Quantifying the narrative flow of imagined versus autobiographical stories.

Lifelong experiences and learned knowledge lead to shared expectations about how common situations tend to unfold. Such knowledge of narrative event flow enables people to weave together a story. However, comparable computational tools to evaluate the flow of events in narratives are limited.

Gaining insights from social media language: Methodologies and challenges.

Language data available through social media provide opportunities to study people at an unprecedented scale. However, little guidance is available to psychologists who want to enter this area of research. Drawing on tools and techniques developed in natural language processing, we first introduce psychologists to social media language research, identifying descriptive and predictive analyses that language data allow.

PREDICTING INDIVIDUAL WELL-BEING THROUGH THE LANGUAGE OF SOCIAL MEDIA.

We present the task of predicting individual well-being, as measured by a life satisfaction scale, through the language people use on social media. Well-being, which encompasses much more than emotion and mood, is linked with good mental and physical health. The ability to quickly and accurately assess it can supplement multi-million dollar national surveys as well as promote whole body health.

Living in the Past, Present, and Future: Measuring Temporal Orientation With Language.

Temporal orientation refers to individual differences in the relative emphasis one places on the past, present, or future, and it is related to academic, financial, and health outcomes. We propose and evaluate a method for automatically measuring temporal orientation through language expressed on social media. Judges rated the temporal orientation of 4,302 social media messages.

Twitter sentiment predicts Affordable Care Act marketplace enrollment.

Background: Traditional metrics of the impact of the Affordable Care Act (ACA) and health insurance marketplaces in the United States include public opinion polls and marketplace enrollment, which are published with a lag of weeks to months. In this rapidly changing environment, a real-time barometer of public opinion with a mechanism to identify emerging issues would be valuable.

Objective: We sought to evaluate Twitter's role as a real-time barometer of public sentiment on the ACA and to determine if Twitter sentiment (the positivity or negativity of tweets) could be predictive of state-level marketplace enrollment.

Psychological language on Twitter predicts county-level heart disease mortality.

Hostility and chronic stress are known risk factors for heart disease, but they are costly to assess on a large scale. We used language expressed on Twitter to characterize community-level psychological correlates of age-adjusted mortality from atherosclerotic heart disease (AHD). Language patterns reflecting negative social relationships, disengagement, and negative emotions-especially anger-emerged as risk factors; positive emotions and psychological engagement emerged as protective factors.

The 2013 US Government Shutdown (#Shutdown) and health: an emerging role for social media.

In October 2013, multiple United States (US) federal health departments and agencies posted on Twitter, "We're sorry, but we will not be tweeting or responding to @replies during the shutdown. We'll be back as soon as possible!" These "last tweets" and the millions of responses they generated revealed social media's role as a forum for sharing and discussing information rapidly. Social media are now among the few dominant communication channels used today.