Food Protection Trends - May/June 2022 - 175

Instagram, partner social media accounts, and news media,
to notify the public about ongoing multistate foodborne
outbreaks and inform people about what they can do to protect
themselves from getting sick. Public notification is an essential
part of the public health response effort, providing timely
access to reliable information and encouraging the public to
make informed choices to prevent additional illnesses (11).
Outbreak investigations often require unique public health
messages, making timely and accurate public communication
essential for preventing illnesses. Public communication
during outbreaks serves several functions, including notifying
consumers of an outbreak, sharing information about the
results of an investigation, providing advice, and enumerating
actions consumers and retailers can take to protect themselves
and their customers. Such actions can include avoiding eating
or selling certain foods for a specific time, returning or discarding
foods, following routine food safety recommendations, and
seeking health care. The CDC uses two Web-based communication
tools to disseminate information about foodborne
outbreaks to the public at different phases of an outbreak investigation:
food safety alerts (FSAs) and investigation notices
(INs) (4). An FSA provides urgent, specific advice to consumers,
restaurants, and retailers about foods to avoid eating or
selling. This advice often includes information about a recall or
other warnings and is used when an outbreak vehicle has been
identified. An IN provides information about an outbreak not
yet linked to a food source or an outbreak linked to a general
type or category of food rather than a specific food and often
reiterates existing food safety recommendations. Both FSAs
and INs could be used to communicate about a single outbreak.
For example, early in an investigation when the outbreak
vehicle has not yet been identified but communication about
the outbreak is needed, an IN could be used. Later, when the
outbreak vehicle is identified, an FSA may be issued.
Facebook is one of the social media platforms that the
CDC uses to communicate with the public. More than
1.62 billion global users visit Facebook daily for a variety of
reasons, including to get news, for entertainment, and to keep
in contact with friends and family (1). As of February 2021,
the CDC's Facebook account had >3.8 million followers
and is used to promote health campaigns, share healthrelated
messaging and resources, and notify the public of
disease outbreaks. The CDC develops multistate foodborne
outbreak-related Facebook posts based on the content of the
FSAs and INs.
To better inform future communication efforts with the
public during multistate foodborne outbreaks, we conducted
a qualitative content analysis of comments on CDC-developed
multistate foodborne outbreak Facebook posts from
September to December 2018. This is the first in which we
have systematically assessed responses to the CDC's outbreak
messaging on Facebook. The objective of this project was to
better understand how people respond to the CDC's messages
about multistate foodborne outbreaks. Although FSAs and INs
are both used to communicate to the public about foodborne
outbreaks, it is unclear how people respond to these different
types of messages. We compared people's responses to Facebook
messages created from FSAs with people's responses to
INs to evaluate any meaningful differences.
MATERIALS AND METHODS
We analyzed comments on foodborne outbreak posts on the
CDC's Facebook page during from 1 September to 31 December
2018. Sprout Social, a social media management platform, was
used to export all comments to an Excel spreadsheet (Microsoft,
Redmond, WA). The critical data elements that were exported
included the date and time of the comment, the name of the
Facebook user, the comment, and the URL for the Facebook
post to which the comment was related.
To be included in the final dataset for analysis, a comment
had to be (i) created within 7 days of the CDC's foodborne
outbreak Facebook post, (ii) created by a user whose privacy
option was set as " public, " and (iii) a comment directly
responding to the CDC's foodborne outbreak Facebook post
and not a reply to someone else's comment. User profiles with
privacy options not set to " public " were intentionally excluded
to respect the intentional action of the users in setting the
privacy options. To distinguish between comments on the
CDC's foodborne outbreak posts and direct replies to those
comments, the original outbreak post was cross-checked on
the CDC's public Facebook page because the data export
alone did not contain information to differentiate between the
two comment types. On the Facebook post, comments to a
post versus a reply are clearly distinguished by an indentation
under an original comment. Before data analysis, all personal
information was removed from the database including any
information that could link someone back to a specific user's
profile (name of the user). Emojis were not available to export
into Excel, so they were excluded from the analysis.
The CDC provides critical information about the foodborne
outbreaks that it announces, including how many people
are sick, what state sick people live in, foods implicated in
the investigation (when known), advice to consumers and
retailers, and signs and symptoms of the illness. The choice of
which of the two Web-based communication tools to use (FSA
or IN) depends on the stage of the investigation. Once FSAs
and INs are developed, key messages from them are posted
to the CDC's social media accounts, including Facebook.
Outbreak posts on Facebook were divided into two categories
for this analysis: posts resulting from an IN and posts resulting
from an FSA.
A traditional inductive coding methodology or grounded
theory approach (9, 14) was used to analyze comments on
the CDC's foodborne outbreak Facebook posts. To start the
analysis process, coders began by reading through the text data
to familiarize themselves with the comments. After this first
pass through the data, coders then began labeling Facebook
comments with various codes to categorize a segment of text
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