"The Psychology of User-Generated Content"
One of the most important trends in the dissemination of news and information today is the explosive growth of user-generated content. In this presentation I report findings on two key factors that shape the nature of such content: first, how news information evolves as it is successively retold across users online, and second, how online content differs depending on the technological medium used to generate it. Specifically, in the first paper I show across ten experiments that, when information is sequentially retold from one user to the next, it undergoes a stylistic transformation termed “disagreeable personalization,” wherein original facts are increasingly supplanted by opinions and interpretations with a slant toward negativity. The work also offers evidence that these effects arise when retellers believe they are more (vs. less) knowledgeable than their recipient about the information they are relaying, which compels them to provide more guidance on its meaning and to do so in a persuasive manner. Next, in the second paper I show that when creating user-generated content on their smartphone (vs. personal computer), users tend to be more self-disclosing of personal or intimate information. This tendency arises across three large-scale field studies and two controlled experiments including a wide range of domains such as social media posts, open-ended survey responses, and compliance with requests for personal information in web advertisements. This increased willingness to self-disclose on one’s smartphone arises from the psychological effects of two distinguishing properties of the device: first, feelings of comfort that many associate with their smartphone, and second, a tendency to narrowly focus attention on the disclosure task at hand due to the relative difficulty of generating content on the smaller device. Taken together, the two projects help explain the often subjective, self-disclosing, and even negative nature of much of the user-generated content one comes across in online settings.
Meeting ID: 975 4482 8190