Style, Content, and the Success of Ideas

Why do some things succeed in the marketplace of ideas? While some argue that content drives success, others suggest that style, or the way ideas are presented, also plays an important role. To provide a stringent test of style's importance, we examine it in a context where content should be paramount: academic research. While scientists often see writing as a disinterested way to communicate unobstructed truth, a multi-method investigation indicates that writing style shapes impact. Separating style from content can be difficult as papers that tend to use certain language may also write about certain topics. Consequently, we focus on a unique class of words linked to style (i.e., function words such as "and," "the," and "on") that are completely devoid of content. Natural language processing of almost 30,000 articles from a range of disciplines finds that function words explain 13-27% of language's impact on citations. Ancillary analyses explore specific categories of function words to suggest how style matters, highlighting the role of writing simplicity, personal voice, and temporal perspective. Experiments further underscore the causal impact of style. The results suggest how to boost communication's impact and highlight the value of natural language processing for understanding the success of ideas.

Quantifying Gender Bias in Consumer Culture

Cultural items like songs have an important impact in creating and reinforcing stereotypes, biases, and discrimination. But the actual nature of such items is often less transparent. Take songs, for example. Are lyrics biased against women? And how have any such biases changed over time? Natural language processing of a quarter of a million songs over 50 years quantifies misogyny. Women are less likely to be associated with desirable traits (i.e., competence), and while this bias has decreased, it persists. Ancillary analyses further suggest that song lyrics may help drive shifts in societal stereotypes towards women, and that lyrical shifts are driven by male artists (as female artists were less biased to begin with). Overall, these results shed light on cultural evolution, subtle measures of bias and discrimination, and how natural language processing and machine learning can provide deeper insight into stereotypes and cultural change.

Do Facial Expressions Predict Ad Sharing? A Large-Scale Observational Study

People often share news and information with their social connections, but why do some advertisements get shared more than others? A large-scale test examines whether facial responses predict sharing. Facial expressions play a key role in emotional expression. Using scalable automated facial coding algorithms, we quantify the facial expressions of thousands of individuals in response to hundreds of advertisements. Results suggest that not all emotions expressed during viewing increase sharing, and that the relationship between emotion and transmission is more complex than mere valence alone. Facial actions linked to positive emotions (i.e., smiles) were associated with increased sharing. But while some actions associated with negative emotion (e.g., lip depressor, associated with sadness) were linked to decreased sharing, others (i.e., nose wrinkles, associated with disgust) were linked to increased sharing. The ability to quickly collect facial responses at scale in peoples' natural environment has important implications for marketers and opens up a range of avenues for further research.