Narcissism and Fame: A Complex Network Model for the Adaptive Interaction of Digital Narcissism and Online Popularity

Abstract

Social media like Twitter or Instagram play the role of a fertile platform for self-exhibition and allow their users to earn a good repute. People higher in grandiosity share their contents in a charismatic way and as a result, they are successful in gaining attention from others, which may also influence their responses and behaviors. Such attention and repute enable them to be a trendsetter or socially recognized maven. In this paper, we present a complex adaptive mental network model of a narcissist to see how popularity can adaptively influence his/her behavior. To analyze and to support behavior showed by our model, we used some key performance indicators from the literature to study the popularity and narcissism of 30 Instagram. The results of the - both computational and empirical - study indicate that our presented computational adaptive network model in general shows the behavior found from the empirical data.

Full text

Narcissism andfame: acomplex network
model fortheadaptive interaction ofdigital
narcissism andonline popularity
Fakhra Jabeen* , Charlotte Gerritsen and Jan Treur
Introduction
Narcissism reflects a personality trait which relates to a certain cluster of human behav-
iors, which display self-superiority and self-exhibition. ese behaviors mostly relate
to entitlement seeking and having a messiah complex. Narcissists need admiration and
dwell for their own appearance and achievement, which often leads to lack of empathy
for others (Bushman and Baumeister 1998; Fan etal. 2011). Social media platforms can
help narcissists to achieve popularity and have a feeling of worth for themselves, but this
can also increase their vulnerability due to the pervasive nature of social media (Bush-
man and Baumeister 1998). Different artificial intelligence (AI) techniques were used to
detect narcissism from text analysis (Holtzman etal. 2019; Neuman 2016). Also, there
are very limited computational studies addressing these behaviors. Moreover, how pop-
ularity can influence such behavior was not studied yet in more depth. Extending the
preliminary (Jabeen etal. 2019), the current paper addresses this.
e new level of connectivity through social media, provides a new way to become
popular. erefore, media such as Facebook, Twitter or Instagram can act as new
Abstract
Social media like Twitter or Instagram play the role of fertile platforms for self-exhibition
and allow their users to earn a good repute. People higher in grandiosity share their
contents in a charismatic way and as a result, they are successful in gaining attention
from others, which may also influence their responses and behaviors. Such attention
and repute enable them to be a trendsetter or a socially recognized maven. In this
paper, we present a complex adaptive mental network model of a narcissist to see
how popularity can adaptively influence his/her behavior. To analyze and to support
behavior showed by our model, we used some key performance indicators from the
literature to study the popularity and narcissism of 30 Instagram profiles. The results of
the—both computational and empirical—study indicate that our presented computa-
tional adaptive network model in general shows the behavior found from the empirical
data.
Keywords: Digital narcissism, Digital reputation, Popularity influence, Complex
network
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RESEARCH
Jabeenetal. Appl Netw Sci (2020) 5:84
https://doi.org/10.1007/s41109-020-00319-6
Applied Network Science
*Correspondence:
fakhraikram@yahoo.com
Social AI Group, Vrije
Universiteit Amsterdam,
Amsterdam, Netherlands
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Jabeenetal. Appl Netw Sci (2020) 5:84
channels for self-promotion of a narcissist. ey share proactive materials like self-
ies (Holtzman etal. 2010), or posts with their lifestyle information, which makes them
dominant (Alshawaf and Wen 2015). Previous studies explained that there is a relation-
ship between narcissism, excessive usage of social media (McCain and Campbell 2018;
Panek etal. 2013) and reward-seeking behavior (Bushman and Baumeister 1998). In a
preliminary version of our work, we presented a complex second-order adaptive net-
work model that explains the reactions of a narcissist in case of positive and negative
feedback (Jabeen etal. 2019). However, it is also interesting to see how popularity can
influence these reactions; this addition is contributed by the current paper, as is a much
more extensive empirical study involving 30 social media profiles.
More specifically, in this paper, in addition to network-oriented computational mod-
eling of narcissist behaviour, we address both empirically and computationally (a) how
a presumed narcissist earns popularity over time, and (b) how popularity can influence
his/her behavior. e paper is organized as follows. In "State of the art literature" sec-
tion, we discuss the state-of-the-art literature related to narcissistic behaviors, along
with popularity over social media. "Methods and methodologies and the obtained
adaptive network model" section presents the method and methodologies applied and
the obtained adaptive network model. In "Simulation experiments" section simulation
results are presented. "Analysis of simulation experiments with reference to real-world
data" section discusses how behaviors from real-world relates to the designed computa-
tional model, through 30 public Instagram profiles. "Limitations and future work" sec-
tion discusses the limitations and future work options of the study and "Conclusion"
concludes the paper.
State oftheart literature
is section presents the related work in two streams: i.e. firstly, it discusses the psycho-
logical and neurological aspects of a narcissistic person and his/her expected behaviors.
Secondly, it presents the influence of digital reputation over such behaviors. At the end
of the section, AI-based approaches are also discussed, which were used to predict a
narcissist.
Narcissism
Narcissism is characterized by the mythological figure Narcissus, who passionately fell
in love with his own reflection (Brummelman etal. 2015). is complex phenomenon of
acute concern of self-admiration can be described in terms of psychological, cognitive,
and social processes.
Psychologically, narcissists show a high tendency for self-admiration and self-presen-
tation (Wang 2017). A study indicated that there is a strong association between narcis-
sism and reward-seeking behavior (Bushman and Baumeister 1998). Social media like
Instagram is a well-known platform used for self-exhibition (Alshawaf and Wen 2015). A
narcissist may receive a compliment and react with kindness and joy (Moon etal. 2016)
as an outcome of reward-seeking behavior (Fan etal. 2011), or with a non-empathetic
response to a critic (Bushman and Baumeister 1998; Fan etal. 2011).
In cognitive neurological sciences, different brain parts interact with each other for
an interpretation and response to feedback. For example, the prefrontal cortex (PFC)
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along with the Anterior Insula and temporal lobe evaluates feedback as a compliment
(Olsson etal. 2014). As a result, activations in the anterior cingulate cortex (ACC) along
with the ventral striatum show the reward-seeking behavior. Different hormones and
neurotransmitters also take part when a person is admired. For example, dopamine is
released when a narcissist feels that his target of sharing content is achieved, as (s)he is
admired (Daniel and Pollmann 2014). Similarly, γ-aminobutyric acid (GABA) receptors
are activated, due to anxiety, which results from a negative evaluation of a critic (Sun
etal. 2016). is negative evaluation leads to a threat to his/her ego as (s)he feels socially
rejected (Bushman and Baumeister 1998). e hippocampus in the brain is affected by
psychological stress, which affects, in particular, the memory and the learning capabili-
ties by decreased synaptic plasticity (Schmidt etal. 2013; Sun etal. 2016). is reduction
in synaptic plasticity is due to changes in the brain structure caused by stress (Sun etal.
2016). Also, cortisol levels are elevated when a person feels stress (Jauk etal. 2017).
Popularity
Narcissists use social media excessively, to display their charismatic looks and, by their
social skills, they can become social mavens or influencers (Moon etal. 2016). Instagram
is an ideal platform for an individual to engage him/herself and to gain more visibility.
is process of self-promotion involves the visual appearance of a person with a high
number of followers who talk about his/her likability (Holtzman etal. 2010) and, digital
reputation is earned (Alshawaf and Wen 2015). ey proactively gear themselves and
their followers, to increase the follower likability and engagement (Bernarte etal. 2015).
An example of such behavior can be a selfie with lifestyle information (Alshawaf and
Wen 2015), captioned by using hashtags (Page 2012). Often, they follow limited people
and, thus, have a high follower to following ratio, indicating their high influence/popu-
larity (Farwaha and Obhi 2019; Garcia etal. 2017). A study also indicated that high num-
bers of likes can indicate how popular the posts of a person are (Chua and Chang 2016).
High popularity may leave a positive impact and give personal satisfaction, along with
the sense of achievement (Nesi and Prinstein 2015; Trent 1957).
Among AI-based approaches, a study related to machine learning tried to detect
narcissism from text, where text as a vector was compared with personality vectors or
dimensions resulting patterns of narcissism in psychological dimension (Neuman 2016).
Another textual analysis approach (LIWC) used first-person singular pronouns to detect
narcissism (Holtzman etal. 2019). In our previous work, we discussed the vulnerable
behavior of a narcissist through a complex network model (Jabeen etal. 2019). Here, we
extend our work by studying popularity and its influence on the responses/behavior of a
narcissist.
Methods andmethodologies andtheobtained adaptive network model
Causal network modeling is a well-known approach in the field of artificial intel-
ligence, which is helpful in making predictions about the behaviors of a person or
a real-world scenario. Variables in a causal model, act as basic building blocks to
represent the occurrence of an event (e.g. “he graduated”), which leads to behavio-
ral changes in a system or a person (e.g. “he got admission”) (Scheines etal. 1991).
Temporal-causal network modeling distinguishes itself from static causal network
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modeling, by adding a temporal perspective on causality. In addition, adaptive tem-
poral-causal network modeling also addresses that network connections and other
network characteristics can change over time. It is applicable to design and simulate
a variety of models related to many domains like neural, mental, biological, social
network, and many others. is section describes the adaptive temporal-causal net-
work modeling approach using a multilevel reified network architecture (Treur 2020),
which was used to design our model.
