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Hill et al.  Advancements in Agricultural Development 
  Volume 3, Issue 2, 2022 
  agdevresearch.org 

1. Nellie Hill, Assistant Professor, Kansas State University, 301 Umberger Hall, Manhattan, KS 66506, 
nlhill@ksu.edu,  https://orcid.org/0000-0002-8987-3259  

2. Courtney Meyers, Professor, Texas Tech University, Box 42131-Lubbock, TX 79409-2131, 
courtney.meyers@ttu.edu,  https://orcid.org/0000-0001-9099-3613, 

3. Nan Li, Assistant Professor, University of Wisconsin-Madison, 226 Hiram Smith Hall, Madison, WI, 
53706, nli8@wisc.edu,  https://orcid.org/0000-0001-5942-552X  

4. David Doerfert, Professor, Texas Tech University, Box 42131-Lubbock, TX 79409-2131, 
david.doerfert@ttu.edu,  https://orcid.org/0000-0002-1553-3120 

5. Venugopal Mendu, Winifred Asbjornson Plant Sciences Endowed Chair, Associate Professor, 415 
Leon Johnson Hall, Bozeman, MT, 59717-3140, venugopal.mendu@montana.edu, 

 https://orcid.org/0000-0002-4985-2672  
31 

 

How Does the Public Discuss Gene Editing in Agriculture? An 
Analysis of Twitter Content 

 
N. Hill1, C. Meyers2, N. Li3, D. Doerfert4, V. Mendu5 

 
 

  

Abstract 

As people form their opinion about gene editing applications in agriculture, they are utilizing social 
media to seek and share information and opinions on the topic. Understanding how the public 
discusses this technology will influence the development of effective messaging and practitioner 
engagement in the conversation. The purpose of this study was to describe the characteristics of 
Twitter content related to applications of gene editing in agriculture. Social media monitoring 
facilitated a quantitative, descriptive analysis of public Twitter content related to the topic. A 
Meltwater social media monitor collected N = 13,189 relevant tweets for analysis, revealing the 
amount of conversation regarding gene editing in agriculture, the number of contributing Twitter 
users, and the reach of the conversation which was relatively stable over the life of the study. In 
contrast, engagement with the topic rose with the sentiment of tweets becoming increasingly 
positive. News organization accounts had the most reach while a mix of news accounts and personal 
accounts garnered the greatest engagement. These results demonstrate an opportunity for 
agricultural and science communicators to create affirmative messaging about gene editing in 
agriculture delivered through news media Twitter accounts potentially increasing the reach and 
engagement in the social system and with science communication. 
 

Keywords 

Diffusion of innovation, social media monitoring, Meltwater, social systems 



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Introduction and Problem Statement 
 
Stigmas, misrepresentations, and uproar characteristic of public opinion about genetically 
modified organisms could carry over to products of gene editing, hampering their potential to 
meet the challenge of global food demand (Rose et al., 2020; Shew et al., 2018). Science 
communicators and agriculturalists have an opportunity to stave off such negativity in favor of 
public appreciation for the complexities of the food system. To harness the potential, time is of 
the essence (Doxzen & Henderson, 2020; Wirz et al., 2020). Understanding how the public 
discusses gene editing applications in agriculture would help communicators develop strategies 
to promote the positive implications of the technology (Brossard, 2019; Wirz et al., 2020). 
 
In general, gene-editing technologies are used for making specific improvements to desired 
traits in commercial plants and animals (Metje-Sprink et al., 2019). This technology is 
advantageous because of its superior speed, precision, accuracy, and cost (Metje-Sprink et al., 
2019). Unlike genetic modification techniques, gene-editing technologies leave no detectable 
genetic material (Metje-Sprink et al., 2019). As Turnbull et al. (2021) states, “Scientists aver that 
gene editing is not ‘genetic modification’ because the method of introducing changes to the 
DNA is no different from changes that can occur during conventional breeding or in nature” (p. 
3). 
 
The increasing capabilities of gene editing elicit heightened visibility and conversation about the 
technology (Molteni, 2019). Such discussions take place, in part, online. Social media plays a 
significant role in viewing and sharing science information as 33% of Americans consider it an 
important way to get science news (Funk et al., 2017), with 69% getting news in general via 
Twitter (Mitchell et al., 2021). People who share information on social media want to shape a 
smart, helpful, and informed online identity (Kraft et al., 2020). People are more honest about 
their opinions on social media than they are in person (Varma et al., 2017). 
 
Twitter is an excellent means for bridging and encouraging engagement between the public and 
science, but there is a need to understand how science is being discussed on the platform so 
communicators can improve platform-specific content (López-Goñi & Sánchez-Angulo, 2018). 
Previous research has examined Twitter users’ perception of human applications of gene 
editing (Guertin et al., 2018), but a literature review did not find such research specific to 
agricultural applications. This study investigates how Twitter users are diffusing information 
regarding agricultural applications of gene editing through the social system of the platform.  
 

