=Paper=
{{Paper
|id=Vol-3022/paper6
|storemode=property
|title=Simulating the Impact of Personality on Fake News
|pdfUrl=https://ceur-ws.org/Vol-3022/paper6.pdf
|volume=Vol-3022
|authors=Annabel Coates,Tim Muller,Sean Sirur
|dblpUrl=https://dblp.org/rec/conf/atal/CoatesMS21
}}
==Simulating the Impact of Personality on Fake News==
https://ceur-ws.org/Vol-3022/paper6.pdf
Simulating the Impact of Personality on Fake News
Annabel Coates Tim Muller Sean Sirur
University of Nottingham University of Nottingham University of Oxford
annabelcoates@gmail.com tim.muller@nottingham.ac.uk sean.sirur@gmail.com
Abstract
Fake news is a key issue for social networks. We use an agent-based
network simulation to model the spread of (fake) news. The agents’ be-
haviour captures the OCEAN (“big five”) personality trait model. The
network is homophilic for political preference, analytical thinking and
emotion. We studied the system with personality traits and homophily
each turned on/off. Personality traits and homophily exhibited a sta-
tistically significant but typically minimal impact. Ignoring personality
traits when modelling fact-checking can overestimate its effectiveness.
1 Introduction
The spread of false information or “fake news” is a key issue on online social networks (OSNs). With 55% and
49% of US and UK adults now getting news from social networks [SG19, Ofc19], the effects are broad and potent.
For example, fake news has been shown to spread more effectively than true news on social media [VRA18]. In
the medical domain, misinformation was present in 40% of top links relating to common diseases [WKWK18].
This fuels dangerous ideas among the public [CMP19] and leads to avoidable public health crises [Hot16].
To counter fake news, it is essential to investigate both the nature of its spread as well as the OSN users who
create and share it, at times unintentionally. From a user’s perspective, there is an inherent uncertainty in the
verity of a news item. Clearly, trust is an important factor in the spread of news on OSNs, both in terms of
shares and views, and an honest user will not share a news item they distrust. The trustworthiness of a news
item is also deeply related to its believability: with users’ susceptibility to fake news varying with e.g. analytical
thinking skills [PR19b]. Still, it is important to recognize that whether a news item is true or fake is not the
only factor in a user’s decision to share it.
We hypothesise that users’ personalities affects how and why (fake) news spreads. Various prior works study
the effects of personality on OSN usage [CCTS14, ROS+ 09, AV10]. As shown by [CCT17], understanding
how personality affects the spread of news is important. Agent-based models can be used to capture rich user
properties such as detailed internal states, allowing for behaviours of greater depth. As such, we use these results
to implement personality effects in the users.
Network models are used in a range of disciplines including sociology, computer science and economics. As
such, their use extends naturally to the study of OSNs. The presence of homophily, the tendency for individuals
to connect to those similar to themselves, is common to social networks [DJH+ 16, MSLC01]. Simulations of
social networks that do not take homophily into account cannot have accurate network structures [AMS13]. Yet,
its effects on (fake) news spreading is relatively unexplored. We implement a network that is homophilic for
political leaning, analytic thinking skills and emotional state.
Copyright ©2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC
BY 4.0).
In: R. Falcone, J. Zhang, and D. Wang (eds.): Proceedings of the 22nd International Workshop on Trust in Agent Societies, London,
UK on May 3-7, 2021, published at http://ceur-ws.org
1
The aims of this study are to: 1) Account for personality traits in user behaviour 2) Account for homophily
in the network structure 3) Determine the impact of these features on the spread of fake news.
2 Relevant literature
In order to accurately simulate user behaviour in OSNs, it is important to consider social and psychological
studies. Specifically, how users interact with OSNs [AM15], how personality traits affect use of OSNs [CCTS14,
AV10, ROS+ 09] and how individuals perceive fake news [PR19b, SCM20, PCR18].
