Zobrazeno 1 - 10
of 24
pro vyhledávání: '"L Kern"'
Autor:
Salvatore Giorgi, Jason Jeffrey Jones, Anneke Buffone, Johannes C. Eichstaedt, Patrick Crutchley, David B. Yaden, Jeanette Elstein, Mohammadzaman Zamani, Jennifer Kregor, Laura Smith, Martin E. P. Seligman, Margaret L. Kern, Lyle H. Ungar, H. Andrew Schwartz
Publikováno v:
Frontiers in Social Psychology, Vol 2 (2024)
Trust is predictive of civic cooperation and economic growth. Recently, the U.S. public has demonstrated increased partisan division and a surveyed decline in trust in institutions. There is a need to quantify individual and community levels of trust
Externí odkaz:
https://doaj.org/article/ea40aa6a171d47848075653e3336b9bf
Autor:
Jordan Carpenter, Daniel Preotiuc-Pietro, Jenna Clark, Lucie Flekova, Laura Smith, Margaret L. Kern, Anneke Buffone, Lyle Ungar, Martin Seligman
Publikováno v:
Judgment and Decision Making, Vol 13, Pp 562-574 (2018)
Online, social media communication is often ambiguous, and it can encourage speed and inattentiveness. We investigated whether Actively Open Minded Thinking (AOT), a dispositional willingness to seek out new or potentially threatening information, ma
Externí odkaz:
https://doaj.org/article/33e8f6039bd2461d95c1f908475e3b1e
Autor:
Salvatore Giorgi, Lyle H. Ungar, Khoa D. Le Nguyen, Margaret L. Kern, Michal Kosinski, Gregory Park, David B. Yaden, Martin E. P. Seligman, Johannes C. Eichstaedt, H. Andrew Schwartz
Publikováno v:
J Pers
OBJECTIVE: We explore the personality of counties as assessed through linguistic patterns on social media. Such studies were previously limited by the cost and feasibility of large-scale surveys; however, language-based computational models applied t
Autor:
Lyle H. Ungar, Margaret L. Kern, Kokil Jaidka, H. Andrew Schwartz, Salvatore Giorgi, Johannes C. Eichstaedt
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America
Significance Spatial aggregation of Twitter language may make it possible to monitor the subjective well-being of populations on a large scale. Text analysis methods need to yield robust estimates to be dependable. On the one hand, we find that data-
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America
Significance Employment is thought to be more enjoyable and beneficial to individuals and society when there is alignment between the person and the occupation, but a key question is how to best match people with the right profession. The information
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8f57081ffd5feabb34d4ab8dba9d9f84
https://hdl.handle.net/10453/137868
https://hdl.handle.net/10453/137868
Autor:
Johannes C. Eichstaedt, Lyle H. Ungar, David B. Yaden, Sharath Chandra Guntuku, Margaret L. Kern
Publikováno v:
Current Opinion in Behavioral Sciences. 18:43-49
Although rates of diagnosing mental illness have improved over the past few decades, many cases remain undetected. Symptoms associated with mental illness are observable on Twitter, Facebook, and web forums, and automated methods are increasingly abl
Autor:
Jordan Carpenter, Margaret L. Kern, Anneke Buffone, Daniel Preotiuc-Pietro, Lyle H. Ungar, Salvatore Giorgi, Martin E. P. Seligman, Lucie Flekova, Courtney A. Hagan
Publikováno v:
Social Psychological and Personality Science. 8:310-322
People associate certain behaviors with certain social groups. These stereotypical beliefs consist of both accurate and inaccurate associations. Using large-scale, data-driven methods with social media as a context, we isolate stereotypes by using ve
Autor:
Martin E. P. Seligman, Gregory Park, Margaret L. Kern, Maarten Sap, David Stillwell, Michal Kosinski, Evan Weingarten, Lyle H. Ungar, Jonah Berger, H. Andrew Schwartz, Johannes C. Eichstaedt
Publikováno v:
Journal of Personality. 85:270-280
Temporal orientation refers to individual differences in the relative emphasis one places on the past, present, or future, and is related to academic, financial, and health outcomes. We propose and evaluate a method for automatically measuring tempor
Publikováno v:
Journal of Medical Internet Research
Background: Frequent expression of negative emotion words on social media has been linked to depression. However, metrics have relied on average values, not dynamic measures of emotional volatility. Objective: The aim of this study was to report on t