Theoretical and Conceptual Framework 
 
Diffusion of innovation theory served as the conceptual framework for this study. This theory 
posits members of a social system spread information about an innovation through two-way 
communication using various channels over time (Rogers, 2003). This study focuses on diffusion 
of information related to gene editing in agriculture through the social system of Twitter. 



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Science news is exchanged in this social environment to accomplish the common goal of 
demystifying the topic for the public (Busquet & Viken, 2019). 
 
An individual becomes aware of the innovation, forms a perspective on it, decides whether to 
adopt or reject it, takes action on their decision, then seeks validation the right decision was 
made (Rogers, 2003). Social media use is increasingly used for knowledge sharing (Ahmed et al., 
2019). Information perceived as useful and influential to others is more likely to be shared by 
social media users (Kraft et al., 2020). Research utilizing diffusion of innovation theory has 
found social media sharing behaviors indicate the sender’s approval or acceptance of the 
information (Kee et al., 2016). The complexity and far-reaching social, health, economic, and 
national security implications of gene-editing technology make the innovation rife with 
uncertainty for many (Scheufele et al., 2017). With so many avenues of informational needs, 
people turn to multiple sources and communication channels, including social media and news 
media, to gather information to reduce their uncertainty (Rogers, 2003; Scheufele et al., 2017). 
When regulations, governmental guidance, and laws are established, uncertainty is reduced as 
availability of information increases about the technological characteristics and definitions of 
gene editing (Whelan et al., 2020). “It facilitates their decision process (Kat & Oomen, 2007) 
and the diffusion of innovation” (Whelan et al., 2020, p. 2). 
 
Diffusion is affected by the norms and relationships within a social system (Rogers, 2003). Meng 
et al. (2018) utilized diffusion of innovation theory as a framework to identify mass media 
channels widely disseminate information via a tweet because of their large user following. 
Additionally, interpersonal connections serve as brokers to share information across 
communities. Tweets from brokers were retweeted by users more often, increasing the virality 
of the information (Meng et al., 2018). Zhu et al. (2020) also utilized diffusion of innovation 
theory to study tweets from the Center for Disease Control. Tweets with severity, efficacy, and 
call-for-action information were shared more rapidly and diffused to a greater number of 
receivers. In addition, tweets with a negative tone were shared faster and wider than positively 
toned tweets. Tweets with fewer affiliative words (e.g., ally, together, friend) were also shared 
more rapidly and to a greater number of receivers. The researchers concluded tweet 
characteristics have significant effects on diffusion outcomes (Zhu et al., 2020).  
 
Siebert (2019) used diffusion of innovation theory in their framework to conduct a qualitative 
content analysis of tweets about the genetically-engineered AquAdvantage Salmon™. The 
author compared the themes of tweets from salmon producers and those from the public. They 
found producers focused on AquAdvantage Salmon™ as a solution to the complex food 
problem of meeting heightened demand for seafood while reducing environmental impact. 
Tweets from the public discussed how natural and safe the salmon was, as well as the need for 
proper labeling (Siebert, 2019). Siebert (2019) also included the characteristics of innovations 
(Rogers, 2003) as a means of explaining their findings regarding public hesitancy to accept 
AquAdvantage Salmon™.  
 
 



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Purpose 
 
The purpose of this study was to describe the characteristics of Twitter content related to 
applications of gene editing in agriculture. The study was guided by the following research 
questions. 
1. How many mentions of gene editing applications in agriculture were publicly posted on 

Twitter between September 1, 2018 and December 31, 2019? 
2. What was the social reach and engagement of those tweets? 
3. What was the sentiment of those tweets? 
4. How does tweet reach and engagement vary based on tweet sentiment? 
 

Methods 
 
Social media monitoring was utilized to facilitate a quantitative, descriptive analysis of content 
related to gene editing applications in agriculture publicly posted on Twitter between 
September 1, 2018 and December 31, 2019. Data collection was not possible prior to 
September 1, 2018 as Meltwater only maintains a rolling 15 months of social media content. 
Collection concluded at the end of December 2019 due to the volume of tweets collected and 
the timeframe to complete the study. 
 
Twitter is of particular interest for examining relationships within food systems as its structure 
and norms defy those of other social media platforms. The diverse usership able to view public 
accounts, and the continuous sharing of tweets disassemble audience boundaries to construct 
highly individual accounts contributing to the community at large (Pennell, 2016). According to 
the Pew Research Center (Wojcik & Hughes, 2019), approximately 22% of adult Americans use 
Twitter. The median age of a Twitter user is 40 years old, while the median age of a U.S. adult is 
47 years old. Twitter users have slightly higher educational attainment than the general U.S. 
population, with 42% of adult Twitter users having bachelor’s degree while 31% of the adult 
U.S. population has the same degree. Of adult Twitter users who reported their annual 
household income, 41% said it was higher than $75,000, while 31% of the general population’s 
annual household income is above that dollar figure (Wojcik & Hughes, 2019).  These 
characteristics, coupled with social media users’ desire to portray an online identity that is 
smart, helpful, and informed, make Twitter users prime candidates to lead acceptance of new 
technologies (Berger, 2014; Boyd & Ellison, 2007; Rogers, 2003). 
 
