Believe it or not: A network analysis investigating how individuals embrace false and true statements during COVID-19

Figures & data

Table 1. Conceptualizations and operationalizations of core concepts.

Concepts	Conceptualizations	Operationalizations
Belief	Accepting a statement as true regardless of the veracity of statement	If a person perceives a statement, whether true or false, to be true, it is assigned a value of 1, indicating belief; otherwise, it is assigned a 0, indicating non-belief
Misbelief	Misbelief occurred when accepting a false statement or rejecting a true statement	If a person incorrectly perceives a false statement as true or a true statement as false, it is assigned a value of 1, indicating misbelief; conversely, if the belief aligns with the statement’s veracity, it is assigned a 0, indicating accurate belief
(Mis)information network	A network consists of statement nodes and edges that connect statement nodes	The (mis)information network in this study is operationalized through a co-occurrence belief network. We employed two-mode belief networks as a basis for deriving this co-occurrence network. The co-occurrence belief network is composed of statements (nodes) and the edges that connect them. The weight between two nodes indicates the percentage of individuals who concurrently believe both statements to be true. For the purposes of this study, we derived the (mis)information network by transforming the initial two-mode belief network into a one-mode network
Two-mode Belief Network	Two-mode belief network consists of two types of nodes (respondents and statements in the study) and edges that connect the two different types of nodes based on participants’ beliefs in statements	The two-mode belief network is characterized using a two-mode network. Within this network, one set of nodes represents respondents, while the other embodies the statements. The edges in this network denote the respondents’ beliefs in these statements. For instance, an edge exists between a participant and a statement if the respondent believes the statement to be true, irrespective of whether the statement is factually accurate or not
Two-mode Misbelief Network	Two-mode misbelief network consists of two types of nodes (respondents and statements in the study) and edges that connect the two different types of nodes based on participants’ misbeliefs in statements	The two-mode misbelief network is measured using a two-mode network. Within this network, one set of nodes corresponds to respondents, while the other signifies the statements. The edges within this network embody the participants’ misbeliefs in these statements. For example, if a respondent either subscribes to a false statement or rejects a true one, an edge is subsequently established between the respondent and the corresponding statement

Table 2. Percentage of misbelief by statement.

Acronym of statement	Statement	Veracity	Overall misbelief	Misbelief among liberals	Misbelief among conservatives
FACEMASK	A face mask will protect you from COVID-19	True	46%	18%	28%
DELIBCREATED	COVID-19 was deliberately created or released by people	False	37%	12%	25%
GARLIC	Eating garlic does not help prevent COVID-19	True	22%	9%	13%
SALINE	There is no evidence that rinsing your nose with saline protects against COVID-19	True	19%	7%	12%
KNOWNCURE	There is a known cure for COVID-19	False	16%	6%	10%
SUN	Exposure to the sun prevents COVID-19	False	13%	4%	9%
HAVELIFE	Getting COVID-19 doesn’t mean that you will have it for life	True	13%	5%	8%
MOSQUITOSPREAD	Mosquitos help spread COVID-19	False	12%	5%	7%
ORDERINGFROMCHINA	Ordering or buying products shipped from China will make a person sick	False	10%	3%	8%
ONLYELDERS	Only older adults can die from COVID-19	False	10%	3%	6%
VACCINEAVAIL	A vaccine to prevent COVID-19 is available	False	10%	3%	7%
GARGLBLEACH	You can protect yourself from COVID-19 by swallowing or gargling with bleach, taking acetic acid or steroids, or using essential oils, salt water, ethanol or other substances	False	4%	1%	3%
PEPPERS	Adding peppers to your food will prevent COVID-19	False	3%	1%	2%
DRINKINGALCOHOL	Drinking alcohol helps prevent COVID-19	False	3%	1%	2%
RECOVER	You can recover from COVID-19	True	3%	1%	2%

Note: Statements are ordered based on the percentage of overall misbelief, from the highest at the top to the lowest at the bottom. Percentages are rounded at the first decimal.

Figure 1. Conversion of two-mode belief network to (mis)information network.

Note: S = Statement. Edges in the two-mode belief network occur when respondents believe statements regardless of their veracity to be true. The two-mode belief network is transformed into (mis)information network through a process known as projection. The strength of the edges in the (mis)information network is assigned based on the proportion of respondents who simultaneously believe in two distinct statements.

Figure 2. Overall (mis)information network.

Note: The shape of a node indicates the veracity of statements. Square-shaped nodes represent true statements, while round-shaped nodes represent false statements. The thickness of the line indicates the degree of connection between statements. The thicker the line between a pair of statements, the more participants co-believe them.

Figure 3. (A) Liberal and (B) Conservative (mis)information network.

Note: The shape of a node indicates the veracity of statements. Square-shaped nodes represent true statements, while round-shaped nodes represent false statements. The thickness of the line indicates the degree of connection between statements. The thicker the line between a pair of statements, the more participants co-believe them.

Figure 4. Cluster analysis results of (A) Liberal and (B) Conservative (mis)information network.

Note: The shape of a node indicates the veracity of statements. Square-shaped nodes represent true statements, while round-shaped nodes represent false statements. The color and shade of nodes indicate cluster membership. Nodes in blue color are clustered in one group, while nodes in orange color are clustered in another group. Edges between nodes are omitted for visual simplicity.

Figure 5. Disassortative and assortative patterns between liberal and conservative groups.

Note: Pairs of statements are categorized into three conditions based on veracity. If both statements are true, the pair is assigned to the true-true condition. If both statements are false, the pair is assigned to the false-false condition. If the veracity of statements is mixed, the pair is assigned to the false-true condition.

Figure 6. Bivariate graph diffusion distances of (A) the liberal group and (B) the conservative group.

Data availability statement

The data and code will be shared upon request.