A reified network architecture is a multilevel network architecture, in which a
temporal causal network is presented at the base level and the adaptiveness of the
network is represented at (higher) reification levels. e base level contains a causal
network representation, specified by a directed graph having ‘states’ as vertices and,
‘connections’ as edges between them. To illustrate this, consider a connection: X → Y.
is indicates that state Y is influenced by state X. e activation level of Y is com-
puted through a combination function, which uses the aggregated causal impact by
all states including X, from which Y has incoming connections. e aggregated causal
impact depends on the connection weights and the activation levels of the incoming
states. erefore, for each state Y we have a:
• Connection weight ωX,Y : how strong state X can influence state Y. e magnitude
normally varies between 0 and 1, but suppression from a state is specified by a
negative connection weight.
• Speed factor ηY: how fast state Y is influenced by the impact of incoming states.
e range is normally between low: 0 and high: 1.
• Combination function cY(..): used to determine the aggregated impact of all states
with incoming connections to Y. Either an existing combination functions can be
used like: the identity function, the advanced logistic sum function, and so on, or a
custom function can be defined.
e above introduced ωX,Y , ηY and cY(..) are the network characteristics defining a
temporal-causal network model. An adaptive network model occurs when such charac-
teristics are dynamic and change over time. e adaptiveness of the base level network
considered here is represented by first-order adaptation principles (modeled at level II)
and second-order adaptation principles (modeled at level III). An nth-order adaptive net-
work model is specified by declarative specifications of an n + 1 leveled network design
and can be represented mathematically as shown in “Appendix A”. Here, it is shown how
a (three leveled) second-order reified adaptive network architecture was designed to
address the complex adaptive mental network model of a narcissist.
Level I: thebase network level
is section addresses the base network model (Level I) of a narcissist depicting his
mental organization by 39 states (Fig.1). A categorical explanation of each state is
presented in Table1. A state can have three types of incoming connections:
• Black arrows for a positive connection with weight values between (0, 1].
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• Purple arrows for a negative connection with weight values between [−1, 0].
• Green arrows show the adaptive connections which lead to an adaptive behavior and
will be explained further in "Level II and III: the adaptation levels" section.
e model has three inputs from surroundings: wspf, wsnf and wss. State wspf shows the
positive, while wsnf represents the negative feedback from another peer. State wss repre-
sents the stimulus, for example, the usage of social media. ree output states: eshappy,
esact, and essent represent the reaction of a narcissist. State eshappy is an outcome when the
person receives positive feedback (wspf = 1, wsnf = 0) and esact and essent are the outcomes
for a critic received (wspf = 0, wsnf = 1).
When a narcissist shares an attractive post (e.g. his/her selfie with an attractive cap-
tion) over social media, he often receives different types of feedback from others. A
result of feedback like ‘you are awesome’ makes him/her feel happy and loved. Based
upon the narcissus mythology, here his/her self-belief (bs+) evaluates such feedback as
positive (eval+). erefore, the mental states related to self-enhancement (PFC; Insula)
are activated, along with the reward-seeking states: striatum, feelings of self-love (fslove)
and reward (fsrew). e feelings of self-love increase the esteem/self-belief state (bs+)
over time, which escalates his or her reward-seeking behavior, making him/her a narcis-
sistic soul.
A narcissist person usually disagrees to a critic due to high ego/self-belief. So, his/her
negative feelings arise when wsnf = 1, which may result in a non-empathetic/negative
response. To explain it further, a remark like ‘you are ugly’, will be evaluated (eval−) as
negative, and can provoke a response like ‘go off you loser’. Here, ego/self-belief (bs+)
I
III
II
srs
pop
ws
pf
striatum
ss
pf
ws
s
ws
nf
srs
pf
ss
s
ssn
f
eval+
srs
pf
bs
+
fs
love
fs
reward
PFC
insula
cs
es
happy
eval-
os
ps
act
ws
sent
ss
sent
srs
sent
ps
sent
fs
sent
es
sent
val
es
act
ws
eff
ss
eff
srs
eff
fs
emp
ps
emp
ws
anx
ss
anx
srs
anx
ps
anx
fs
anx
_
_
_
ws
pop
ss
pop
W
sat,ins
W
eval,ps
a
W
psa,srs
eff
W
bs,fs
love
W
fs
love
,bs
W
fs
rew
,sat
W
fs
sent
,ps
a
H
M
Fig. 1 Reified second-order adaptive network architecture for a narcissist person, consisting three levels:
base level I, first-order adaptation level II and second-order adaptation level III
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initially tries to suppress this evaluation through control state (cs). However, evaluataion
(eval−) is too strong to be suppressed, resulting, (a) stimulation of negative sentiments
and (b) a non-empathic reaction to the peer.
Here, we address two categories of negative sentiments/feelings by the sentiment body
loop (wssent; sssent; srssent; fssent; pssent; essent): negative and extreme negative (Ntshangase
2018). e negative feelings are the low-intensity feelings like: fear, sadness or rejection.
While the extreme/very negative feelings, are the ones with a high intensity such as of
anger, humiliation, rage or frustration. Action (psact; esact), is an aggregate result of nega-
tive feelings (fssent), evaluation (eval−) and valuation (val) states. is may result in a
Table 1 Categorical explanation ofstates ofbase network (level I)
Categories References
Stimulus states “the representation of the world external to the
body can come into the brain only via the
body itself” (Damasio 2012)
wsiWorld state. i = stimulus s; posi-
tive/negative feedback (pf/nf)
ssiSensory state. i = stimulus; pf/nf
srsiRepresentation state j = pf/nf
Attribution/evaluation states “Narcissism involves states for self-enhance-
ment and mentalizing” (Olsson et al. 2014)
eval+ Positive evaluation of feedback
eval- Negative evaluation of feedback
Happiness related states “fMRI studies show activations at or near
dopaminergic midbrain nuclei and the VS
that correlate with both reward expectation
and reward prediction errors…”(Daniel and
Pollmann 2014)
bs+Self-belief state
striatum Ventral Striatum: brain part
PFC Prefrontal cortex: brain part
fsreward Feeling state of reward (Amygdala)
fslove Feeling state self-love (Amygdala)
eshappy Execution state of happiness
insula Anterior Insula: brain part
Sentiment related action states “mind is informed of the actions taken.. the
feeling associated with the information
signifies that the actions were engendered by
our self” (Damasio 2012)
os Ownership state
psact Preparation state of action
esact Execution state of action
Body loops: sentiment (sent) and anxiety (anx) “The as-if body loop hypothesis entails that
the brain structures in charge of triggering
a particular emotion be able to connect to
the structures in which the body state corre-
sponding to the emotion would be mapped.”
(Damasio 2012)
wsiWorld state for i = sent/anx
ssiSensor state i = sent/anx
psiPreparation state for i = sent/anx
fsiFeeling state for i = sent/anx
essent Execution state of sentiment
Predicted effect of action “They need to know that this person will listen
to their fears, take them seriously and do
something” (Elliott 2002)
wseff World state of effect
sseff Sensor state of effect
srseff Representation state of effect
Control states “the survival intention of the eukaryotic cell
and the survival intention implicit in human
consciousness are one and the same”.
(Damasio 2012)
cs Control state
val Valuation state
Popularity “popularity moderated … depressive symp-
toms.” (Nesi and Prinstein 2015)
wspop World state of effect
sspop Sensor state of effect
srspop Representation state of effect
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response like “back off” or deleting and block that peer. It is to be noted, that the valua-
tion state (val) in principle doesn’t get activated if the person has empathy (fsemp; psemp),
which is not the case here (as he/she is narcissist (Fan etal. 2019). After activation of
psact, the thought process related to ownership state (os) and predicted effect (wseff; sseff;
srseff) is also activated, which induces anxiety (wsanx; ssanx; srsanx; fsanx and psanx). e
body loop of anxiety differs from the body loop of sentiments (Raghunathan and Pham
1999; Weger and Sandi 2018), as it can elevate such reactions (esact) along with experi-
ence/learning from the actions (psact).
Popularity (wspop; sspop; srspop) serves as a moderator to these negative feelings. us,
popularity lowers the negative evaluation (eval−), negative sentiments and feelings of
anxiety(Nesi and Prinstein 2015), so the negative outcomes appear less than before (dis-
cussed in "Level II and III: the adaptation levels" section).
Level II andIII: theadaptation levels
e reified network architecture used for our network model has two adaptation lev-
els represented by first- (Level II) and second-order (Level III) adaptation (see Fig.1).
e first-order adaptation level (Level II) relates to the ability to learn/adapt certain
behavior(s) by experience over time (for example: with age/popularity) known as neu-
roplasticity or hebbian plasticity/hebbian learning. In this case, connections in the base
network appear not to be fixed in terms of their weights and may change over time
(shown by green arrows at Level I). In our model, this change due to hebbian learning
principle is modeled by seven reification states: ‘W-states’ at Level II (also see Table2).
e second-order adaptation level (Level III) addresses the adaptation of W-states,
which represents plasticity of neuroplasticity or metaplasticity (Robinson et al. 2016;
Schmidt etal. 2013). It is modeled by adaptive persistence factor μ and adaptive learning
rate η by reification states M and H respectively at Level III. is shows how synaptic
transmission can be influenced and controlled by other factors, for example, through
hormones or neurotransmitters (Robinson etal. 2016; Treur 2020, Ch. 4).