Meltwater, a social media monitoring platform, was used to collect the relevant content. A 
monitor was established within Meltwater using a Boolean search query (available upon 
request) to identify only content related to gene editing applications in agriculture within the 
designated time frame. The keywords included in the Meltwater monitor were based on an in-
depth scan of the scholarly literature and popular press and the various terminology used to 
discuss gene editing therein (Huang et al., 2016; Johnson, 2015; Tagliabue, 2015; Guertin et al., 
2018). Data were limited to the United States because definitions and regulations regarding 
gene editing differ from country to country (Metje-Sprink et al., 2019). The monitor gathered N 



Hill et al.  Advancements in Agricultural Development 
 

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= 13,189 public tweets pertaining to the study, constrained by platform data limitations. IBM 
SPSS v. 25 was used for descriptive and non-parametric statistics to address the research 
questions. Statistical significance was set a priori at < .05 (Field, 2017). 
 
Meltwater creates reports containing each tweet and extensive information associated with it, 
including the date and time the tweet was published, username of the publisher, full text of the 
tweet, country of origin, reach of the post (number of followers of the post author), 
engagement with the post if it is an original tweet (number of replies, retweets, and likes), and 
sentiment (overall positive, negative, or neutral tone) (Gan, 2021). Sentiment is analyzed by a 
Natural Language Processing Computational Linguistics algorithm to assess the opinion and 
emotions of a text and categorize it as positive, negative, or neutral in overall tone (Kadam & 
Joglekar, 2013; Bishop, n.d.).  
 
Due to the nuanced, informal, and creatively punctuated nature of tweets, there is debate over 
the best method to mine sentiment from them (Roberts et al., 2018). To illuminate potential 
differences between manual and automated methods of sentiment analysis, the lead author 
manually annotated a randomly selected subset of 1% (n = 1,389) of the tweets from the 
sample (Roberts et al., 2018). Each tweet was read, then assigned into one of three categories 
(positive, negative, or neutral) based on the overall emotional tone of the tweet (Kadam & 
Joglekar, 2013; Roberts et al., 2018). Of the 1,389 tweets, 565 were annotated as positive, 363 
as neutral, and 461 as negative in overall tone. Meltwater’s natural language processing 
algorithm designated 570 tweets as positive, 146 as neutral, and 673 as negative. Overall, 951 
(68.3%) of the subset tweets matched in sentiment assignment.  
 
As examples of tweets the author and Meltwater agreed on in terms of sentiment: 
@InnovatureNow: Tailgating season is in full swing and thanks to agricultural innovations like 
gene editing, football food classics can stay in play. (positive); @HealthRanger: New Crispr GMO 
food is a dangerous experiment so don't be a lab rat that eats it! https://t.co/Fxi0zpYQua 
#crisprdanger #dangerousfood (negative); @CellBioNews170: Nature plants review explores 
the current state and future of CRISPR technology in crops https://t.co/zjkhQWmWJe (neutral). 
 
While manual annotation allows a human to identify tone in a message and appreciate the 
sentiment of words in context, the subjectivity of the process in combination with the volume 
of tweets in need of coding are important limitations to this method of sentiment analysis 
(Resch et al., 2016; Roberts et al., 2018). Limitations are an inherent part of research. No social 
media monitoring tool or sentiment analysis algorithm is perfect, so platforms other than 
Meltwater should be used to assess differences in analysis. Meltwater was chosen for this study 
due to funding availability and application for practical use. 
 

 
 
 



Hill et al.  Advancements in Agricultural Development 
 

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Findings 
 
RQ1: How many mentions of gene editing applications in agriculture were publicly posted on 
Twitter between September 1, 2018, and December 31, 2019? 
During the time frame of the study, there were 13,189 mentions of gene editing related to 
agriculture on Twitter that met the search criteria. Of those, 3,576 were posted in the last 
quarter of 2018 and 9,614 were posted in 2019. Those tweets were publicly posted by 5,824 
unique users (individual accounts without duplication). Figure 1 displays the changes in 
frequency (or numerical count) of tweets and unique users contributing to the conversation by 
month during the search period for the study. 
 
Figure 1 
 
Frequency of Gene editing in Agriculture Tweets and Unique Twitter Users Contributing 

 
 
RQ2: What was the social reach and engagement of those tweets? 
Tweets pertaining to the study had the potential to reach 266,554,740 Twitter users. Reach is 
the number of Twitter users who may see the tweet, calculated based on the number of 
followers of the post author (Gan, 2021). Peak reach occurred in May 2019 with 147,919,414 
potential viewers of tweets regarding gene editing in agriculture. Figure 2 presents the changes 
in reach during the time period of the study.  
 