We also discuss similar agent-based studies and their findings. Including the use of agent-based modelling as
an effective way of studying the spread of fake news, and comparison of simulation methods.
2.1 Social Science
One study explored user interactions within OSNs [AM15]. Descriptive data from surveys were used to identify
motivations for using different social networks and the interactions within them. It found that, for Facebook
use, entertainment and information sharing were the most valued reasons. I was also found that content is
more likely to be “liked” than shared on Facebook. Usage motivations may also be affected by personality
which must be considered when interpreting such results. The authors provide a quantitative analysis of users’
motivations for using Facebook and partaking in certain interactions, this behavioural perspective is valuable
for simulating how users interact with other users and posts. However, relationships and data obtained in this
study are representative of a relatively small demographic, as the participants of the surveys were all college
aged. Therefore the study is limited in representing older users that are less computer literate [MP12] and can
play a large role in the spread of fake news [Cho19].
[PR19b] study the cause of susceptibility to fake news. In particular, they study the extent to which suscep-
tibility lies in motivated reasoning (wanting to believe something is true) or failing to identify that something
is false (lack of analytical thinking). Participants completed cognitive reasoning tests and rated their perceived
accuracy of fake and true news headlines. From this it was found that analytic thinking gave participants a
higher ability to differentiate between true and fake news. This led the authors to conclude that failing to
discern between fake and true news was the more significant factor than partisanship, in believing it.
In line with [PR19b], [SCM20] find that analytical thinking is negatively related to susceptibility to fake
news. An additional finding by [SCM20], is that participants believe the news consistent with their own political
beliefs to be more accurate. Therefore the combined findings of [PR19b] and [SCM20], tell us that while analytic
thinking is the primary factor in susceptibility to sharing fake news, (politically) motivated reasoning must also
be taken into account. Our model uses these idea in having agents assess the news.
Caci et al [CCTS14] provide a detailed analysis on how the OCEAN personality variables [Gol93] affect
Facebook use (e.g. the amount of connections made or the session frequency and length). Paired with the
distribution of personality traits in the population, this represents an accurate model of the population’s use of
Facebook. We incorporate the following relationships from Caci et al in our simulation:
• Conscientiousness (B=-0.18) and agreeableness (-0.21) are predictors of lower frequency of use.
• Extroversion (B=0.12) and neuroticism (B=0.14) are predictors of high frequency of use.
• Conscientiousness (B=-0.16) is a predictor of low session length.
• Extroversion (B=0.24) and neuroticism (B=0.14) are predictors of high session length.
• Conscientiousness (B=-0.28) and agreeableness (B=-0.28) are predictors of a lower number of connections.
• Openness (B=0.24) and extroversion (B=0.47) are predictors of a higher number of connections.
Given that Caci et al studied only the population of Italy, our results apply primarily to that population.
However, the OCEAN model aims to provide an exhaustive, universally understood representation of personality.
As such, our results help to lay a foundation upon which future research may generalize.
Similarly to [CCTS14], [ROS+ 09] use self-report personality tests and a questionnaire of Facebook use, to
gather data on the link between the OCEAN personality traits and Facebook use. They find that an individual’s
’motivation to communicate’ was largely influential on how they used Facebook. They also make predictions
relating a willingness to share personal information to neuroticism, and the use of a range of communication
features with extroversion. These findings have been used within this study to determine how frequently different
users share posts in general (share probability).
2
Based on the work of [ROS+ 09], [AV10] seek to determine relationships between personality and Facebook
use. They do this using ”more objective” data than [ROS+ 09], gathering data from participants’ Facebook
profiles. In agreement with [CCTS14] they find that highly extroverted individuals have a ”signficantly higher
number of friends”. However some of their findings disagree with those of [CCTS14], including that: 1) Higher
agreeableness is not a predictor more Facebook friends, and 2) conscientiousness is a predictor of more Facebook
friends. In agreement with [ROS+ 09]’s predictions, [AV10] find highly neurotic individuals post more personal
information, providing confidence in this result’s accuracy.