In Fig. 1, the inter-level interactions are represented by two types of arrows: red
(downward) and blue (upward). e red arrows show the specific causal impact from
reification states to a certain state, while the blue arrows are used to create and represent
Table 2 Explanation ofstates inlevel II andIII
States perlevel References
Level II (plasticity/omega states) 1–4: Potentiation in the striatum
depends not only on strong pre- and
postsynaptic activation … reward
prediction … modify behavior (Dan-
iel and Pollmann 2014)
5–7: Presynaptic somatodendritic
5-HT1… people with a high level of
aggression, there is a greater density
… with impulse control (de Almeida
et al. 2015)
1.
Wfslove,bs
For fslove → bs
2.
Wbs,fslove
For bs → fslove
3. Wsat,ins For striatum → insula
4.
Wfsrew,striatum
For fsrewa rd → striatum
5.
W
eval−, ps
a
For eval- → psact
6.
Wpsact, srseff
For psact → srseff
7.
W
fs
sent
, ps
a
For fssent → psact
Level III (meta-plasticity) Damage to neurons in hippocampal
CA3 area and microstructure of syn-
apse indicates that anger… harms
plasticity …. (Sun et al. 2016)
HSpeed factor for Wfsang,psa
MPersistance factor for Wfsang,psa
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the dynamics of the reification states on the higher levels. For illustration, consider when
a person receives negative feedback, (s)he reacts (psact; esact) after having a negative sen-
timent about the feedback (connection: eval− → psact). e way of reacting after such a
feeling is learnt from personal experience. is can be modeled by hebbian learning at
Level II. To model Hebbian learning, reification state
W
eval−,ps
act
receives an impact from
the pre-synaptic and post-synaptic states, i.e. eval− (relating to stress-related cortisol
levels) and psact; this
Weval
−
,psact
in turn affects the post-synaptic state psact, making it a
form of circular causation. Similarly, when a positive feedback is evaluated (fsreward relat-
ing to dopamine release), this affects
Wfsrew,striatum
, with respective pre-synaptic (fsreward)
and post-synaptic (striatum) states. A similar pattern of interlevel connections can be
observed for Level III. Here, metaplasticity states H and M receive the respective input
from the pre-synaptic (srssent; srsanx) and post-synaptic (psact) states, represented in Fig.1
by blue upward arrows. ese states are related to meta-adaptation, which controls (red
arrows from M and H to
Wfssent,psa
) the learning and the speed of the state
Wfssent,psac
at
Level II (Schmidt etal. 2013; Sun etal. 2016).
A network model can be simulated using the reified network engine designed in MAT-
LAB, by providing a declarative specification in the form of role matrices. A role matrix
is a compact specification by the concept of the role played by a state (Treur 2020, Ch.
9). For example, base network matrix (mb) enlists all the states with incoming connec-
tions to any state. Similarly, connection weight matrix (mcw) and speed matrix (ms)
provide the connection weights and speed factor for each state. e combination func-
tion weight (mcfw) and combination function parameter matrix (mcfp) specify combi-
nation functions with their weights, and parameters respectively. Role matrices provide
a declarative specification of the adaptive network model. e full specification of the
adaptive network model in terms of role matrices can be found online (Jabeen 2020).
Simulation experiments
By simulation experiments the dynamics of the designed adaptive network model can be
explored through simulating real-world scenarios. In this section, we present different
simulations. First, we will see the two reactions of a narcissist i.e. a happy reaction or a
reaction expressing annoyance. Second, we will see how a person gains popularity over
social media and how it will influence both of his/her reactions. ird, we will see how
a person reacts, when (s)he loses popularity. erefore, this section is divided into two
subsections (a) reactions to a feedback and (b) influence of popularity on the reactions.
Reactions tofeedback
Here, we present our two scenarios; i.e. with: (a) a positive reaction or, (b) a negative
reaction, along with few example tweets of Donald Trump, who is studied as a ‘narcis-
sistic’, and to have a ‘messiah complex’ (Nai 2019).
Reacting apositive feedback
Social media like Facebook, Twitter, or Instagram is a platform, where self-confidence of
a narcissist speaks by itself (Moon etal. 2016; Wang 2017). For example, the following
tweet of Trump:
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…my two greatest assets … mental stability and being, like, really smart … I went
from VERY successful businessman, to top TV Star…. (Tweeted: 1:27 PM – Jan 6,
2018).
Figure2 shows the simulation results; here the horizontal axis shows the time scale
and, the vertical axis shows the dynamic state values ([0,1]) over time. As positive feed-
back is received (wspf = 1), the state eval+ (purple) is activated, which in turn activates
the state PFC (golden) around time point t ≈ 5–10. ese two activations along with
bs+ ( brown), activate the self-rewarding behavior through the striatum state (green-
dotted). is activates insula (orange) at t ≈ 12, indicating a self-thinking process. e
self-thinking process, boosts the feelings of self-love fslove (dark-brown) and self-reward
fsreward (pink), at time point t ≈ 10. As a result, (s)he expresses gratitude, with such an
expression.
Reaction anegative feedback
While observing a negative feedback of another person, a narcissist can react negative or
extreme negative. Negative reactions may include an expression of sadness, fear, disgust,
etc. While extreme negative reactions express negative feelings with a stronger intensity
and can be expressed through anger, hostility, etc. (Ntshangase 2018). For example, let’s
consider another tweet of Trump, where he doesn’t seem to feel pleasure from another
peer, i.e.:
what kind of lawyer would tape a client? So sad! is this a first, never heard of it
before? Why was the tape so abruptly (cut)….too bad (Tweeted: 2:34 PM – July 25,
2018).
Or, let’s take an example like,
… world class loser, Tim O`Brien, who I haven’t seen or spoken … knows NOTHING
about me … wrote a failed hit piece book… (Tweeted: 6:20 AM – Aug 8, 2019) (Fol-
ley 2019).
Figures3 and 4, shows the simulation results. Certain behavior (e.g. videotaping and
cutting in between without any notification) is evaluated as negative, thus eval− (
Fig. 2 Simulation of the model when wspf = 1 and wsnf = 0: reaction is cheerful/happy
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) gets activated at time point t ≈ 10–15. is stimulates the negative sentiments
(fssent , pssent ), along with the re-action states (bright green : psact; esact)
at t ≈ 20–25. Also, the body loop of sentiments is activated (wssent; sssent; srssent; pssent;
fssent and essent: clustered by ) around time point t ≈ 20. is action provokes self-
conscious behavior (os) on the basis of some past memories ( : wseff; sseff and; srseff)
resulting in anxiety (wsanx; ssanx; srsanx; fsanx; and psanx: clustered by ). As the per-
son doesn’t have empathy ( : psemp), also anxiety intensifies the action (esact) state.
Here, it can be observed, that although self-rewarding states are low (values = 0.03 at
time t = 0–10), the feeling of self-love fslove ( ) continues to grow after t = 100,
intensifying the self-belief/ego (black dotted), indicating his love for himself only grows
with the period of time. Figure4 shows a similar behavior, with higher intensity shown
by a body loop of sentiments in red. Here, it is to be noted that the reward related states
like striatum ( ) drops immediately at start t = 5–10.
Inuence ofpopularity onreactions duringfeedback
In this section, we address two behaviors of a narcissist: i.e. a) how (s)he reacts when (s)
he is not popular and b) how does the popularity influence his/her behavior.
Fig. 3 Simulation of the model when wspf = 0 and wsnf = 1: reaction is negative
Fig. 4 Simulation of the model when wspf = 0 and wsnf = 1: reaction is extreme negative
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When theperson isnotpopular
"Reactions to feedback" section explains the reactions of a narcissist upon a posi-
tive or a negative feedback (Figs.3, 4). Here, we combine them (Fig.5), to address (a)
behavior without popularity and (b) hebbian learning (described further in "Exhibi-
tion of learning experience in the model" section). Here, wspop = 0, and the episodes
with white background are the episodes whenever a positive feedback is observed, for
example, the first episode has duration of time points t = 0–100. In contrast, the epi-
sodes with colored background show the episodes with negative feedback, for exam-
ple, during time points t = 100–200. e length of duration and order of occurrences
can be interchanged or overlapped, but for the purpose of simplicity, we kept them
non-overlapping and with equal intervals. Interestingly, learning from different levels
of intensities can be observed through two similar episodes. For example, negative
response/action ( : psact; esact) in the earlier episodes is lower (t = 100–200) than
the later episode (t = 300–400). Similarly, anxiety ( : wsanx; ssanx; srsanx; fsanx; psanx)
also increases with each episode.
When theperson gains popularity
Popularity is not earned overnight, but narcissists who aim to become social maven
or influencers often choose tactics related to self-grandiosity and socialization. For
example, they use social media to share their selfies and have a high number of lik-
ability and followers (Chua and Chang 2016; Folley 2019; Page 2012). Popularity influ-
ences the behaviors, and the symptoms related to depression (Nesi and Prinstein
2015), and anxiety are reduced (Trent 1957).
is ongoing process is shown in Fig.6. For simplicity, only the important curves
are presented in the figure. A person starts to earn popularity ( ) by sharing posts,
at time point t = 450. is popularity gain lowers the intensity of the negative feelings
(fssent: , essent:, anxiety: ), which were high before t < 450, with no popular-
ity. Here it is to be noted that the popularity of a person is 0 for the minimum and 1
for the maximum.
Fig. 5 Simulation of the model with alternative episodes of wsnf = 1 or wspf = 1: no popularity
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When theperson loses popularity
Popularity is not static always, and it is natural that a person can gain/lose popularity
over time. e reason can be variation of looks, trends, and so on (Polhemus 2011). As a
result, narcissists’ vulnerability may lead to negative reactions.
