Michael Robert Haupt
Biographic Data
| ID | 2744356 |
|---|---|
| NAME | Michael Robert Haupt |
| GIVEN NAMES | Michael Robert |
| FAMILY NAME | Haupt |
| SIGNATURE | HAUPT M R |
| AFFILIATIONS | University of California San Diego |
| ORCID | 0000-0003-4985-7796 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 5 |
| AUTHOR COUNT | 7 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 1 |
The role of narcissism and motivated reasoning on misinformation propagation
Background Explanations for why social media users propagate misinformation include failure of classical reasoning (over-reliance on intuitive heuristics), motivated reasoning (conforming to group opinion), and personality traits (e.g., narcissism). However, there is a lack of consensus on which explanation is most predictive of misinformation spread. Previous work is also limited by not distinguishing between passive (i.e., “liking”) and active …
Is This Safe? Examining Safety Assessments of Illicit Drug Purchasing on Social Media Using Conjoint Analysis
Background: Illicit substance sales facilitated by social media platforms are a growing public health issue given recent increases in overdose deaths, including an alarming rise in cases of fentanyl poisoning. However, little is known about how online users evaluate what features of social media posts convey safety, which can influence their intent to source illicit substances. Objectives: This study adapts conjoint analysis which assessed how at…
The Influence of News Consumption Habits and Dispositional Traits on Trust in Medical Scientists
Public trust in medical institutions is essential for ensuring compliance with medical directives. However, the politicization of public health issues and the polarized nature of major news outlets suggest that partisanship and news consumption habits can influence medical trust. This study employed a survey with 858 participants and used regression analysis to assesses how news consumption habits and information assessment traits (IATs) influenc…
Chronic pain patients low in social connectedness report higher pain and need deeper pressure for pain relief
The experience of rejection and disconnection reliably amplifies pain. Yet, little is known about the impact of enduring feelings of closeness, or social connectedness, on experiences of chronic pain. The current secondary analysis tested the hypothesis that greater social connectedness would predict lower chronic pain ratings, mediated by lower depression and anxiety. In addition, based on the social-affective effects of deeper pressure, and our…
Characterizing twitter user topics and communication network dynamics of the “Liberate” movement during Covid-19 using unsupervised machine learning and social network analysis
Digital Surveillance to Identify California Alternative and Emerging Tobacco Industry Policy Influence and Mobilization on Facebook
Growing popularity of electronic nicotine-delivery systems (ENDS) has coincided with a need to strengthen tobacco-control policy. In response, the ENDS industry has taken actions to mobilize against public health measures, including coordination on social media platforms. To explore this phenomenon, data mining was used to collect public posts on two Facebook public group pages: the California Consumer Advocates for Smoke Free Alternatives Associ…
Identifying and characterizing scientific authority-related misinformation discourse about hydroxychloroquine on twitter using unsupervised machine learning
This study investigates the types of misinformation spread on Twitter that evokes scientific authority or evidence when making false claims about the antimalarial drug hydroxychloroquine as a treatment for COVID-19. Specifically, we examined tweets generated after former U.S. President Donald Trump retweeted misinformation about the drug using an unsupervised machine learning approach called the biterm topic model that is used to cluster tweets i…
Identifying and characterizing scientific authority-related misinformation discourse about hydroxychloroquine on twitter using unsupervised machine learning
This study investigates the types of misinformation spread on Twitter that evokes scientific authority or evidence when making false claims about the antimalarial drug hydroxychloroquine as a treatment for COVID-19. Specifically, we examined tweets generated after former U.S. President Donald Trump retweeted misinformation about the drug using an unsupervised machine learning approach called the biterm topic model that is used to cluster tweets i…
Characterizing twitter user topics and communication network dynamics of the “Liberate” movement during Covid-19 using unsupervised machine learning and social network analysis
Digital Surveillance to Identify California Alternative and Emerging Tobacco Industry Policy Influence and Mobilization on Facebook
Growing popularity of electronic nicotine-delivery systems (ENDS) has coincided with a need to strengthen tobacco-control policy. In response, the ENDS industry has taken actions to mobilize against public health measures, including coordination on social media platforms. To explore this phenomenon, data mining was used to collect public posts on two Facebook public group pages: the California Consumer Advocates for Smoke Free Alternatives Associ…
Identifying and characterizing scientific authority-related misinformation discourse about hydroxychloroquine on twitter using unsupervised machine learning
This study investigates the types of misinformation spread on Twitter that evokes scientific authority or evidence when making false claims about the antimalarial drug hydroxychloroquine as a treatment for COVID-19. Specifically, we examined tweets generated after former U.S. President Donald Trump retweeted misinformation about the drug using an unsupervised machine learning approach called the biterm topic model that is used to cluster tweets i…
The Influence of News Consumption Habits and Dispositional Traits on Trust in Medical Scientists
Public trust in medical institutions is essential for ensuring compliance with medical directives. However, the politicization of public health issues and the polarized nature of major news outlets suggest that partisanship and news consumption habits can influence medical trust. This study employed a survey with 858 participants and used regression analysis to assesses how news consumption habits and information assessment traits (IATs) influenc…
Chronic pain patients low in social connectedness report higher pain and need deeper pressure for pain relief
The experience of rejection and disconnection reliably amplifies pain. Yet, little is known about the impact of enduring feelings of closeness, or social connectedness, on experiences of chronic pain. The current secondary analysis tested the hypothesis that greater social connectedness would predict lower chronic pain ratings, mediated by lower depression and anxiety. In addition, based on the social-affective effects of deeper pressure, and our…
The role of narcissism and motivated reasoning on misinformation propagation
Background Explanations for why social media users propagate misinformation include failure of classical reasoning (over-reliance on intuitive heuristics), motivated reasoning (conforming to group opinion), and personality traits (e.g., narcissism). However, there is a lack of consensus on which explanation is most predictive of misinformation spread. Previous work is also limited by not distinguishing between passive (i.e., “liking”) and active …
Is This Safe? Examining Safety Assessments of Illicit Drug Purchasing on Social Media Using Conjoint Analysis
Background: Illicit substance sales facilitated by social media platforms are a growing public health issue given recent increases in overdose deaths, including an alarming rise in cases of fentanyl poisoning. However, little is known about how online users evaluate what features of social media posts convey safety, which can influence their intent to source illicit substances. Objectives: This study adapts conjoint analysis which assessed how at…
Misinformation and Its Impacts (5 works) · Psychology (5 works) · Computer Science (4 works) · Medicine (4 works) · Political science (4 works) · Social media (4 works) · Internet privacy (3 works) · Law (3 works) · Social Psychology (3 works) · Business (2 works)