 
 
 
 
 
 
 

579

1,157
1,007

832

543

768

1,018
1,152

707
589

911 898

610

847 816 755

331

758 749
605

347
429

640 663

461
347

567 560

332
475 508

429

Sept. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec.

Fr
eq

ue
nc

y

Month 2018 - 2019



Hill et al.  Advancements in Agricultural Development 
 

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Figure 2 
 
Reach of Gene Editing in Agriculture Tweets 

 
 
As the reach of a tweet is calculated based on the number of followers of the post author, the 
top 10 Twitter accounts in terms of reach are presented in Table 1. 
 
Table 1 
 
Gene Editing in Agriculture Top 10 Twitter Accounts Regarding Reach  
Account Name Account Type Reach 
nytimes News Media 43,472,723 
WIRED News Media 10,349,781 
ScienceNews News Media 2,814,784 
businessinsider News Media 2,586,572 
WIREDScience News Media 2,013,565 
TheAtlantic News Media 1,828,854 
CNET News Media 1,621,044 
sciencemagazine News Media 1,246,320 
NYTScience News Media 1,150,285 
RogueNASA Personal Account 868,282 

 
Original tweets pertaining to the study resulted in a total of 24,067 engagements, which are 
replies, retweets, and likes associated with an original tweet (Gan, 2021). Average engagement 
with individual tweets was 7.43 (SD = 17.69). Engagement with individual tweets ranged from 

0

20,000,000

40,000,000

60,000,000

80,000,000

100,000,000

120,000,000

140,000,000

160,000,000

Se
pt.

Oct. Nov. Dec.
Jan

.
Fe

b.
Mar. Apr.

May Jun.
Jul.

Aug.
Se

p.
Oct. Nov. Dec.

Re
ac

h 
Vo

lu
m

e

Month 2018 - 2019



Hill et al.  Advancements in Agricultural Development 
 

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zero to 452. Figure 3 presents engagement with tweets and frequency of tweets during the 
time period of the study.   
 
Figure 3 
 
Frequency and Engagement with Gene Editing in Agriculture Tweets 

 
 
External to Meltwater’s reporting, the proportion of retweets to original tweets was calculated 
as an indicator of interaction over the entire search (Grabbert et al., 2019). Of the 13,189 
tweets collected, 7,022 (53.2%) were retweets, indicating high interaction among accounts 
participating in the conversation about gene editing in agriculture (Grabbert et al., 2019). The 
top 10 Twitter posts in terms of engagement are presented in Table 3.  
 
Table 3 
 
Gene Editing in Agriculture Top 10 Twitter Posts Regarding Engagement 
Account Name Tweet Engagement 
ajitjohnson_n Researchers used CRISPR before birth in an 

animal model to treat a lethal lung disease 
that causes death within hours after birth. 
This study shows that in utero editing could 
be a promising new approach for treating 
fatal diseases before birth. 
https://t.co/qqC4YeIUMo    
https://t.co/i1FtvMnwmz   

452 

nytimes The world's first Crispr snails might help clear 
up a mystery of left/right asymmetry in the 
animal kingdom https://t.co/GwZxifW4CR   

334 

579

1,157
1,007

832

543
768

1,018
1,152

707
589

911 898

610
847 816 755812

2,023

1,621

1,270

887

1,301

1,957

2,287 2,380

953

1,631
1,449 1,412

1,683

1,146
1,255

Sept. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec.

Fr
eq

ue
nc

y

Months 2018-2019

-- Tweets
-- Engagement



Hill et al.  Advancements in Agricultural Development 
 

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Account Name Tweet Engagement 

nytimes The world's first Crispr snails might help clear 
up a mystery of left/right asymmetry in the 
animal kingdom https://t.co/ptdPhaaSFt   

320 

nytimes The world's first Crispr snails might help clear 
up a mystery of left/right asymmetry in the 
animal kingdom https://t.co/R3EIWOXcrL   

243 

Incarnated_ET Scientists cure mice of HIV for first time in 
groundbreaking study using CRISPR A group 
of scientists have, for the first time, 
eliminated HIV DNA from the genomes of 
living animals, in what is being described as 
a critical step towards developing a cure for 
the AIDS virus. 