Figure7 shows, when a person loses/tends to lose popularity, how different feedbacks
can influence him/her. First, it can be observed in the duration of t = 1800–1900, when
a positive feedback is received (wspf = 1), the person feels rewarded and loved (fslove and
fsreward: ), so he is happy (eshappy: ). However, in this scenario, his esteem (bs:
) and fslove are already high, so there is no further learning in the self-rewarding
behavior. e reason is that (s)he is aware of his/her self-worth. Second, when a disliking
behavior or a critic is observed, (s)he flares up, which activates the negative sentiments
(sentiment = essent: ; action = esact: ) and anxiety ( ) for t > 2100. Here, it
is to be noted that predicted effect shows the same behavior due to hebbian learning of
(srseff → psact).
Exhibition oflearning experience inthemodel
In this section, we discuss the influence of hebbian learning on the Levels II and
III. Previously, we saw the complex learning behavior over time (in episodes). For
Fig. 6 Simulation of alternative episodes of wsnf = 1 or wspf = 1: with popularity gain
Fig. 7 Simulation of alternative episodes of wsnf = 1 or wspf = 1. With popularity loss
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example, in the second episode of positive feedback (t = 200–300), the reward-
related states (striatum, fsreward, fslove, insula) are elevated more than the first epi-
sode (t = 0–100) in Fig.5. Similar behavior is observed when negative feedback is
received. Here, we can observe the underlying behavior of hebbian learning (Fig.8)
at other levels: Level II for plasticity (W-states) and Level III for metaplasticity (M
and H). For example, consider Weval−,psa (blue), the initial value of the state is 0.2.
During each negative episode the value is increased, so during t = 300 to 400 the
value is increased almost from 0.5 to 0.76. Similarly, Wpsa,srseff is raised compared
to the previous episode showing the learning behavior (Sun etal. 2016). However,
it can also be observed that due to metaplasticity, the state Wfssent,psa (colored back-
ground) was not much raised between two episodes due to M and H states (dotted)
(Sun etal. 2016).
Figure9 reflects how popularity influence states at Level II and Level III. Here, we
can see that the learning in W-states related to negative evaluation, action, and sen-
timents start to reduce after t > 450. This is an effect of popularity gain, also we see
same behavior for the metaplasticity-related states M and H. This behavior would be
vice versa when a person loses popularity.
Fig. 8 Effects of plasticity (W states) and metaplasticity for
W
fs
sent
,ps
a
(M and H)
Fig. 9 Effects of plasticity (W states) and metaplasticity (M and H) under influence of popularity
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Analysis ofsimulation experiments withreference toreal‑world data
In this section, we analyze the behavior of our adaptive network model in relation to
gathered empirical/real-world data. To accomplish this, we analysed thirty random pub-
lic Instagram profiles, with presumably some extent of narcissistic traits, in line with
literature such as (Chua and Chang 2016; Folley 2019; Page 2012). We compared the
behaviors found there to our simulation experiments discussed in the previous section.
Materials andmethods
Social media like Twitter or Instagram offer an environment where people tend to share
their information, emotions and opinions to get feedback from others. We chose Insta-
gram because: (1) its users have more tendency towards narcissism (Moon etal. 2016),
and (2) different types of reactions can be observed in the form of conversations. ese
profiles were selected using the following criteria:
(1) the participants had at least shared 60 posts and
(2) they tend to share their selfies.
To examine the behavior of the model in correlation with the Instagram data, we used
the following hypotheses through few key performance indicators (KPIs) were obtained
(see Table3):
(a) Narcissism/grandiose exhibition
1 Narcissistic people tend to share their selfies more frequently.
2 On appreciation, they feel happy and proud but react negatively otherwise.
Table 3 KPIs tomeasures forpopularity andnarcissism alongwiththeir relevant literature
KPI Explanation Reference
Grandiose exhibition
selfiepm/otherpicspm How many selfie/other pictures shared
per month “Categories emerged … on Instagram.
Personal promotion, brand promotion,
and sponsored promotion … increase their
popularity… digital reputations” (Alshawaf
and Wen 2015)
postfreqpm Frequency of sharing posts per month “narcissists have more Facebook friends and
tend to post more provocative material”
(Bernarte et al. 2015)
pconvsspm; nconvspm How many positive and negative conver-
sations per month “The relation between narcissism and disa-
greeableness increases when self-esteem is
taken into account” (Holtzman et al. 2010)
Popularity
followerspm How many followers per month “Instagram Leaders … have more followers
than they are following” (Farwaha and
Obhi 2019; Utz et al. 2012)
likespm How many likes per month We chose the number of “likes” as the index of
popularity of a post (Zhang et al. 2018)
htagspm Count the number of posts which had
one or more hashtags (boolean) “… use hashtags to make their professional
identity searchable … promote their iden-
tity as affiliated.. wider professional field”
(Farwaha and Obhi 2019, p. 2012)
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(b) Popularity
1 ey gain popularity through particular behaviors, for example, self-presenta-
tion, or by using hashtags (Utz etal. 2012).
2 ey have a high number of followers or friends (Utz etal. 2012)
3 More popularity can influence their behaviors:
(a) ey engage more to seek admiration. (Paramboukis etal. 2016)
(b) eir depression/anxiety is reduced (Nesi and Prinstein 2015; Trent 1957).
Figure 10 briefly describes the algorithm used to formulate the results for the
addressed KPIs. First, we extracted basic data of a profile from Instagram (steps 1–4).
Second, we extracted data for each post in relation to its duration (5–7). Later, for every
month, we extracted the posting frequency, the average number of likes, the selfie count,
the number of posts which used hash tags, and the positive and negative conversations
(8–13).
For selfie recognition, we used the KNN classifier with face encodings (Adam 2016)
with the minimum threshold of 0.4. Moreover, for sentiment analysis, we used the com-
bination of two classifiers: the IBM Watson tone analyzer and the Vader Sentiment
Analyzer. e Watson tone analyzer was able to identify three types of sentiments:
Cheerful, Negative, and Strong Negative. Cheerful emotions were related to happy/neu-
tral reactions: joy, positive analytical. By positive analytical, we mean a neutral/positive
discussion with an audience (maybe by telling a product name). is was computed by
looking into the sentiment of the previous comment, and based upon its score, it was
considered as a non-negative reply (as telling about herself and her products will make
her feel happy about herself). e negative emotions were related to sadness or fear,
while extreme negative meant anger, which is a negative feeling with stronger intensity
(Ntshangase 2018). It can be an outcome of humiliation, annoyance or hostility. If the
IBM Tone Analyzer does not detect any tone (for example, “Nice” without “.”), theVader
Fig. 10 Algorithm showing steps to extract data for KPIs
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sentiment analyzer was used. It can detect three type of sentiments: positive, negative,
and neutral (Hutto and Gilbert 2014), which were also used in our prior work (Jabeen
etal. 2019). Table4 shows some example conversations in terms of feedback ‘F’ and reply
‘R’, as analyzed by the Watson tone analyzer and the Vader sentiment analyzer.
Results anddiscussion
In this section, we will discuss our results from relevant to deviant cases in relation to
the simulation experiments presented in "Simulation experiments" section. Each sec-
tion will discuss the KPIs of popularity with reference to narcissism (Table3), i.e.: (a)
number of followers per month, (b) the average number of likes obtained per month,
and (c) hashtag usage. e obtained results for all 30 considered profiles can be found in
“Appendix B”.
Followers
Different studies indicate ‘followers to following ratio’ (ff) and the number of followers
(f) as a measure of popularity of a profile (Farwaha and Obhi 2019; Garcia etal. 2017).
In our analysis, we used the current number of followers/and related trends to study
behaviors in relation with popularity and narcissism. erefore, we distributed the 30
extracted profiles in three groups with respect to the number of followers (Fig.11). e
first group consists of 5 of the 30 profiles (more than 50K), the second group had 9 pro-
files (between 10 and 50K), and our third group has 16 profiles (less than 10K).
Table 4 Three conversation examples withtheir sentiments
Type Feedback/reply Sentiment
F1 It looks hella face tuned Neutral
R1 you look hella negative Negative
F2 Well I think you look gorge! So happy for your family during this time Joy
R2 thank you! Joy
F3 You need to blend you highlight a bit more Neutral
R3 No I want to blind you so you piss off my page Anger
1K - 10K 10K - 50 K
> 50 K
Fig. 11 Distribution of the participants of our study with respect to the number of followers
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e collected data was analyzed using a measurement of time in months. It was
observed that all users tend to post on a regular basis. As every profile tends to share
different numbers of posts per month, so we took the average of posts per month, like
posts/selfies per month by a user. It was observed that most participants tend to share
more posts with selfies each month over a period of time (See “Appendix B” for the self-
ies ratio of each user). is can be an indication of self-love. For example: in Fig.12,
P3:CB has a high ratio of followers to following (followers: 262,000, following: 609), indi-
cating this person is popular. Figure12a shows a normalized distribution of the number
of posts, average likes, hashtags, and followers per month. We can see an increase in
posting frequency along with the average number of likes and number of followers. We
can also see the trendlines indicating a linear increase in the average numbers of likes
and the number of followers. is is also addressed by a user like:
I don’t think that looks nice but the media say it was pretty, so people started follow-
ing that and they got a lot of likes for it… (Chua and Chang 2016).
In Fig.12b, we can see some correlation between sharing selfies and average likes and
thus the number of followers in a month. High variations were also observed between
the average number of selfies and the number of followers (see “Appendix B”). erefore
in "e average number of likes" section we will discuss our analysis with respect to the
average likes as well.
