This thesis empirically investigates alarm and prosocial words in online news headlines and the associated reads (the number of clicks), responses (including the number of likes, dislikes, and comments), and relays (the number of shares). I analyze over 170,000 online news headlines and mainly the associated number of reads and likes for each news story on an online news platform. Theoretically, based on the meta-level evolutionary theory－evolution by natural selection－I propose a middle-level evolutionary model of prosocial media effects from a nature-nurture interactive perspective. Then, I propose a specific evolutionary model that was derived from the proposed middle-level model, the human alarm system for sensational news, a psychological mechanism designed to detect and concern threatening news. I generate research questions from the specific model to test whether news headlines with alarm words attract more likes as a survival concern indirectly through an increased number of reads as a selection device, and whether prosocial words in headlines serve as a moderator. The results of a conditional indirect effect model showed that given that online readers click on (i.e, read) news headlines with alarm words, the fact that it has a prosocial word in the headlines leads readers more likely to "like" it. The empirical findings' theoretical and methodological contributions, research agenda, and examples of implications for future studies are discussed.
|Date of Award||14 Aug 2017|
|Supervisor||Xinshu ZHAO (Supervisor)|
- Electronic newspapers
- Online journalism
- Reporters and reporting