2.2 Data Analyses
In [GRG+ 20], the aim is to determine the link between personality and linguistics of users, and their likelihood
to share fake news, or fact-check. That research question is similar to ours. They create a convolutional neural
network (CheckerOrSpreader) that distinguishes between users based on linguistic patterns and features that
represents users’ personality traits within tweets. The results of the study show that the model has improved
performance at identifying spreaders and fact-checkers when personality and linguistic features are taken into
account. These results can be directly compared to the results of this study in reference to whether personality
affects the spread of fake news, despite the differences in methodology. As well as using real data instead of
simulated behaviour, Gianchanou et al.’s study focuses on spreading behaviour on an individual level, whereas
this study investigates the effect of personality on the spread throughout a network.
The authors of [VRS18] aim to determine how and why fake news spreads differently from true news. The
authors analyse the properties of fake news content, the spread of fake news and properties of susceptible users.
They find results applicable to this model, such as that users that spread fake news tend to follow fewer people,
and have fewer followers. This provides insight into other network properties that could affect the spread of fake
news.
Through content and linguistic analysis, [VRS18] find that certain categories of fake news – politics and urban
legends – are particularly susceptible to large spread. The importance of the category of news found provides
motivation to take into account individual interests, as is done so in this model through the political leaning
of users and news. They find a higher emotional content in fake news, with replies and comments expressing
greater levels of disgust and surprise than true news. This supports the use of emotional level as an important
attribute of news articles, as in this model.
Key findings on homophily in social networks are found by [DJH+ 16], who focus on homophily in twitter
users of similar moral purity, determined by analysing tweet content. They find that social networks reflect
moral selection and that offline and online differences in moral purity are particularly predictive of distance
between two individuals in the social network. This supports the modelling of homophily in this study, in
particular, homophily around political leaning as this is contextually similar to moral purity.
2.3 Other simulation studies
Jin et al. create a epidemiological model of the spread of true and fake news on Twitter based on data analysis
of 8 viral stories [JDS+ 13]. The population is modelled with 4 possible states for users to be in (Susceptible,
Exposed, Infected and Sceptical), when in certain states users can ’infect’ other users and spread fake news. They
prove that this model is accurate in representing the spread of both true and fake news. As well as this, they
generate specific results such as that users obtain true news from many sources versus limited or single sources
for fake news, causing an initial spike in shares when true news is first made public, which is not present for fake
news. Our agent-based approach investigates whether such a model of uniform agents is realistic; specifically,
whether it is fine to ignore personality.
In [BFP11], the authors create a study simulating an OSN (Facebook) in an agent-based approach (as we do).
They model Facebook by defining 4 groups of users (typical user, power-user, town crier, deep-end diver) with
different behavioural characteristics. These groups are then given representative weights within the population.
They create a model of Facebook that reproduces the real world phenomena of increased connectivity in the
community and increased heterogeneity of the population with time. Whereas they use 4 categories of users with
their own characteristics, we use personality traits as hidden variables determining characteristics.
3 Model description
The simulation model for this study uses an agent-based modelling approach. Agents are discrete entities with
their own goals and behaviours, their defining quality is making decisions based not only on their environment
3
but also their own internal state. Of particular importance to our study are personality traits of agents.
In this model, the agents are users of an online social network based on Facebook. It is effective for users to
be modelled as agents, as their actions depend on both the environment they are in (such as the posts they view
and other users) and their internal state. As explained, we suppose that people with similar political leaning,
analytical skills and emotional state tend to cluster together – a network property known as homophily [New18].
As our study focuses on the effects of personality traits and homophily, our simulation can run with personality
traits and homophily individually being turned on, down or off.
3.1 Modelling Entities
In order to portray an individual’s behaviour towards news seen online, the effect of personality [CCTS14, AV10],
analytic thinking [PR19b] and interests (i.e political leaning) [PR19b] need to be considered.
