Comparison of public discussions of gene editing on social media between the United States and China

Abstract

The world’s first gene-edited babies event has stirred controversy on social media over the use of gene editing technology. Understanding public discussions about this controversy will provide important insights about opinions of science and facilitate informed policy decisions. This study compares public discussion topics about gene editing on Twitter and Weibo, as wel asthe evolution of these topics over four months. Latent Dirichlet allocation (LDA) was used to generate topics for 11,244 Weibo posts and 57,525 tweets from September 25, 2018, to January 25, 2019. Results showed a difference between the topics on Twitter versus Weibo: there were more nuanced discussions on Twitter, and the discussed topics between platforms focused on different areas. Temporal analysis showed that most discussions took place around gene-edited events. Based on our findings, suggestions were provided for policymakers and science communication practitioners to develop more effective communication strategies toward audiences in China and the U.S.

A comparison of three methods to determine the subject matter in textual data

“…How well do these analyses deal with the changing nature of text (Jiang et al, 2016;Calabrese et al, 2020)? Also, future work should examine how these analysis methods can handle languages other than English with different syntax, such as Korean and Chinese (Kwon et al, 2009;Ji et al, 2022). Lastly, the three methods we use in this study focus on the co-occurrence of words; however, advanced language models, such as ChatGPT (OpenAI, 2022) are now being developed to analyze text data through multiple techniques.…”

“…Latent Dirichlet Allocation (LDA) (Blei et al, 2003;Maier et al, 2018), is widely employed to extract topics from text data (Ji et al, 2022). LDA uses an unsupervised probabilistic model that generates mixtures of latent topics from a corpus of text, where each topic is characterized by a distribution of words.…”

“…Generally, noise words such as prepositions, conjunctions, transitive verbs, and other meaningless symbols are removed prior to the creation of the list of bigrams (Doerfel and Barnett, 1999). Also, the words often undergo stemming, the combining of the forms of a word into a single word, its root term (Ji et al, 2022). For example, "compute", "computing", "computer", and "computed" would be combined and treated as the symbol, "compute."…”

Social Media and Chronic Pain: What Do Patients Discuss?

“…The LDA model gives the topic of each post with a distribution over the topics, and each topic is a multinomial distribution over words in the corpus, thereby relying on a three-stage Bayesian probabilistic model [ 22 ]. In other words, LDA is a mathematical method for finding the mixture of words associated with each topic while simultaneously also determining the mixture of topics that describes each post [ 23 ]. Topic modelling was conducted using NLTK and Gensim.…”

Social network analysis of Twitter interactions: a directed multilayer network approach

“…In a related work, Jiwanggi and Adriani (2016) detail methods for extracting a summary of topics from a collection of Tweets. Topic modeling is frequently useful to model the evolution of public discourse on topics of interest such as vaccination or gene editing (Ji et al 2022). Community discovery methods seek to identify the closely connected groups of users in a network, whether by individual communications or communications related to a common topic such as vaccine hesitancy (Ruiz et al 2021).…”