A longitudinal analysis of usage patterns, topics, and information dissemination related to five names for cultured meat on social media

Abstract

Cultured-meat products, which have been hailed for their potential to address multiple drawbacks of traditional meat production, have received regulatory approval in countries including Singapore and the United States and are experiencing rapid market growth. The name of any product could influence public perceptions of it, and thereby affect consumption; and how cultured-meat products should be labeled remains the subject of debate. However, conducting large-scale consumer tests aimed at understanding the association between public perceptions of such products and the various proposed labels/names for them would be time-consuming and expensive. Therefore, this longitudinal research project on how five common cultured-meat labels have been used, and the associations between particular labels and public perceptions, instead relied on 424,382 relevant messages posted or retweeted on Twitter/X between July 2010 and December 2022. This novel approach enabled us to identify a dynamic interplay between label choice and public perceptions, and that each label was associated with a unique set of topics. Also, using social-network analysis, we were able to delineate the structures of cultured meat-related retweet networks and identify the key influencers within them. Our analysis revealed the importance of labeling within the challenging process of arriving at a consensus about what cultured meat should be called.

Keywords: Food labeling; Nomenclature; Public perceptions; Social-network analysis; Twitter.

Graphical abstract

Fig. 1

Workflow of this study.

Fig. 2

(A) The number of tweets of each label, represented by different colors; (B) the proportion of the tweets containing each label. Events corresponding to the four most prominent peaks are highlighted. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3

The whole-dataset word clouds for (A) “in-vitro meat”, (B) “cell-based meat”, (C) “cultivated meat”, (D) “cultured meat”, and (E) “lab-grown meat”.

Fig. 4

(A) The entire retweet network; (B) the union network made up of the 1.5-degree ego networks of the identified influencers, with node sizes proportional to betweenness centrality. Key influencers, identified by their values of betweenness centrality and in-degree centrality, are labeled. The color of each edge represents the label used in the corresponding tweet. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 5

Retweet network graphs of each year's data. Edge colors indicate which label was contained in that tweet. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)