Personality traits. In this model, personality is represented using the OCEAN (or “big five”) model of
personality traits as defined in [Gol93]: openness, conscientiousness, extroversion, agreeableness and neuroticism.
We implement the results of Caci et al [CCTS14] into the model directly.
For simulation purposes, we introduce a personality factor, P , a value between 0 and 1, determined the
weighting that the different personality traits have in the model. We set all other parameters, such that their
distribution remains unchanged as we remove the effect of personality traits. This way any effect resulting from
changing P must be due to personality traits interacting with other variables.
When creating a population of people within the model, a normal distribution of the prevalence of each
personality trait within the 2015 UK population was used [RJL15] to ensure the prevalence within the model
reflected that of the real world.
The weights from Caci et al.’s path analysis [CCTS14] are taken directly to determine an individual Facebook
user’s number of friends, frequency of use and session duration within the model. If P = 1, these values are
based purely on openness (o), conscientiousness (c), extroversion (e), agreeableness (a), neuroticism (n). A user’s
tendency to have a large network, C is defined as:
C = 0.24o − 0.28c + 0.47e − 0.28a + 0.2d1 (1)
A users frequency of use, f is defined as:
f = −0.18c + 0.12e − 0.21a + 0.14n, (2)
A user’s session length,L is defined as:
L = −0.16c + 0.24e + 0.14n (3)
A user’s sharing tendency,w is defined as (after [AV10]):
n+e
w= (4)
2
The variables were diluted and normalised as required, depending on the personality factor P . In the case that
P = 0 these variables are randomly distributed along the same prior distribution.
Analytic thinking. Each individual also had an analytic thinking value, representing a user’s general
likelihood of believing fake news. This captures analytic thinking as described in the literature [PR19b, SCM20].
While some studies find a link between personality traits and analytic thinking [AR20, Yam20, CBK04], it was
decided that defining the traits independently was of more value to this model.
Political leaning The political leaning attribute is used to determine the level of relevancy that a specific
news article has to an individual. Although this attributed is named political leaning, it encompasses all interests
and opinions that a user holds. The magnitude of the difference between the political leaning of an individual
and a news article they are viewing, determines the relevancy to an individual. A higher relevancy leads to a
higher probability of sharing.
Emotional state Due to the finding that news that invokes more of an emotional response receives more
shares [VRS18], each user in this model has an attribute, emotional state, to represent their susceptibility to
emotional content. This was independent of personality within this model.
Assessing News When viewing a true or fake news article online, an individual’s likelihood of sharing the
news depends on a number of factors. In this model three factors are used to determine this likelihood:
1. Believability: the extent to which the person believes the news is true. This factor has been found to have
a large effect on the sharing of fake news [PR19b]
4
2. Relevancy: how much the news aligns with the person’s (political) beliefs. Modelled as 1 dimensional.
3. Emotional level: the level of emotional response that the news invokes in the viewer, for example Vosoughi
et al. link the likelihood of sharing fake news articles with their emotional content.
Believability is calculated from:
B = a · (bnews − 1) + 1 = a · bnews + (1 − a) (5)
where bnews measures the intrinsic believability of the news and a measures the analytic thinking of the
viewer. For high analytical thinkers B ≈ bnews , meaning they estimate the believability of the news well. For
low analytical thinkers, B ≈ 1, meaning they tend to believe most news, regardless of its veracity.
The emotional response invoked by a piece of news, is related to both the emotional content of the news and
how susceptible the viewer is to emotional content. One measure of this is a person’s level of neuroticism, as
it is an indicator of emotional instability [CM99, ORR12, Gol93]. Due to this, the emotional factor, E, in this
model was determined as:
E = n · mnews , (6)
where n is the individual’s neuroticism and mnews is the emotional level of the news (for P = 1).