During the conversation analysis, it was observed that 11 out of the 30 profiles actively
responded to their followers. Figure 13 shows the distribution of participants with
respect to their total response rate (=
conv
totalposts
), with values like:
On the one hand, it was observed that 5:14 users in the category of < 10K followers, and
3:9 users in 10–50K actively responded to their followers. While on the other hand in the
Responserate
(p)=



high;value ≥0.75,
medium;value >0.5 and <
0.75
low
0%
20%
40%
60%
80%
100%
ab
Apr-18 Jul-18 Oct-18 Feb-19May-19Aug-19Dec-19Mar-20
postcountavglikes
hashtags followers
Linear (postcount) Linear (avglikes)
Linear (followers)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Selfie avglikes
Fig. 12 a Posting frequency in relation with the popularity related KPIs. b Selfie sharing with average
number of likes over time
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more than 50K category, all users (5:5) actively participated in conversations. In other
words, 13 participants participated into the conversations more proactively (Bernarte etal.
2015).
An overall observation of conversations and sentiment analysis, people tend to respond
more in a positive or neutral manner (Joy, positive analytical and Positive) than a nega-
tive manner (Anger, Fear, Sadness, Negative). Another interesting pattern was that most
users with a low number of followers had more cheerful comments than negative ones. is
truly doesn’t relate to our simulations (i.e., negative behaviors have no/higher intensity with
low/less popularity). However, we can assume that they didn’t get critics most of the time,
another possible reason can be to attract more followers or friends, or they were naïve on
Instagram. With reference of the number of followers, there was no significant variation
observed for negative or positive conversations (See “Appendix B”).
The average number oflikes
In this section, we analyze the behavior of Instagram users with respect to an increase/
decrease in the average number of likes. As per hypothesis, a user seeks the opportunity of
self-promotion to get compliments or likes (Holtzman etal. 2010; Paramboukis etal. 2016;
Zhang etal. 2018). As addressed by an Instagram user:
It makes me happy, … I think, to me is you are cool, you’re pretty, so you get a lot of
likes. (Chua and Chang 2016).
In relation to grandiose self-exhibition, we looked into the selfie ratio, mostly it was
observed, that participants have a higher tendency of getting likes if they share selfies
(Fig.12b; “Appendix B”). To investigate it further, we took each profile and computed the
pearson correlation coefficient between the number of selfies and the average number of
likes shared per month by:
Fig. 13 Average responses per post with respect to the followers’ distribution
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where corrp = correlation value of a profile,
selfie
and
likes
are the sample means of selfies
and average number of likes in the duration of data collected.
It was observed that most of the profiles had a positive correlation between the two
variables, however there were 6 out of 30 profiles, for which this correlation was low
(> − 0.1). Figure14 shows the distribution of users with respect to their relation/correla-
tion values where:
Here, 12 users (40%) showed a weak linear relationship, while 18 people showed mod-
erate to strong positive relationships (moderate: 7; high: 11). is explains the behavior
that people tend to share their selfies more often, as they may find this as an opportunity
for approval and likability from their followers (Chua and Chang 2016).
While looking into the reactions of the users, we studied the extracted sentiments in
the context of the average number of likes. Mostly, it was observed that in all profiles
the users were mostly happy when they received more likes than otherwise. To make an
explicit conclusion, we normalized each sentiment also in conversations. erefore, a
sentiment score per month was assigned through:
where sent_score(t) = the individual score of a sentiment in a month t and,
sentiments
(
t)
= total sentiments found within a month t. sent(t) = a value of a senti-
ment in range of [0,1].
Here, it is to be noted that possible sentiments are the cheerful (Joy/Positive, Positive
Analytical, Neutral), the negative (Fear, Sadness, Negative) and the extreme negative
(Anger) sentiments. For example, if in a month t, the sentiments of a user are: Joy = 2,
Sadness = 1, and Negative = 1, then sent_score for each in the month t are: Joy = 0.5,
corr
p=

selfie −selfie

likes −likes


selfie −selfie

2

likes −likes

2
relation
(selfie,likes)=



high;corrp>0.5,
medium;corrp>0.3 and <
0.5
low;corrp<0.29
sent
(t)=
sent_score(t)
sentiments(t)
High
37%
Moderate
23%
Low
40%
Fig. 14 Distribution of participants with respect to correlation values between selfies and average likes
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Sadness = 0.25 and Negative = 0.25. is implies that during conversations in month
t, the user was 50% filled with ‘Joy’ and 25% for the rest of two. Similarly, we normal-
ized the average number of likes for each month by dividing average likes obtained in a
month by maximum likes received by a user in the duration of extracted data, resulting
in a value between [0,1].
We manually analyzed all profiles for the similarities and the differences, mostly posi-
tive conversations were observed showing personal satisfaction (Nesi and Prinstein
2015). However, in negative responses/reactions few interesting patterns were observed.
For example in Fig.15 when average number of likes of P2:LV are decreased (June 18,
December 18, February 19 and so on) we can observe negative conversations (sadness:
green, negative = maroon or anger: silver). Also, positive conversations can be seen
when (s)he gets more likes. A similar pattern can be observed for P24:LJ, P30: AB and so
on (“Appendix B”).
is can be considered as the behavior of a person being similar to the behavior we
modeled in "Methods and methodologies and the obtained adaptive network model"
section, shown in Fig. 1, (which models the reactions over a feedback as a cheerful
response or a negative reply). Also, when a person gets popular (more average likes),
then negative expressions are reduced. Here, it is to be noted that in February 18, there
are few sudden drops in the average number of likes and conversations. is is possible,
because this user did not share any post in this duration (Fig.16).
For all profiles, we observed few variations in the behaviors in comparison to the
designed model. However, here we use notion of ‘most of the times’ to generalize their
behaviors. What we mean to say here is that although in August 18 P2:LV received more
likes, we can still see some negative sentiments, but most of the time the person showed
behavior similar to our model.
Table5 enlists the profiles which reflected the indicated behavior most of the time, as
well as the profiles which responded positively, and the rest which act more like outliers
and show more variations from our simulation experiments. ese fluctuating behaviors
can be due to multiple reasons like: difference in personalities, their current popularity
and time. For example, P10 or P20 seems to be less popular (less number of likes), during
the whole time for which data was collected, resulting in fluctuating behavior.
We also tried to look through the patterns of hashtags, however, we were unable to
see any patterns in relation to the behaviors, except most of the profiles used hashtags to
0%
20%
40%
60%
80%
100%
AnalycalAngerFear JoySadness
Posive NegaveNeutralavglikes
Fig. 15 Relation between the sentiments and the average number of likes (normalized) over time
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gain visibility. In conclusion from Table5, we saw that almost 60% of the profiles showed
behaviors similar to our model, i.e. a narcissist is overwhelmed with joy when they get
positive feedback and otherwise. Also, increase in popularity lead to happy reactions
with a decrease in negative conversations. In "Limitations and future work" section, limi-
tations and future work of the study are discussed.
Limitations andfuture work
e Watson analyzer is pretty accurate, also the Vader sentiment analysis gives a high
accuracy in sentiment detection and classification (Hutto and Gilbert 2014). How-
ever, during the conduction of the study, it was observed that classifiers identified
a few responses as negative, although they were positive (‘fierce as fuck ’) or (‘fuck!!
love you’). Although we adapted sentiment analysis as per needs of Instagram contents,
though, it still can be validated further. Moreover, during selfie detection and analysis,
many pictures that were taken from the back or were incomplete (without face), were
categorized as others. Improvements in the two can help to improve the results and
0%
20%
40%
60%
80%
100%
Mar-17 Sep-17 Apr-18 Oct-18 May-19 Dec-19 Jun-20 Jan-21
postcountavglikes hashtags followers
0%
20%
40%
60%
80%
100%
Selfiesavglikes
Fig. 16 Sharing behaviors of P2 with average number of likes over time
Table 5 Results showing which proles are mostly aligned withthesimulation results
Aligned proles Only positive proles Non-aligned proles Total
P2 P3 P4 P8 P17 P18 P1 P6 P7
P5 P9 P12 P25 P28 P10 P11 P13
P15 P16 P24 P14 P19 P20
P27 P29 P30 P21 P22 P23
P26
12 = 40% 5 = 16.66% 13 = 43.33% 30 = 100%
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study further. We haven’t used textual analysis approaches to study narcissism in the
text, as they require natural language processing with longer texts, whereas in Instagram
bibliography is known as the most long text, but it is not intended for this type of analy-
sis. Also, we encountered messages which didn’t have any text but just emojis like ‘♥♥’
or ‘ ’.
Furthermore, in this study, almost all of the profiles in the dataset were presumed as
narcissists. However, the authors didn’t have their NPI scores or knew them personally.
To make our work more concrete, it would be nice to investigate it more, for example,
why do they have fluctuating behaviors and their relationship to the personality traits of
a narcissist. So, as future work, we aim to set an experiment, which involves studying a
person in relevance to his/her NPI score, sensitivity, and overall mood of a person to see
this in relation to narcissism. is will help us to study behaviors with the understanding
of narcissism in relation to personality traits in more detail. We also aim to study sur-
rounding people like friends and family, who interact to a person with such behaviors.