209 

AgBioWorld Japan understands that gene-editing like 
#CRISPR is not GMO, just plain old 
mutagenesis with knowledge & precision! 
So, Genome-edited food products to go on 
sale in Japan, minus scary labelling or 
unnecessary regulatory burden 
https://t.co/VpbSW4PkMu   

184 

AgBioWorld Whoa! A major breakthrough in sorghum, 
gene editing has elevated the protein of 
this important crop from 9-10% to a 
staggering 15-16%. Also improved 
digestibility. A big deal for Africa & India, 
another reason to embrace NBT, remove 
regulatory hurdles 
https://t.co/2WBlEECemA   

165 

IDSAInfo Is #HIV a curable disease? A new study 
suggests yes, as researchers find success in 
eliminating the disease from an infected 
animal’s genome through a combo of 
modified ARV treatment & gene-editing 
tool CRISPR-cas9. https://t.co/hrqZc4hATU  
#EndHIVEpidemic  

 

122 

 



Hill et al.  Advancements in Agricultural Development 
 

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Account Name Tweet Engagement 

WIREDScience Crispr works in almost every animal that 
scientists have tried, from silkworms to 
monkeys, and in just about every cell 
type—kidney cells, heart cells, you name it. 
What’s more, Crispr is both fast and cheap. 
So how far do we want it to go? 
https://t.co/uueH6Bqbh0   

121 

BioBeef So I binge watched #UnnaturalSelection on 
Netflix last night 'cause that is what animal 
geneticists with an interest in #scicomm do 
on a Friday night, & have some thoughts 
around agricultural applications of genome 
editing so I wrote a BLOG @ucanr @ucdavis 
https://t.co/pUb0jEPSC7   

117 

Note. Engagement is number of replies, retweets, and likes of an original tweet.  
 
RQ3: What was the sentiment of those tweets? 
Meltwater’s natural language processing algorithm assigned a sentiment of positive, negative, 
or neutral to each tweet based on the overall tone of the message (Bishop, n.d.). Of all 
collected tweets (N = 13,189), Meltwater coded 5,083 (38.5%) as positive, 1,840 (14.0%) as 
negative, and 6,266 (47.5%) as neutral in tone. Peak positive sentiment occurred in November 
2019 with 64% of tweets during the month having an overall positive tone. Peak negative 
sentiment occurred in February 2019, with 52% of tweets during the month having an overall 
negative tone. Peak neutral sentiment occurred in August 2019, with 62% of tweets during the 
month having an overall neutral tone. Figure 4 presents the sentiment of tweets during the 
time period of the study. 
 
 
 
 
 
 
 
 
 
 
 
 
 
  



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Figure 4  
 
Sentiment of Gene Editing in Agriculture Tweets  

 
 
RQ4: How does tweet reach and engagement vary based on tweet sentiment? 
A Kruskal-Wallis H test was utilized to address if tweet reach varied based on tweet sentiment, 
because a visual inspection of a boxplot indicated outliers too extreme to allow for ANOVA 
statistical analysis, but also too valuable to be removed from the data set (Field, 2017; Laerd 
Statistics, n.d.). As such, a Kruskal-Wallis H test was run to determine if there were differences 
in tweet reach between three groups of tweet sentiment: positive (n = 5,083), negative (n = 
1,511), and neutral (n = 6,595). Distributions of reach were similar for all groups, as assessed by 
visual inspection of a boxplot, therefore requiring a comparison of medians (Laerd Statistics, 
n.d.). Median reach was statistically significantly different between groups,  
χ2(2) = 8.279, p = .016. To determine differences between groups, pairwise comparisons were 
performed using Dunn's (1964) procedure with a Bonferroni correction for multiple 
comparisons. Adjusted p-values are presented. Post hoc analysis revealed statistically 
significant differences in median tweet reach between positive sentiment (Mdn = 944.00) and 
neutral sentiment (Mdn = 1011.00) tweets (p = .023), but not between positive sentiment and 
negative sentiment (Mdn = 915.00) (p = 1.00), or negative sentiment and neutral sentiment (p = 
.211). 
 
To eliminate duplicated data, 7,022 retweets were removed from the population to focus on 
original tweet engagement. Additionally, 2,928 tweets received no replies, retweets, or likes, so 
these outliers were removed from the dataset in order to determine if a difference in sentiment 
elicits a difference in the amount of engagement. These steps left a sample of n = 3,239 tweets 

0%
10%
20%
30%
40%
50%
60%
70%
80%
90%

100%

Se
pt.

Oct. Nov. Dec.
Jan.

Fe
b.

Mar.
Apr.

May
Jun.

Jul.
Aug.

Se
p.

Oct. Nov. Dec.

Se
nt

im
en

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er

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nt

ag
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Month 2018-2019

% Positive
% Negative
% Neutral



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for statistical analysis. A Kruskal-Wallis H test was utilized to address if tweet engagement 
varied based on tweet sentiment because a visual inspection of a boxplot indicated outliers too 
extreme to allow for ANOVA statistical analysis, but also too valuable to be removed from the 
data set (Field, 2017; Laerd Statistics, n.d.). As such, a Kruskal-Wallis H test was run to 
determine if there were differences in tweet engagement between three groups of tweet 
sentiment: positive (n = 1,285), negative (n = 417), and neutral (n = 1,537). Distributions of 
reach were not similar for all groups, as assessed by visual inspection of a boxplot, requiring a 
comparison of mean rank (Laerd Statistics, n.d.). Engagement was statistically significantly 
different between sentiment groups, χ2(2) = 14.650, p = .001. To determine differences 
between groups, pairwise comparisons were performed using Dunn's (1964) procedure with a 
Bonferroni correction for multiple comparisons. Adjusted p-values are presented. Post hoc 
analysis revealed statistically significant differences in tweet engagement between positive 
sentiment (mean rank = 1696.15) and neutral sentiment (mean rank = 1573.77) tweets (p = 
.001), as well as between positive sentiment and negative sentiment (mean rank = 1555.75) (p 
= .021), but not negative sentiment and neutral sentiment (p = 1.00). 
 