Finally, the relevancy factor of the news exists to measure the degree to which the news relates to a person
and their life. Therefore the relevancy factor is negatively dependent on how different the political leaning of
the news is and the person’s political leaning of topics are. It is therefore calculated from:
R = max(0.4 − 0.4 · |lnews − lperson |, 1 − 5 · |lnews − lperson |) (7)
where lnews and lperson are the political leaning of the news and of the person respectively. The formula is
sensitive to nearby leanings, but still allows for non-zero values for further news.
These factors are used to determine the share probability, S, for a specific person and news article as follows:
2 · B + R + 1.8 · E · s
S =w· , (8)
4
where w is a person’s share tendency, R is the relevancy factor, E is the emotional factor, s is the person’s
emotional state and B is the believability factor.
[PR19a] and [PR19b] found it is a belief or lack of belief in a news article that most significantly limits the
spread of news in OSNs, hence, in the formula B is weighted 2. The factor 1.8 is to compensate for the fact that
s is a fractional value between 0 and 1 – on average 1.8 · s ≈ 1.
News Differences in the content and perception of news was modelled via attributes measuring emotional
level, believability and political leaning. These attributes were given normally distributed values between 0 and
1. Believability is a measure of the intrinsic credibility of a news item. Belief in a news item depends on its
believability and the individual’s online literacy. It was found that, on average, social media users are “quite
good” at distinguishing between news sources of different quality [PR19a]. As such, the believability of fake
and true news were each generated from a low distribution with µ = 0.2, σ = 0.25 and a high distribution with
µ = 0.8, σ = 0.25, respectively. Political leaning determines the news’ level of relevancy to an individual viewer.
Political leaning is modelled neutrally and symmetrically along a “left-right” axis.
3.2 Modelling the Network and Homophily
OSN structure is captured through mathematical graphs or networks. Such networks are composed of nodes,
representing the OSN users, and links, representing two users mutually “following” each other.
One crucial activity in the study of real-world networks is the development of algorithms that can generate
realistic simulated networks. The properties of such networks depend on the algorithm as different ones each
capture different properties that are believed to be common to social networks. Two foundational algorithms
are described here:
Barabasi-Albert (BA) model: This algorithm models “preferential attachment”. That is, nodes are more
likely to be attached to a node that already has many other attachments.
Connected Watts-Strogatz (CWS) model: This model ensures the network exhibits high clustering (the
number of actual links between a node’s neighbours divided by the number of potential links between them) and
small-world effects (the average number of hops between any two nodes grows “slowly” with the network size).
We ran our simulations across both these types of networks, finding similar results. Except Figure 4, all figures
are computed using the Barabasi-Albert model with degree 30. Figure 4 is included as a typical comparison.
5
(a) All news (b) True news (c) Fake news
Figure 1: Plots of how often news is shared over time (topology BA).
(a) All news (b) True news (c) Fake news
Figure 2: Plots of how often news is viewed over time (topology BA).
(a) All news (b) True news (c) Fake news
Figure 3: Plots of how often news goes viral over time (topology BA).
6
(a) All news (b) True news (c) Fake news
Figure 4: Plots of how often news is viewed over time (topology CWS).
We implement a degree of homophily in the network structure. Neighbours are likely to share political leaning,
analytic thinking and emotional state. We do not assume homophily of personality traits directly. The degree of
homophily can be varied. At full homophily, we generate the attributes based on neighbours, ensuring that the
result will be distributed according to a specific distribution. As such, homophily does not skew the distributions
of political leanings, analytical thinking and emotion.
4 Findings
4.1 News Spreading over Time
In this section, we look at the effect that personality traits have on the spread of news. We can consider true
news, fake news, and all news. We found that personality traits have some limited effect on the spread of news.
There are various ways to measure the spread of news e.g. how often news is shared. It is possible for some
news to spread via a few influential nodes to a disproportionately large audience. As such, the average size of
the audience may be a good measure. In practice, a lot of news either fails to reach a significant audience, or it
plateaus at a point where nearly everyone has seen it. We refer to the later as going viral, and we can have a
look at what proportion of news goes viral as a measure of spread too. We present three measures in Figures 1-4,
discussed below.
