We propose a new way of imagining and measuring opinions emerging from social media. As people tend to connect with like-minded others and express opinions in response to current events on social media, social media public opinion is naturally occurring, temporally sensitive, and inherently social. Our framework for measuring social media public opinion first samples targeted nodes from a large social graph and identifies homogeneous, interactive, and stable networks of actors, which we call “flocks,” based on social network structure, and then measures and presents opinions of flocks. We apply this framework to Twitter and provide empirical evidence for flocks being meaningful units of analysis and flock membership predicting opinion expression. Through contextualizing social media public opinion by foregrounding the various homogeneous networks it is embedded in, we highlight the need to go beyond the aggregate-level measurement of social media public opinion and study the social dynamics of opinion expression using social media.
Research on public views of biotechnology has centered on genetically modified (GM) foods. However, as the breadth of biotechnology applications grows, a better understanding of public concerns about non-agricultural biotechnology products is needed in order to develop proactive strategies to address these concerns. Here, we explore the perceived benefits and risks associated with five biotechnology products and how those perceptions translate into public opinion about the use and regulation of biotechnology in the United States. While we found greater support for non-agricultural biotechnology product, 70% of individuals surveyed showed no or little variation in their support across the products, indicating opinions about early GM products may be influencing the acceptance of emerging biotechnologies. We identified five common patterns of opinions about biotechnology and used machine learning models to integrate a wide range of factors and predict a respondent’s opinion group. While the model was particularly good at identifying individuals supportive of biotechnology, differentiating between individuals from the non- and conditionally-supportive opinion groups was more challenging, emphasizing the complexity of public opinions of emerging biotechnology products.
more » « less- PAR ID:
- 10303185
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Environmental Research Letters
- Volume:
- 14
- Issue:
- 11
- ISSN:
- 1748-9326
- Page Range / eLocation ID:
- Article No. 114018
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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