Conclusions, Discussion, and Recommendations 
 
As people form their opinion about gene editing applications in agriculture, they turn to social 
media to seek and share information and opinions on the topic (Gil de Zúñiga, et al., 2012; 
Hughes et al., 2012). The associated sharing, liking, and tweeting behaviors mark the 
communication structure within the Twitter social system (Rogers, 2003). Communications 
strategies promoting the positive implications of gene editing applications in agriculture rest on 
an understanding of how the public discusses the technology (Brossard, 2019; Wirz et al., 
2020).  
 
Findings suggest Twitter users are marking their information exchange within the social system 
with increased participation in the form of replies, retweets, and likes as well as greater 
positivity (Kee et al., 2016; Rogers, 2003). This bodes well for the concern that negative public 
opinion of genetically modified organisms could carry over to products of gene editing (Rose et 
al., 2020; Shew et al., 2018).  
 
As communicators will seek to expand the conversation about applications of gene editing, 
results indicate mass media channels hold the greatest opportunity to do so. All but one of the 
top 10 accounts with the greatest reach represent a news platform. Mass media channels, such 
as these, hold the greatest opportunity to influence knowledge acquisition about gene editing 
in agriculture by linking Twitter users to information outside of the social system (Rogers, 
2003). Results indicated neutrally toned messages tended to reach a wider audience than 
positively toned tweets, but otherwise, there were no significant differences between 
sentiment and tweet reach. This result adds nuance to the findings of Zhu et al. (2020) who only 
examined reach of positive versus negative messages and found negative messages to be 
shared among a wider audience. Additional investigation of how the tone of tweets affects 
information diffusion is needed (Zhu et al., 2020).  



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Engagement with the conversation about gene editing can be an indicator of interaction among 
the social system as well as approval of or agreement with the message (Grabbert et al., 2019; 
Kee et al., 2016; Rogers, 2003). Both are goals among communicators who desire to create 
content that fosters conversation and encourages feedback from the audience. More than half 
of the tweets in this study were retweets. Higher engagement (a combination of retweets, 
replies and likes) was associated with positively toned tweets compared to negative or neutral 
tweets. This finding is in contrast with previous literature, which found negatively toned tweets 
to elicit higher engagement (Meng et al., 2018; Zhu et al., 2020). More investigation is needed, 
but our findings suggest as communicators seek to elicit engagement with content regarding 
gene editing in agriculture, they should consider creating messages with an optimistic or 
affirmative sentiment instead of fearful or indifferent tones.  
 
Peaks and valleys in the datasets are cause for further inquiry into the information shared 
during a given month that received the greatest reach or engagement among the gene-editing 
in agriculture conversation on Twitter. A review of the top tweets in terms of reach and 
engagement by month reveal timely studies as well as policy speculation and updates regarding 
applications of gene-editing garner heightened attention. This suggests people are curious 
about gene-editing and also follow the regulatory journey of its applications. 
 
In the future, researchers should consider a longitudinal study to see how characteristics of the 
Twitter discourse around gene editing in agriculture changes over time with the advancement 
and prevalence of the technology. Researchers should identify opinion leaders in gene editing 
to understand who may be driving the innovation-decision process among the public and across 
subsequent community networks.   
 
To better understand public opinion of gene editing in agriculture, conversation about the topic 
could be monitored on other social media platforms as well as public opinion polls. A content 
analysis of the mass media channels on Twitter sharing information regarding gene editing in 
agriculture could give insight into what information readers are gathering about the topic. In 
addition, researchers should work with practitioners to develop and experimentally test 
messages with different sentiments to identify performance differences in a more controlled 
environment.  
 

References 

Ahmed, Y. A., Ahmad, M. N., Ahmad, N., & Zakaria, N. H. (2019). Social media for knowledge-
sharing: A systematic literature review. Telematics and Informatics, 37, 72–112. 
https://doi.org/10.1016/j.tele.2018.01.015  

 
Berger, J. (2014). Word of mouth and interpersonal communication: A review and directions for 

future research. Journal of Consumer Psychology, 24(4), 586–607. 
https://doi.org/10.1016/j.jcps.2014.05.002   

 



Hill et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v3i2.187   44 
 

Bishop, R. (n.d.). How is sentiment assigned? Retrieved from 
https://help.meltwater.com/en/articles/4064558-how-is-sentiment-assigned  

 
Boyd, D. M., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. 