The graphs in Figures 1-4 are plotted against time steps. Agents in the system can act (e.g. view and/or
share news) every time step. After enough time steps, the system stops. We have chosen to cut off after 400
time steps, as most things happen in 300 time steps.
In Figures 1, 2 and 4, each data point is the average of 50 runs with 200 true news articles and 100 fake news
articles each (so 15,000 news articles of which 10,000 true and 5,000 fake). As a result lines have little random
variation. In Figure 3, however, each datapoint represents the average of 50 runs, of the proportion of news
articles that goes viral. The effective sample size is much smaller, and thus it has some random variation.
Figures 1-3 are based on random Barabasi-Albert networks. We have run the same experiments on Connected
Watts-Strogatz as well, and the results are similar, even if the exact numbers differ a bit. To illustrate, we
present the statistics for the number of views in Figure 4. We focus on BA topology only, for brevity sake.
Each of the graphs in Figures 1-4 has 2 plots, a gray (solid) plot, and a (black) dashed plot. The difference
is in whether personality traits are used in the simulation. The gray plot does not use personality traits, and
the dashed plot does. The pairs of plots in Figures 1 and 2 are very similar. But in subfigures (c), there is a
noticeable, albeit minor difference; this difference is not random. In Figure 3, the difference between the pairs of
plots is larger, but there is randomness in the plots. The difference in Figure 3(c) is not caused by randomness.
Personality traits do have a noticeable effect (12% difference) on the rate with which fake news is going viral.
If an estimation error of 12% for a simulation or data analysis is not acceptable, then the model must take into
account personality traits.
4.2 Personality Traits
In this section, we have a further look at the effect of personality traits. Recall that we can gradually adjust
the effect of personality traits from none to full. We refer to this as the personality factor P . Recall that
the personality factor only affects how much impact the personality traits have. The distribution of the other
7
(a) Shares per person (b) Views per person (c) Ratio views per share
Figure 5: Plots of shares/views relative to the influence of personality traits.
variables (share tendency; time and frequency of Facebook use; and the way news is assessed) remains the same
– but loses its correlation with personality as P goes to 0. Since these distributions remain constant over P , any
effect of adjusting P on the spread can be expected to be subtle.
Here, we have run 100 runs, for the values P ∈ {0, 0.2, 0.4, 0.6, 0.8, 1}, each data point is the mean amount of
shares/views per person of any/true/fake news. There are 1000 people to take the average from. However, if one
news article more or less goes viral, this will result in 100’s of people viewing/sharing the article. The average of
the 1000 participants is therefore relatively volatile. Since we expect any effect to be subtle, we need the larger
quantity of runs to get accurate results. The current amount of runs (100) keeps fluctuations well under 1%,
anything over that is significant as a result. The solid plots represent the total number of shares/views/ratios
per person, the dashed plots of true news, and the dash-dotted plots of fake news.
From Figure 5, “subtle effects” is an understatement. Figure 5(a) represents the sharing of news per person.
Figure 5(a) looks flat, but all three plots have a slight decrease. Only the plot for true news is (barely) significant,
decreasing by 0.24 shares of true news per person, from 19.55 to 19.31. Personality traits cause a 1.2% decrease
in true news shares. Figure 5(b) represents the views of news per person. Figure 5(b) has a marginally visible
increase. The plots for all news and for fake news are significant – again barely. The views of fake news increases
2.48% (from 66.77 to 68.47); the views of all news increases 1.41% (from 212.26 to 215.28). It is interesting to
note that the shares tend to decrease whereas views tend to increase.