Conclusion
In this paper, we presented a complex adaptive mental network model, which addresses
the adaptive cognitive processes of a narcissist. Moreover, it explains his or her behavior
and reactions, when (s)he receives positive or negative feedback. As his/her personal-
ity is vulnerable, an ego-threatening message is responded in a negative way, especially
when popularity is low. In addition to our prior work, we saw how popularity can influ-
ence such a person’s behavior. It was studied in how reward-seeking behavior blends
with an increase in popularity, and the negative reactions are reduced. In order to com-
pare our adaptive network model with empirical data, we extracted and analyzed data
from 30 public profiles. Both from our simulation experiments and from the empirical
analysis we observed that popularity acts as a moderator for a person with narcissistic
traits. us our model indeed displays the real-world behavior of a narcissist, concern-
ing the expression of emotion under the influence of increase/decrease in popularity.
In future work, we aim to incorporate different psychological measures like NPI score,
sensitivity, or mood, to monitor narcissists. Moreover, we aim to design an automated
system that can support a narcissist by counseling if he is highly vulnerable.
List of symbols and Abbreviations
AI: Artificial intelligence; GABA: γ-Aminobutyric acid; ACC : Anterior cingulate cortex; μ; M: Persistence; Persistence Reifica-
tion; η; H: Learning rate; Learning Rate reification; ω; W: Connection weight; Connection weight Reification; cY: Combina-
tion function for a state Y; Pi,j: Combination function parameter reification; ws: World state; ss: Sensory state; srs: Sensory
representation state; fs: Feeling state; eval+: Positive evaluation; eval−: Negative evaluation; bs+: Belief state; striatum:
Ventral striatum; PFC: Prefrontal cortex; eshappy: Execution state of happiness; insula: Anterior insula; os: Ownership state;
ps: Preparation state; es: Execution state; act: Action; pf: Positive feedback; nf: Negative feedback; anx: Anxiety; sent: Sen-
timent; eff: Effect/predicted effect; pop: Popularity; cs: Control state; val: Valuation state;
Wfslove,bs
: Omega representa-
tion state for connection fslove → bs+;
Wbs,fslove
: Omega representation state for connection bs → fslove; Wsat,ins: Omega
representation state for connection striatum → insula;
Wfsrew,striatum
: Omega representation state for connection
fsreward → striatum;
Weval
−
,psa
: Omega representation state for connection eval− → psact;
Wpsact,srseff
: Omega rep-
resentation state for connection psact → srseff;
Wfssent,psact
: Omega representation state for connection fssent → psact;
postfreqpm: Posting frequency behavior per month; likespm: Average number of likes per month; selfiepm: Number of
selfies shared per month; otherpicspm: Number of other pictures per month; hashtagspm: Number of hashtags used per
month; pconvspm: Positive conversations per month; nconvspm: Negative conversations per month; USER: User name
to login; PASSWORD: User’s password to login; KPI: Key Performance Indicators; w.r.t: With respect to.
Acknowledgements
The first author is grateful to Vrije Universiteit Amsterdam and University of the Punjab for providing the opportunities
for research and successful completion of the study.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 23 of 31
Jabeenetal. Appl Netw Sci (2020) 5:84
This is an extended version of a paper (Jabeen et al. 2019) that appeared in Complex Networks’19. The new content
of this article is a much larger empirical study and an additional focus on the influence of popularity on narcissism,
presented along with the analysis of simulation experiments.
Authors’ contributions
Fakhra Jabeen being a Ph.D. student presented the idea and completed the experiments and the project, while Dr.
Charlotte Gerritsen and Prof. Dr. Jan Treur being supervisors, designed the study and helped towards completion of the
project and producing the final manuscript of the paper. All authors read and approved the final manuscript.
Funding
Overseas scholarship from University of the Punjab, Lahore Pakistan.
Availability of data and materials
The data and materials used for analysis and development of results is available in https ://githu b.com/MsFak hra/Maven s.
Competing interests
The authors declare that they have no competing interests.
Appendix
A. Numerical relevance ofthemodel
e mathematical representation of a reified network architecture in terms of its net-
work characteristics can be explained as follows (Treur 2020):
1 At every time point t, the activation level of state Y at time t is represented by Y(t),
with the values between [0,1].
2 e single impact of state X on state Y at time t is represented by impactX,Y (t) = ωX,Y
X(t); where ωX,Y is the weight of connection X → Y. All single impacts for a given
state Y are aggregated by a combination function cY(..); see below.
3 Specific states are used to model specific types of network adaptation, where network
characteristics such as connection weights and combination functions are dynamic.
For example, WX,Y represents an adaptive connection weight ωX,Y(t) for the connec-
tion X → Y, while HY represents an adaptive speed factor ηY(t) of state Y. Similarly,
Ci,Y and Pi ,j,Y represent adaptive combination functions cY(.., t) over time and their
parameters, respectively. Combination functions are built as a weighted average from
a number of basic combination functions bcfi(..) from a library, which take parame-
ters Pi,j ,Y and values Vi as arguments. For adaptive network models in which network
characteristics are dynamic as well, the universal combination function c*Y(..) used
for any state Y is defined as:
where at time t:
• variable S is used for the speed factor reification HY(t)
• variable Ci for the combination function weight reification Ci,Y(t)
• variable Pi,j for the combination function parameter reification Pi,j,Y(t)
• variable Vi for the state value Xi(t) of base state Xi
• variable Wi for the connection weight reification WXi,Y(t)
• variable W for the state value Y(t) of base state Y.
c∗
Y(
S,C
1
,
...
,C
m
,P
1,1
,P
2,1
,
...
,P
1,m
,P
2,m
,V
1
,
...
,V
k
,W
1
,
...
,W
k
,W
)
=W+S[C1bcf1(P1,1,P2,1 ,W1V1,...,WkVk)
+··· +
Cm
bcf
m
(
P1,m
,
P2,m
,
W1V1
,...,
WkVk
)]/(
C1
+··· +
Cm
)−
W]
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 24 of 31
Jabeenetal. Appl Netw Sci (2020) 5:84
4 Based on the above universal combination function, the effect on any state Y after
time Δt is computed by the following universal difference equation as:
which also can be written as a universal differential equation:
B. Dataset
e large table below enlists the data collected from the 30 Instagram profiles. e first
and the third column have the information like the profile ID, their name initials, their
number of followers (f) and current followers to following ratio (f/f). Here it is to be
noted that to keep the anonymity of results, each profile is assigned ID in a pattern like
PXX. e second and fourth column consist of the increase/decrease in frequency.
a of posts, followers, average number of likes and hash tags
b ratio between selfies and other pictures
c sentiments related variations
ese data were extracted and studied over a period of time for each profile s indicated.
Note that this compares to simulation results for the model designed in "Methods and
methodologies and the obtained adaptive network model" section aiming at a single per-
son and his/her related behavior.
Profile:
Initials
F
f/f
a. Popularity
b. Selfie Ratio
c. Percentage Reactions with respect to average number of Likes
Profile:
Initials
F
f
/f
a. Popularity
b. Selfie Ratio
c. Percentage Reactions with respect to average number of Likes
P1:
VF
364867
2547.4
a.
b.
c.
P2:
LV
315400
854.6
a.
b.
c.
0%
20%
40%
60%
80%
100%
Apr-18 Jul-18 Oct-18 Feb-19 May-19 Aug-19 Dec-19 Mar-20
postcountavglikes hashtags followers
0%
20%
40%
60%
80%
100%
Aug-18
Sep-18
Oct-18
Nov-18
Dec-18
Jan-19
Feb-19
Mar-19
Apr-19
May-19
Jun-19
Jul-19
Aug-19
Sep-19
Oct-19
Nov-19
Dec-19
Jan-20
Selfiesavglikes
0%
50%
100%
Aug-18
Sep-18
Oct-18
Nov-18
Dec-18
Jan-19
Feb-19
Mar-19
Apr-19
May-19
Jun-19
Jul-19
Aug-19
Sep-19
Oct-19
Nov-19
Dec-19
Jan-20
Analytical AngerFear JoySadness
Positive Negative Neutralavglikes
0%
20%
40%
60%
80%
100%
Mar-17 Sep-17 Apr-18 Oct-18 May-19 Dec-19 Jun-20 Jan-21
postcountavglikes hashtags followers
0%
20%
40%
60%
80%
100%
Jul-17
Sep-17
Nov-17
Jan-18
Mar-18
May-18
Jul-18
Sep-18
Nov-18
Jan-19
Mar-19
May-19
Jul-19
Sep-19
Nov-19
Jan-20
Mar-20
May-20
Jul-20
Selfiesavglikes
0%
50%
100%
Jun-17
Aug-17
Oct-17
Dec-17
Feb-18
Apr-18
Jun-18
Aug-18
Oct-18
Dec-18
Feb-19
Apr-19
Jun-19
Aug-19
Oct-19
Dec-19
Feb-20
Apr-20
Jun-20
Analytical AngerFear JoySadness
Positive Negative Neutralavglikes
Y
(
t
+
t
)=
Y
(
t
)+[
c∗
Y(
H
Y(
t
)
,C
1,Y(
t
)
,
...
,C
m,Y(
t
)
,P
1,1(
t
)
,P
2,1(
t
)
,
...
,
P1,m(t),P2,m(t),X1(t),...,Xk(t),WX
1
,Y(t),...,WX
k
,Y(t),Y(t)) −Y(t)]�t
dY
(
t
)/
dt
=
c∗
Y(
H
Y(
t
)
,C
1,Y(
t
)
,
...
,C
m,Y(
t
)
,P
1,1(
t
)
,P
2,1(
t
)
,
...
