Pseudo-events: Tracking mediatization with machine learning over 40 years. (July 2023)
- Record Type:
- Journal Article
- Title:
- Pseudo-events: Tracking mediatization with machine learning over 40 years. (July 2023)
- Main Title:
- Pseudo-events: Tracking mediatization with machine learning over 40 years
- Authors:
- Xu, Mengyao
Hu, Lingshu
Hinnant, Amanda - Abstract:
- Abstract: Using automated content analysis, this research explores the phenomenon of pseudo-events coverage in The New York Times ( N = 70, 370 articles) from 1980 to 2019. By clarifying the operationalization of pseudo-events, this study introduces pseudo-events as a valuable tool to index how different social subsystems perpetuate mediatization (which is when institutions absorb and abide by media logic). Machine-learning classifiers were constructed to measure pseudo-events, which provides historicity, specificity, and measurability — three tasks set forth for new mediatization research. We found a significant increase in pseudo-event coverage, expressing a more positive tone than genuine event coverage. Moreover, political pseudo-event coverage shows quadrennial cycles with peaks in each presidential election year. Our findings reveal the expansion of mediatization since 1980 and show how media logic has been internalized in different ways by the social subsystems of politics, culture, and economics. Institutions and their social actors need efficient tools to abide by media logic in seeking publicity and commanding authority, and pseudo-events have matured into one of the most dominant tools, especially for political actors. This study offers an innovative approach to capture complex phenomena and shows promises of broader application of machine learning to empirically quantify and identify patterns using theoretical concepts. Highlights: Pseudo-event coverage hasAbstract: Using automated content analysis, this research explores the phenomenon of pseudo-events coverage in The New York Times ( N = 70, 370 articles) from 1980 to 2019. By clarifying the operationalization of pseudo-events, this study introduces pseudo-events as a valuable tool to index how different social subsystems perpetuate mediatization (which is when institutions absorb and abide by media logic). Machine-learning classifiers were constructed to measure pseudo-events, which provides historicity, specificity, and measurability — three tasks set forth for new mediatization research. We found a significant increase in pseudo-event coverage, expressing a more positive tone than genuine event coverage. Moreover, political pseudo-event coverage shows quadrennial cycles with peaks in each presidential election year. Our findings reveal the expansion of mediatization since 1980 and show how media logic has been internalized in different ways by the social subsystems of politics, culture, and economics. Institutions and their social actors need efficient tools to abide by media logic in seeking publicity and commanding authority, and pseudo-events have matured into one of the most dominant tools, especially for political actors. This study offers an innovative approach to capture complex phenomena and shows promises of broader application of machine learning to empirically quantify and identify patterns using theoretical concepts. Highlights: Pseudo-event coverage has increased since 1980 in The New York Times N = 70, 370. Political pseudo-event coverage peaks in each presidential election year. Pseudo-event coverage has a more positive tone than genuine event coverage. Charisma is more popular than rationality to gain authority in pseudo-events. … (more)
- Is Part Of:
- Computers in human behavior. Volume 144(2023)
- Journal:
- Computers in human behavior
- Issue:
- Volume 144(2023)
- Issue Display:
- Volume 144, Issue 2023 (2023)
- Year:
- 2023
- Volume:
- 144
- Issue:
- 2023
- Issue Sort Value:
- 2023-0144-2023-0000
- Page Start:
- Page End:
- Publication Date:
- 2023-07
- Subjects:
- Mediatization -- Pseudo-events -- Hierarchy of influences -- Machine learning -- Media logic -- The New York times
Interactive computer systems -- Periodicals
Man-machine systems -- Periodicals
004.019 - Journal URLs:
- http://www.sciencedirect.com/science/journal/07475632 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.chb.2023.107735 ↗
- Languages:
- English
- ISSNs:
- 0747-5632
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.921600
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 26830.xml