We created Figure 5(c) to more clearly represent this diverging trend, by plotting the number of views per
numbers of shares (per person). With visual inspection, it’s clear that there is a statistically significant trend
upwards, of around 2.2% per plot. Since shares are decreasing, and views are increasing, it is fair to say that
the model is appropriately balanced. Meaning that increases or decreases cannot be explained by other variables
being skewed when adjusting personality traits. The only reasonable explanation for the increase in ratio, is the
effect of personality traits having a positive impact on that ratio.
The effect of personality traits exists – i.e. there are statistically significant effects. But the effects are minimal.
For a basic model of social networks, arguably, a 2.2% error is acceptable. However, there may be hidden effects
of personality traits that do not directly affect the spread of fake news, but that may interfere constructively or
destructively with other mechanisms or phenomena. As we can see in the next two sections.
4.3 Analytic Thinking
Here, we discuss how analytic thinking (AT) interacts with personality and homophily. Overall, it can be seen
that low AT is highly related to greater sharing and viewing of fake news but no decrease in that of true news.
Low AT individuals are prone to believe articles with little credibility. Personality variation, however, primarily
affects frequency of use, session length, sharing frequency and sensitivity to emotional news.
In figure 6, four investigations are presented: with personality traits turned on/off and with homophily turned
on/off. The personality traits are controlled by the personality factor P . Homophily controls whether personal
variations between neighbours correlate or not. In every investigation, the weighting factor on analytic thinking
was varied from 0 to 1. As the AT weighting decreases, both average AT and AT variation per user decrease. As
the weighting factor increases, average AT and AT variation grow. The model performed 300 runs per weighting
and the average taken to reduce the impact of random factors.
8
(a) Personality off, homophily off (b) Personality on, homophily off
(c) Personality off, homophily on (d) Personality on, homophily on
Figure 6: Plots of how often news is viewed over the degree of analytic thinking on CWS graphs.
It can be seen that true news views remain relatively high throughout. This is because the bottleneck for
sharing true news is not related to credibility but instead to emotional engagement or political leaning. This is
in line with prior studies [PR19b, SCM20, VRS18]. In Figure 6, a dashed line is true news, a dash-dotted line is
fake news and a solid line is the average. The x-axis is the amount of analytic thinking that average people are
assigned in a simulation. On the left hand side, few people are analytic thinkers, and on the right side, nearly
all people are analytical thinkers. The y-axis is the number of unique readers of an article on average.
Figure 6(a): A user’s ability to assess news as false is dependent solely on AT, with low AT users being
predisposed to believe all news. Both the high AT weight and low AT weight systems share and view true
news at a high frequency. However, as AT weights decrease, users become increasingly unable to distinguish
low-believability fake news from high-believability true news. As such, the sharing of fake news rises somewhat
above that of true news (as fake news is more likely to be emotionally engaging) and the viewership of fake news
rises to meet that of true news.
Figure 6(b): Here, the impact of personality traits (and therefore the importance of their inclusion in such
models) is highlighted. As each personality trait only affects a well-defined set of system variables, the traits
that play a crucial role can be inferred from the system behaviour.
Firstly, it can be seen that the sharing of fake news has increased at all AT weights. The directly relevant
factors (other than AT) are user sharing frequency, user connectivity and news article emotionality. These are
related only to the extroversion and neuroticism traits. Extroversion contributes to both the sharing frequency
and the connectivity of a user. Highly extroverted users will share more and have more followers. Neuroticism
contributes positively to both sharing frequency and sensitivity to emotional news. At low AT weights, the
average user becomes poor at distinguishing fake and true news. However, users high in neuroticism will respond
to the emotionality of fake news and share it with an increased frequency.
Figure 6(c): Homophily can present itself through grouping high/low AT individuals, left/right individuals
and high/low emotion individuals. With respect to the impact on this graph, it seems that grouping by AT
seems to have the largest impact: fake news shares at high AT weights are lower than for when homophily is not
present. Firstly, AT is distributed along a bell curve, so the majority of the network has a relatively high AT.