,
P1,m(t),P2,m(t),X1(t),...,Xk(t),WX
1
,Y(t),...,WX
k
,Y(t),Y(t)) −Y(t
)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 25 of 31
Jabeenetal. Appl Netw Sci (2020) 5:84
P3:
CB
262000
430.22
a.
b.
c.
P4:
NM
199762
1008.9
a.
b.
c.
0%
20%
40%
60%
80%
100%
Apr-18 Jul-18 Oct-18 Feb-19 May-19 Aug-19 Dec-19 Mar-20
postcount avglikes hashtags followers
0%
20%
40%
60%
80%
100%
Selfieavglikes
0%
20%
40%
60%
80%
100%
Aug-18
Sep-18
Oct-18
Nov-18
Dec-18
Jan-19
Feb-19
Mar-19
Apr-19
May-19
Jun-19
Jul-19
Aug-19
Sep-19
Oct-19
Nov-19
Dec-19
AnalycalAngerFear JoySadness
PosiveNegaveNeutralavglikes
0%
20%
40%
60%
80%
100%
Apr-18 Jul-18 Oct-18 Feb-19 May-19 Aug-19 Dec-19 Mar-20
postcountavglikes hashtags followers
0%
20%
40%
60%
80%
100%
Aug-18
Sep-18
Oct-18
Nov-18
Dec-18
Jan-19
Feb-19
Mar-19
Apr-19
May-19
Jun-19
Jul-19
Aug-19
Sep-19
Oct-19
Nov-19
Dec-19
Jan-20
Selfies avglikes
0%
20%
40%
60%
80%
100%
Aug-18
Sep-18
Oct-18
Nov-18
Dec-18
Jan-19
Feb-19
Mar-19
Apr-19
May-19
Jun-19
Jul-19
Aug-19
Sep-19
Oct-19
Nov-19
Dec-19
Jan-20
AnalycalAngerFear JoySadness
PosiveNegaveNeutralavglikes
P5:
AA
164719
194.5
a.
b.
c.
P6
LN
40000
33.03
a.
b.
c.
0%
20%
40%
60%
80%
100%
Apr-18 Jul-18 Oct-18 Feb-19 May-19 Aug-19 Dec-19 Mar-20
postcount avglikes hashtags followers
0%
20%
40%
60%
80%
100%
Aug-18
Sep-18
Oct-18
Nov-18
Dec-18
Jan-19
Feb-19
Mar-19
Apr-19
May-19
Jun-19
Jul-19
Aug-19
Sep-19
Oct-19
Nov-19
Dec-19
Jan-20
Selfiesavglikes
0%
20%
40%
60%
80%
100%
Aug-18
Sep-18
Oct-18
Nov-18
Dec-18
Jan-19
Feb-19
Mar-19
Apr-19
May-19
Jun-19
Jul-19
Aug-19
Sep-19
Oct-19
Nov-19
Dec-19
Jan-20
Analytical AngerFear JoySadness
Positive Negative Neutralavglikes
0%
20%
40%
60%
80%
100%
Apr-18 Jul-18 Oct-18 Feb-19 May-19 Aug-19 Dec-19 Mar-20
postcountavglikes hashtags followers
0%
20%
40%
60%
80%
100%
Selfie avglikes
0%
20%
40%
60%
80%
100%
Analytical AngerFear JoySadness
Positive Negative Neutralavglikes
P7:
AG
31600
60.42
a.
b.
c.
P8:
KC
15702
21.99
a.
b.
c.
0%
20%
40%
60%
80%
100%
Apr-18 Jul-18 Oct-18 Feb-19 May-19 Aug-19 Dec-19 Mar-20
postcount avglikes hashtags followers
0%
20%
40%
60%
80%
100%
Aug-18
Sep-18
Oct-18
Nov-18
Dec-18
Jan-19
Feb-19
Mar-19
Apr-19
May-19
Jun-19
Jul-19
Aug-19
Sep-19
Oct-19
Nov-19
Dec-19
Selfie avglikes
0%
20%
40%
60%
80%
100%
Analytical AngerFear JoySadness
Positive Negative Neutralavglikes
0%
20%
40%
60%
80%
100%
Apr-18 Jul-18 Oct-18 Feb-19 May-19 Aug-19 Dec-19 Mar-20
postcountavglikes hashtags followers
0%
20%
40%
60%
80%
100%
Selfie avglikes
0%
20%
40%
60%
80%
100%
Analytical AngerFear JoySadness
Positive Negative Neutralavglikes
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 26 of 31
Jabeenetal. Appl Netw Sci (2020) 5:84
P9:
LC
15279
19.34
a.
b.
c.
P10:
IK
14200
4.95
a.
b.
c.
0%
20%
40%
60%
80%
100%
Apr-18 Jul-18 Oct-18 Feb-19 May-19 Aug-19 Dec-19 Mar-20
postcount avglikes hashtags followers
0%
20%
40%
60%
80%
100%
Selfieavglikes
0%
20%
40%
60%
80%
100%
AnalycalAngerFear JoySadness
Posive NegaveNeutralavglikes
0%
20%
40%
60%
80%
100%
Mar-17 Sep-17 Apr-18 Oct-18 May-19 Dec-19 Jun-20 Jan-21
postcountavglikes hashtags followers
0%
20%
40%
60%
80%
100%
Jun-17
Aug-17
Oct-17
Dec-17
Feb-18
Apr-18
Jun-18
Aug-18
Oct-18
Dec-18
Feb-19
Apr-19
Jun-19
Aug-19
Oct-19
Dec-19
Feb-20
Apr-20
Selfieavglikes
0%
20%
40%
60%
80%
100%
AnalycalAngerFear JoySadness
Posive NegaveNeutralavglikes
P11:
CS
13000
5.19
a.
b.
c.
P12:
IM
11244
26.46
a.
b.
c.
0%
20%
40%
60%
80%
100%
Apr-18 Jul-18 Oct-18 Feb-19 May-19 Aug-19 Dec-19 Mar-20
postcount avglikes hashtags followers
0%
20%
40%
60%
80%
100%
Aug-18
Sep-18
Oct-18
Nov-18
Dec-18
Jan-19
Feb-19
Mar-19
Apr-19
May-19
Jun-19
Jul-19
Aug-19
Sep-19
Oct-19
Nov-19
Dec-19
Selfie avglikes
0%
20%
40%
60%
80%
100%
Analytical AngerFear JoySadness
Positive Negative Neutralavglikes
0%
20%
40%
60%
80%
100%
Apr-18 Jul-18 Oct-18 Feb-19 May-19 Aug-19 Dec-19 Mar-20
postcountavglikes hashtags followers
0%
20%
40%
60%
80%
100%
Selfie avglikes
0%
20%
40%
60%
80%
100%
Analytical AngerFear JoySadness
Positive Negative Neutralavglikes
P13:
SB
11145
11.9
a.
b.
c.
P14:
MB
10486
7.13
a.
b.
c.
0%
20%
40%
60%
80%
100%
Mar-17 Sep-17 Apr-18 Oct-18 May-19 Dec-19 Jun-20 Jan-21
postcount avglikes hashtags followers
0%
20%
40%
60%
80%
100%
Jul-17
Sep-17
Nov-17
Jan-18
Mar-18
May-18
Jul-18
Sep-18
Nov-18
Jan-19
Mar-19
May-19
Jul-19
Sep-19
Nov-19
Jan-20
Mar-20
May-20
Jul-20
Selfie avglikes
0%
20%
40%
60%
80%
100%
Jul-17
Sep-17
Nov-17
Jan-18
Mar-18
May-18
Jul-18
Sep-18
Nov-18
Jan-19
Mar-19
May-19
Jul-19
Sep-19
Nov-19
Jan-20
Mar-20
May-20
Jul-20
Analytical AngerFear JoySadness
Positive Negative Neutralavglikes
0%
20%
40%
60%
80%
100%
Apr-18 Jul-18 Oct-18 Feb-19 May-19 Aug-19 Dec-19 Mar-20
postcountavglikes hashtags followers
0%
20%
40%
60%
80%
100%
Selfie avglikes
0%
20%
40%
60%
80%
100%
Analytical AngerFear JoySadness
Positive Negative Neutralavglikes
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 27 of 31
Jabeenetal. Appl Netw Sci (2020) 5:84
P15:
FC
9600
10.6
a.
b.
c.
P16:
NL
8439
1.97
a.
b.
c.
0%
50%
100%
Sep-17 Apr-18 Oct-18 May-19 Dec-19 Jun-20 Jan-21
postcountavglikeshashtags followers
0%
20%
40%
60%
80%
100%
Selfiesavglikes
0%
20%
40%
60%
80%
100%
Analytical AngerFear JoySadness
Positive Negative Neutralavglikes
0%
20%
40%
60%
80%
100%
Aug-19 Oct-19 Dec-19 Jan-20 Mar-20 Apr-20 Jun-20 Aug-20
postcountavglikes hashtags followers
0%
20%
40%
60%
80%
100%
Oct-19 Nov-19 Dec-19 Jan-20 Feb-20 Mar-20 Apr-20 May-20 Jun-20 Jul-20
Selfies avglikes
0%
20%
40%
60%
80%
100%
Oct-19 Nov-19 Dec-19 Jan-20 Feb-20 Mar-20 Apr-20 May-20 Jun-20 Jul-20
Analytical AngerFear JoySadness
Positive Negative Neutralavglikes
P17:
SM
8400
8.5
a.
b.
c.
P18:
TR
8079
16.16
a.
b.
c.
