Semantic Network Analysis Reveals Opposing Online Representations of the Search Term “GMO”

Abstract

Making sound food and agriculture decisions is important for global society and the environment. Experts tend to view crop genetic engineering, a technology that can improve yields and minimize impacts on the environment, more favorably than the public. Because there is a causal relationship between public opinion and public policy, it is important to understand how opinions about genetically engineered (GE) crops are influenced. The public increasingly seeks science information on the Internet. Here, semantic network analysis is performed to characterize the presentation of the term “GMO (genetically modified organism),” a proxy for food developed from GE crops, on the web. Texts from three sources are analyzed: U.S. federal websites, top pages from a Google search, and online news titles. We found that the framing and sentiment (positive, neutral, or negative attitudes) of “GMO” varies across these sources. It is described how differences in the portrayal of GE food by each source might affect public opinion. A current understanding of the types of information individuals may encounter online can provide insight into public opinion toward GE food. In turn, this knowledge can guide teaching and communication efforts by the scientific community to promote informed decision‐making about agricultural biotechnologies.

Online Representations of “Genome Editing” Uncover Opportunities for Encouraging Engagement: A Semantic Network Analysis

“…This software uses a lexicon that is based on an empirically validated two-step process involving machine learning and the MPQA subjectivity lexicon (https://mpqa.cs.pitt.edu/lexicons/subj_lexicon/), and it includes three types of polarity weights (positive, negative, and neutral) to tag words that match its lexicon entry (Wilson, Wiebe, & Hoffmann, 2005). This approach has been shown to detect subjectivity and contextual polarity with accurate results (Wilson et al, 2005) and has been used in previous studies (Jiang et al, 2018; Ruiz & Barnett, 2015). The processed text data were fed into the software, which then gave an output of each word with its associated polarity weight.…”

“…We analyzed texts obtained from the Wikipedia entry and the top Google search results for “genome editing.” Previous studies have used the SMA approach with other controversial scientific subjects, such as online representation of the human papillomavirus vaccine (Ruiz & Barnett, 2015) and genetically engineered foods (Jiang, Anderton, Ronald, & Barnett, 2018). Based on Scheufele’s (1999) process model of framing, this study provides insight into how online sources portray genome editing to the public and raises the following questions:…”

OK, Google, Tell Me About Birth Control: Sentiment Analysis of Anti- and Pro-Birth Control Headlines and Snippets

“…It might be the case that the online resources returned by Google Search have an impact on the information seeker's attitude toward an issue because of the difference in the content and its framing (e.g. Jiang, Anderton, Ronald & Barnett, 2018). The proposed research was the first to compare word usage, popularity and sentiments of headlines and snippets returned by Google Search when searching for birth control related information.…”

Expert communication on Twitter: Comparing economists’ and scientists’ social networks, topics and communicative styles

“…Improvements in machine-learning methods such as Google BERT ( Devlin et al, 2019 ) together with curated large open corpora such as DBPedia and BabelNet have resulted in recent exciting developments in semantic web methods. Our study follows the approach of Jiang et al (2018) and Calabrese et al (2019) ; it is based on identifying the network of associations between concepts expressed in a text. We created a network of words, where two words are connected if they are in vicinity of each other in a tweet.…”