Journal of Computer-Mediated Communication, 13(1), 210–230. 
https://doi.org/10.1111/j.1083-6101.2007.00393.x 

 
Brossard, D. (2019). Biotechnology, communication and the public: Keys to delve into the social 

perception of science. Mètode Science Studies Journal, 9, 39–45. 
https://doi.org/10.7203/metode.9.11347 

 
Busquet, F., & Vinken, M. (2019). The use of social media in scientific research and creative 

thinking. Toxicology in Vitro, 59, 51–54. https://doi.org/10.1016/j.tiv.2019.04.006 
 
Doxzen, K., & Henderson, H. (2020). Is this safe? Addressing societal concerns about CRISPR-

edited foods without reinforcing GMO framing. Environmental Communication, 14(7), 
865–871. https://doi.org/10.1080/17524032.2020.1811451    

 
Field, A. (2017). Discovering statistics using IBM SPSS statistics (5th ed.) SAGE. 
 
Funk, C., Gottfried, J., & Mitchell, A. (2017, September 20). Science news and information today. 

Pew Research Center. https://www.journalism.org/2017/09/20/science-news-and-
information-today/  

 
Gan, B. (2021, June 14). The importance of social media metrics - definitions and calculations. 

Meltwater. https://www.meltwater.com/en/blog/measure-your-clients-goals  
 
Gil de Zúñiga, H., Jung, N., & Valenzuela, S. (2012). Social media use for news and individual’s 

capital, civic engagement and political participation. Journal of Computer-Mediated 
Communication, 17(3), 319–336. https://doi.org/10.1111/j.1083-6101.2012.01574.x  

 
Grabbert, M., Khoder, W. Y., Gratzke, C., Paffenholz, P., Salem, J., & Bauer, R. M. (2019). 

Comprehensive analysis of Twitter activity on #Incontinence. Neurourology and 
Urodynamics, 39(1), 440–446. https://doi.org/10.1002/nau.24227 

 
Guertin, L. E., & McGuire, R. M., & Torres, A. (2018). Public perception of human applications of 

CRISPR gene editing (Interactive qualifying project). Worcester Polytechnic Institute. 
https://digitalcommons.wpi.edu/iqp-all/2911/  

 
Huang, S., Weigel, D., Beachy, R. N., & Li, J. (2016). A proposed regulatory framework for 

genome-edited crops. Nature Genetics, 48(2), 109 –111. 
http://dx.doi.org/10.1038/ng.3484  

 



Hill et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v3i2.187   45 
 

Hughes, D. J., Rowe, M., Batey, M., & Lee, A. (2012). A tale of two sites: Twitter vs. Facebook 
and the personality predictors of social media usage. Computers in Human Behavior, 
28(2), 561–569. https://doi.org/10.1016/j.chb.2011.11.001  

 
Johnson, N. (2015, December 21). It is practically impossible to define ‘GMOs’. Grist. 

https://grist.org/food/mind-bomb-its-practically-impossible-to-define-gmos/  
 
Kadam, S. A., & Joglekar, S. T. (2013). Sentiment analysis: An overview. International Journal of 

Research in Engineering & Advanced Technology, 1(4), 1–7. 
http://www.ijreat.org/Papers%202013/Issue4/IJREATV1I4016.pdf  

 
Kee, K. F., Sparks, L., Struppa, D. C., Mannucci, M. A., & Damiano, A. (2016). Information 

diffusion, Facebook clusters, and the simplicial model of social aggregation: A 
computational simulation of simplicial diffusers for community health interventions. 
Health Communication, 31(4), 385–399. 
https://doi.org/10.1080/10410236.2014.960061  

 
Kraft, P. W., Krupnikov, Y., Milita, K., Ryan, J. B., & Soroka, S. (2020). Social media and the 

changing information environment: Sentiment differences in read versus recirculated 
news content. Public Opinion Quarterly, 84(S1), 195–215. 
https://doi.org/10.1093/poq/nfaa015    

 
Laerd Statistics (n.d.). Kruskal-Wallis H test using SPSS Statistics. Statistical tutorials and 

software guides. Retrieved from https://statistics.laerd.com/spss-tutorials/kruskal-
wallis-h-test-using-spss-statistics.php  

 
López-Goñi, I., & Sánchez-Angulo, M. (2018). Social networks as a tool for science 

communication and public engagement: Focus on Twitter. FEMS Microbiology Letters, 
365(2), 1–4. https://doi.org/10.1093/femsle/fnx246  

 
Meng, J., Peng, W., Tan, P. N., Liu, W., Cheng, Y., & Bae, A. (2018). Diffusion size and structural 

virality: The effects of message and network features on spreading health information 
on Twitter. Computers in Human Behavior, 89, 111–120. 
https://doi.org/10.1016/j.chb.2018.07.039  

