepts. However …