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My research focuses on four broad areas of research, all involving language, social behavior, and personality or individual differences:

  • Coordination in dialog: This line of research is mainly about language style matching, or LSM. We think that when people match each other's language syle, or function word use, it indicates that they're socially engaged (i.e., paying attention to each other's words and mental states). In most situations, matching is good: We like people who are similar to us and easy to understand. Style matching predicts positive outcomes in speed-dating, work collaborations, friendly correspondence, published poetry, IM chats, and so on. However, there is also evidence that focusing on your partner rather than the task at hand (e.g., negotiation issues) leads to negative outcomes such as impasse and poor problem solving in competitive conversations.

  • Individual language use, health behavior, and personality: Most of this research concerns self-talk (coaching yourself throughout the day or writing about a personal problem). How does language use during self-talk relate to self-regulation and self-improvement? Can we improve people's ability to change their own behavior with subtle writing manipulations? Does self-deception in self-talk ("you will certainly be able to finish 3 papers in one day") behave like other kinds of deception, in terms of linguistic indicators and cognitive-affective consequences? How do traits like neuroticism or impulsivity affect the outcomes of self-talk? I think self-talk is fascinating. It may be the single most ubiquitous and influential source of self-regulation that we have, but it hasn't received much systematic attention in mainstream psychology.

  • Community language use and health: The increasing availability of Big Data (loosely defined as datasets that are too big to open in Excel without several minutes or explosions) gleaned from the internet has been a godsend for computerized text analysts in psychology and elsewhere. People increasingly live large chunks of their daily lives online, leaving behavioral trails wherever they go. We can use language use on Twitter to predict disease and understand factors that quicken and slow its spread. Amazon reviews can be used to understand the nature of expertise and decision-making. Facebook statuses can be used to identify factors that constrain or magnify behavioral indicators of personality. The Internet is a goldmine, and we're just starting to be able to analyze its data properly. I think this is an excellent time for psychologists to find a friendly computer scientist and start several collaborations. The work I've done in this area so far, for example, would not have been possible without the help of generous friends from the University of Pennsylvania's Positive Psychology Center, such as Andy Schwartz.

  • Literature: Literature is an extraordinarily rich data source. It records how experts at thinking about other people tend to think about other people. In stream-of-consciousness writing, we get a glimpse of how the mind works, or how authors believe it works. Most of the time, we find that art imitates life: Fictional beggars who become kings adopt higher status language, the language of an impending breakup is the same in published poetry as it is in real-life couple's everday conversations, and so on. Currently we're studying how individual differences such as sex and empathy relate to individuals' tendencies to write fictional men and women as similar or distinct (based on function word frequencies). So far we've found that across both expert writers and naive participants, women tend to write characters that are fairly androgynous and men write characters that follow typical gender norms for language (with women being more self-focused and social, and men being more formal and socially distant). This differences is largely explained by sex differences in empathy and systemizing (self-reported interest in people vs. interest in rule-based systems).

Interested in calculating language style matching (LSM) in your own data? Save or open this link to see a slightly outdated but still functional help file. And here is some LSM syntax for R.

Go to liwc.net or secretlifeofpronouns.com to read more about the Linguistic Inquiry and Word Count (LIWC), the computerized text analysis program created by James Pennebaker that I use in most of my research.

To visit my former graduate and postdoc labs' webpages, click here for James Pennebaker's lab or here for Dolores Albarracin's Social Action Lab.

If you're interested in R or online research, you might enjoy these sites (not mine): Quick R and MTURK.