Daniel Preotiuc-Pietro
Biographic Data
| ID | 839728 |
|---|---|
| NAME | Daniel Preotiuc-Pietro |
| GIVEN NAMES | Daniel |
| FAMILY NAME | Preotiuc-Pietro |
| SIGNATURE | PREOTIUC-PIETRO D |
| AFFILIATIONS | University of Pennsylvania |
| ORCID | 0000-0002-4504-0212 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 1 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2018 |
| H-INDEX | 1 |
Facebook language predicts depression in medical records
Significance Depression is disabling and treatable, but underdiagnosed. In this study, we show that the content shared by consenting users on Facebook can predict a future occurrence of depression in their medical records. Language predictive of depression includes references to typical symptoms, including sadness, loneliness, hostility, rumination, and increased self-reference. This study suggests that an analysis of social media data could be u…
Real Men Don’t Say “Cute”: Using Automatic Language Analysis to Isolate Inaccurate Aspects of Stereotypes
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 verbal expression. Across four social categories—gender, age, education level, and political orientation—we identify words and phrases that lead people to incorrectly guess the social category of the wr…
Personality Profiles of Users Sharing Animal-related Content on Social Media
Animal preferences are thought to be linked with more salient psychological traits of people, and most research examining owner personality as a differentiating factor has obtained mixed results. The rise in usage of social networks offers users a new medium in which they can broadcast their preferences and activities, including about animals. In two studies, the first on Facebook status updates and the second on images shared on Twitter, we revi…
Personality Profiles of Users Sharing Animal-related Content on Social Media
Animal preferences are thought to be linked with more salient psychological traits of people, and most research examining owner personality as a differentiating factor has obtained mixed results. The rise in usage of social networks offers users a new medium in which they can broadcast their preferences and activities, including about animals. In two studies, the first on Facebook status updates and the second on images shared on Twitter, we revi…
Real Men Don’t Say “Cute”: Using Automatic Language Analysis to Isolate Inaccurate Aspects of Stereotypes
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 verbal expression. Across four social categories—gender, age, education level, and political orientation—we identify words and phrases that lead people to incorrectly guess the social category of the wr…
Personality Profiles of Users Sharing Animal-related Content on Social Media
Animal preferences are thought to be linked with more salient psychological traits of people, and most research examining owner personality as a differentiating factor has obtained mixed results. The rise in usage of social networks offers users a new medium in which they can broadcast their preferences and activities, including about animals. In two studies, the first on Facebook status updates and the second on images shared on Twitter, we revi…
Facebook language predicts depression in medical records
Significance Depression is disabling and treatable, but underdiagnosed. In this study, we show that the content shared by consenting users on Facebook can predict a future occurrence of depression in their medical records. Language predictive of depression includes references to typical symptoms, including sadness, loneliness, hostility, rumination, and increased self-reference. This study suggests that an analysis of social media data could be u…
Psychology (3 works) · Computer Science (2 works) · Social media (2 works) · World Wide Web (2 works) · Anger (1 works) · Animal and Plant Science Education (1 works) · Big Five personality traits (1 works) · Biology and political orientation (1 works) · Categorization (1 works) · Clinical Psychology (1 works)