 
Metje-Sprink, J., Menz, J., Modrzejewski, D., & Sprink, T. (2019). DNA-free genome editing: Past, 

present and future. Frontiers in Plant Science, 9, Article 1957. 
https://www.doi.org/10.3389/fpls.2018.01957  

 
Mitchell, A., Shearer, E., & Stocking, G. (2021). News on Twitter: Consumed by most users and 

trusted by many. Pew Research Center. 
https://www.pewresearch.org/journalism/2021/11/15/news-on-twitter-consumed-by-
most-users-and-trusted-by-many/  

 



Hill et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v3i2.187   46 
 

Molteni, M. (2019, March 12). The WIRED guide to Crispr. WIRED. Retrieved from 
https://www.wired.com/story/wired-guide-to-crispr/  

 
Pennell, M. (2016). More than food porn: Twitter, transparency, and food systems. 

Gastronomica: The Journal of Critical Food Studies, 16(4), 33–43. 
https://doi.org/10.1525/gfc.2016.16.4.33  

 
Resch, B., Summa, A., Zeile, P., & Strube, M. (2016). Citizen-centric urban planning through 

extracting emotion information from Twitter in an interdisciplinary space-time linguistic 
algorithm. Urban Planning, 1(2), 114–127. https://doi.org/10.17645/up.v1i2.617  

 
Roberts, H., Resch, B., Sadler, J., Chapman, L., Petutschnig, A., & Zimmer, S. (2018). Investigating 

the emotional responses of individuals to urban green space using Twitter data: A 
critical comparison of three different methods of sentiment analysis. Urban Planning, 
3(1), 21–33. https://doi.org/10.17645/up.v3i1.1231  

 
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press. 
 
Rose, K. M., Brossard, D., & Scheufele, D. A. (2020). Of society, nature, and health: How 

perceptions of specific risks and benefits of genetically engineered foods shape public 
rejection. Environmental Communication, 15(7), 1017–1031. 
https://doi.org/10.1080/17524032.2019.1710227    

 
Scheufele, D. A., Jamieson, K. H., & Kahan, D. (2017). Conclusion – on the horizon: The changing 

science communication environment. In K. H. Jamison, D. Kahan, & D. A. Scheufele 
(Eds.), The Oxford handbook of the science of science communication (pp. 461–467). 
Oxford University Press. 

 
Shew, A. M., Nalley, L. L., Snell, H. A., Nayga Jr., R. M., & Dixon, B. L. (2018). CRISPR versus 

GMOs: Public acceptance and valuation. Global Food Security, 19, 71–80. 
https://doi.org/10.1016/j.gfs.2018.10.005    

 
Siebert, M. M. (2019). Genetically modified salmon and the future of food: A qualitative content 

analysis of Twitter [Doctoral dissertation, The University of New Mexico]. ProQuest One 
Academic. https://digitalrepository.unm.edu/cj_etds/125/  

 
Tagliabue, G. (2015). The nonsensical GMO pseudo-category and a precautionary rabbit hole. 

Nature Biotechnology, 33(9), 907–908. https://doi.org/10.1038/nbt.3333 
 
Turnbull, C., Lillemo, M., & Hvoslef-Eide, T. A. K. (2021). Global regulation of genetically 

modified crops amid the gene edited crop boom–A review. Frontiers in Plant Science, 
12, 258, Article 630396. https://doi.org/10.3389/fpls.2021.630396  

 



Hill et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v3i2.187   47 
 

Varma, V., Kurisinkel, L. J., & Radhakrishnan, P. (2017). Social media summarization. In E. 
Cambria, D. Das, S. Bandyopadhyay, & A. Feraco (Eds), A practical guide to sentiment 
analysis (pp. 135–153). Springer. https://sentic.net/practical-guide-to-sentiment-
analysis.pdf  

 
Whelan, A. I., Gutti, P., & Lema, M. A. (2020). Gene editing regulation and innovation 

economics. Frontiers in Bioengineering and Biotechnology, 8, Article 303. 
https://doi.org/10.3389/fbioe.2020.00303  

 
Wirz, C. D., Scheufele, D. A., & Brossard, D. (2020). Societal debates about emerging genetic 

technologies: Toward a science of public engagement. Environmental Communication, 
14(7), 859¬864. https://doi.org/10.1080/17524032.2020.1811478  

 
Wojcik, S., & Hughes, A. (2019, April 24). Sizing up Twitter users. Pew Research Center. 

https://www.pewresearch.org/internet/2019/04/24/sizing-up-twitter-users/  
 
Zhu, X., Kim, Y., & Park, H. (2020). Do messages spread widely also diffuse fast? Examining the 

effects of message characteristics on information diffusion. Computers in Human 
Behavior, 103, 37–47. https://doi.org/10.1016/j.chb.2019.09.006  

 
 
 
 
 
 
 
 
 
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