Secondly, due to homophily, these high AT agents are also more likely to connect. Therefore, the core cluster(s)
of the network will be mostly composed of high AT individuals who are relatively good at not sharing fake news
9
(a) Shares per person (b) Views per person (c) Prop. of views/shares being true
Figure 7: Plots of shares/views relative to the influence of personality traits.
Fake shares will likely occur in smaller fringe clusters of lower AT individuals. They are unlikely to spread into
the core cluster and so are unlikely to be picked up by other lower AT clusters, further lowering the spread of
fake news. The effect of AT on fake news in Figure 6(c) is therefore exaggerated compared to 6(a).
Figure 6(d): For this figure, the same effects as in Figure 6(b) and Figure 6(c) apply, but they are combined.
Notice the effect of homophily on the influence of AT on the spread of fake news is clearly the strongest effect.
Comparing the pairs of graphs of personality traits, we see that the effect of personality traits is minimal,
although it is statistically significant. Comparing the pairs of graphs of homophily, we see that the effect is more
pronounced for fake news. The effect is still limited in scope.
4.4 Fact Checking News
In this section, we implement a fact-checking mechanism. If the news is true, then users will be informed that
the news is verified true. If the news is fake, then users will be warned that the news is verified false. Of course,
this only accomplishes that users’ beliefs may shift, but not necessarily to 100% or 0%. For most people, the
belief will increase if an article is verified to be true, and decrease if it is verified to be fake. But for some people,
the opposite may be true.
If we consider personality traits, then personality traits will influence the probability of believing and valuing
the fact-checker’s judgement. Our implementation is that conscientiousness alone determines the direction and
magnitude of the effect of the fact-checker’s judgement. If personality traits are off, a similarly distributed
random value will take its place. The mean and variance of the impact of fact-checkers remains the same, for all
personality factors 0 ≤ P ≤ 1. The belief in an article before fact-checking is used as a baseline, and the belief
will shift according to the impact of the fact check.
Graphs (a) and (b) in Figure 7 are the same as in Figure 5, but with the fact-checking mechanism turned on.
There are clearly somewhat more shares/views of true news, and somewhat less shares/views of fake news. This
is the intended effect of fact-checking. There are various reasons why the effect is modest: first, there are other
factors such as emotional engagement or political leaning at play, and second, the impact of fact-checking may
be small or negative for some people. As before, the influence of personality traits is plotted on the x-axis.
The influence of personality traits is different when fact-checking is introduced; compare Figures 5 and 7. In
Figure 7, the true shares decreases by 2.3% (from 21.25 to 20.75), but the fake shares increases by 5.6% (from
6.71 to 7.11). For views, in Figure 7(b), true news does not change significantly, and fake news is seen 5.9%
more. In both cases, introducing personality traits lowers the proportion of true news versus fake news.
We plot this explicitly in Figure 7(c). The line with + is the proportion of shares that are of true news, and
the line with × is the proportion of views. Recalling that fact-checking was a measure to raise the proportion of
true news being spread, it shows that personality traits are lowering the effectiveness of the fact-checking. The
effectiveness is lowered by a percentage point. However, the total effectiveness of the fact-checking is only about
5 percentage points. That is a fairly big relative difference.
5 Conclusion
We built an agent-based simulation of the spread of fake news. The agents were modelled to have personality
traits and homophilic distribution of attributes. The numbers used to model personality traits are based on
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psychology research. The simulation provides results that are in line with the findings in the literature.
Our simulation has the ability to turn personality traits and homophily off, without affecting the general
distribution of all the characteristics of the people. We have looked at the effects of doing to on the spread of
news – and fake news in particular. It turns out that there is a statistically significant effect in all configurations
we have tried. But that the effect is limited in size. In particular, personality traits increase the number of
views per share, irrespective of whether the news is true or not. Homophily exaggerates the effects of analytic
thinking on the spread of fake news. When implementing a fact-checker, the personality traits of people work
against the countermeasures. Not modelling personality can lead a specialist to overestimate the effectiveness of
fact-checking.
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