0%
20%
40%
60%
80%
100%
Sep-17 Apr-18 Oct-18 May-19 Dec-19 Jun-20 Jan-21
postcount avglikes hashtags followers
0%
20%
40%
60%
80%
100%
Jul-17
Sep-17
Nov-17
Jan-18
Mar-18
May-18
Jul-18
Sep-18
Nov-18
Jan-19
Mar-19
May-19
Jul-19
Sep-19
Nov-19
Jan-20
Mar-20
May-20
Jul-20
Selfie avglikes
0%
20%
40%
60%
80%
100%
Jul-17
Sep-17
Nov-17
Jan-18
Mar-18
May-18
Jul-18
Sep-18
Nov-18
Jan-19
Mar-19
May-19
Jul-19
Sep-19
Nov-19
Jan-20
Mar-20
May-20
Jul-20
Analytical AngerFear JoySadness
Positive Negative Neutralavglikes
0%
20%
40%
60%
80%
100%
Apr-18 Jul-18 Oct-18 Feb-19 May-19 Aug-19 Dec-19 Mar-20
postcountavglikes hashtags followers
0%
20%
40%
60%
80%
100%
Selfie avglikes
0%
20%
40%
60%
80%
100%
Analytical AngerFear JoySadness
Positive Negative Neutralavglikes
P19:
EL
7748
3.97
a.
b.
c.
P20:
S
5800
6.66
a.
b.
c.
0%
20%
40%
60%
80%
100%
Apr-18 Jul-18 Oct-18 Feb-19 May-19 Aug-19 Dec-19 Mar-20
postcount avglikes hashtags followers
0%
20%
40%
60%
80%
100%
Selfieavglikes
0%
20%
40%
60%
80%
100%
AnalycalAngerFear JoySadness
Posive NegaveNeutralavglikes
0%
20%
40%
60%
80%
100%
Mar-17 Sep-17 Apr-18 Oct-18 May-19 Dec-19 Jun-20 Jan-21
postcountavglikes hashtags followers
0%
20%
40%
60%
80%
100%
Jul-17
Sep-17
Nov-17
Jan-18
Mar-18
May-18
Jul-18
Sep-18
Nov-18
Jan-19
Mar-19
May-19
Jul-19
Sep-19
Nov-19
Jan-20
Mar-20
May-20
Jul-20
Selfies avglikes
0%
20%
40%
60%
80%
100%
Jul-17
Sep-17
Nov-17
Jan-18
Mar-18
May-18
Jul-18
Sep-18
Nov-18
Jan-19
Mar-19
May-19
Jul-19
Sep-19
Nov-19
Jan-20
Mar-20
May-20
Jul-20
AnalycalAngerFear JoySadness
PosiveNegaveNeutralavglikes
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 28 of 31
Jabeenetal. Appl Netw Sci (2020) 5:84
P21:
KS
4740
3.78
a.
b.
c.
P22:
EZ
4300
3.32
a.
b.
c.
0%
20%
40%
60%
80%
100%
Mar-17 Sep-17 Apr-18 Oct-18 May-19 Dec-19 Jun-20 Jan-21
postcount avglikes hashtags followers
0%
20%
40%
60%
80%
100%
Jul-17
Sep-17
Nov-17
Jan-18
Mar-18
May-18
Jul-18
Sep-18
Nov-18
Jan-19
Mar-19
May-19
Jul-19
Sep-19
Nov-19
Jan-20
Mar-20
May-20
Jul-20
Selfie avglikes
0%
20%
40%
60%
80%
100%
Jul-17
Sep-17
Nov-17
Jan-18
Mar-18
May-18
Jul-18
Sep-18
Nov-18
Jan-19
Mar-19
May-19
Jul-19
Sep-19
Nov-19
Jan-20
Mar-20
May-20
Jul-20
Analytical AngerFear JoySadness
Positive Negative Neutralavglikes
0%
20%
40%
60%
80%
100%
Mar-17 Sep-17 Apr-18 Oct-18 May-19 Dec-19 Jun-20 Jan-21
postcountavglikes hashtags followers
0%
20%
40%
60%
80%
100%
Jul-17
Sep-17
Nov-17
Jan-18
Mar-18
May-18
Jul-18
Sep-18
Nov-18
Jan-19
Mar-19
May-19
Jul-19
Sep-19
Nov-19
Jan-20
Mar-20
May-20
Jul-20
Selfiesavglikes
0%
20%
40%
60%
80%
100%
Jul-17
Sep-17
Nov-17
Jan-18
Mar-18
May-18
Jul-18
Sep-18
Nov-18
Jan-19
Mar-19
May-19
Jul-19
Sep-19
Nov-19
Jan-20
Mar-20
May-20
Jul-20
Analytical AngerFear JoySadness
Positive Negative Neutralavglikes
P23:
OM
4100
3.75
a.
b.
c.
P24:
KL
3632
4.11
a.
b.
c.
0%
20%
40%
60%
80%
100%
Dec-14 Jul-15 Jan-16 Aug-16Mar-17 Sep-17 Apr-18 Oct-18 May-19 Dec-19 Jun-20 Jan-21
postcount avglikes hashtags followers
0%
20%
40%
60%
80%
100%
Jul-17
Sep-17
Nov-17
Jan-18
Mar-18
May-18
Jul-18
Sep-18
Nov-18
Jan-19
Mar-19
May-19
Jul-19
Sep-19
Nov-19
Jan-20
Mar-20
May-20
Jul-20
Selfies avglikes
0%
20%
40%
60%
80%
100%
Jul-17
Sep-17
Nov-17
Jan-18
Mar-18
May-18
Jul-18
Sep-18
Nov-18
Jan-19
Mar-19
May-19
Jul-19
Sep-19
Nov-19
Jan-20
Mar-20
May-20
Jul-20
Analytical AngerFear JoySadness
Positive Negative Neutralavglikes
0%
20%
40%
60%
80%
100%
Mar-17 Sep-17 Apr-18 Oct-18 May-19 Dec-19 Jun-20 Jan-21
postcountavglikes hashtags
0%
20%
40%
60%
80%
100%
Jul-17
Sep-17
Nov-17
Jan-18
Mar-18
May-18
Jul-18
Sep-18
Nov-18
Jan-19
Mar-19
May-19
Jul-19
Sep-19
Nov-19
Jan-20
Mar-20
May-20
Jul-20
Selfie avglikes
0%
20%
40%
60%
80%
100%
Jul-17
Sep-17
Nov-17
Jan-18
Mar-18
May-18
Jul-18
Sep-18
Nov-18
Jan-19
Mar-19
May-19
Jul-19
Sep-19
Nov-19
Jan-20
Mar-20
May-20
Jul-20
Analytical AngerFear JoySadness
Positive Negative Neutralavglikes
P25:
LJ
3200
2.58
a.
b.
c.
P26:
SS
3174
4.78
a.
b.
c.
0%
20%
40%
60%
80%
100%
Aug-19 Oct-19 Dec-19 Jan-20 Mar-20 Apr-20 Jun-20 Aug-20
postcount avglikes hashtags followers
0%
20%
40%
60%
80%
100%
Oct-19 Nov-19 Dec-19 Jan-20 Feb-20 Mar-20 Apr-20 May-20 Jun-20 Jul-20
Selfiesavglikes
0%
20%
40%
60%
80%
100%
Oct-19 Nov-19 Dec-19 Jan-20 Feb-20 Mar-20 Apr-20 May-20 Jun-20 Jul-20
Analytical AngerFear JoySadness
Positive Negative Neutralavglikes
0%
20%
40%
60%
80%
100%
Apr-18 Jul-18 Oct-18 Feb-19 May-19 Aug-19 Dec-19 Mar-20
postcountavglikes hashtags followers
0%
20%
40%
60%
80%
100%
Selfie avglikes
0%
20%
40%
60%
80%
100%
Analytical AngerFear JoySadness
Positive Negative Neutralavglikes
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 29 of 31
Jabeenetal. Appl Netw Sci (2020) 5:84
P27:
KLI
3169
5.93
a.
b.
c.
P28
EB
3094
2.85
a.
b.
c.
0%
20%
40%
60%
80%
100%
May-19 Jul-19 Aug-19 Oct-19 Dec-19 Jan-20 Mar-20 Apr-20 Jun-20 Aug-20
postcountavglikes hashtags followers
0%
20%
40%
60%
80%
100%
Jul-19 Aug-19Sep-19Oct-19 Nov-
19
Dec-19Jan-20 Feb-20 Mar-
20
Apr-20 May-
20
Jun-20 Jul-20
Selfiesavglikes
0%
20%
40%
60%
80%
100%
Jul-19 Aug-19Sep-19Oct-19 Nov-
19
Dec-19Jan-20 Feb-20 Mar-
20
Apr-20 May-
20
Jun-20 Jul-20
Analytical AngerFear JoySadness
Positive Negative Neutralavglikes
0%
20%
40%
60%
80%
100%
Apr-18 Jul-18 Oct-18 Feb-19 May-19 Aug-19 Dec-19 Mar-20
postcountavglikes hashtags followers
0%
20%
40%
60%
80%
100%
Selfie avglikes
0%
20%
40%
60%
80%
100%
Analytical Anger Fear Joy Sadness
Positive Negative Neutralavglikes
P29:
AT
2845
2.5
a.
b.
c.
P30:
AB
1534
1.91
a.
b.
c.
0%
20%
40%
60%
80%
100%
Mar-17 Sep-17 Apr-18 Oct-18 May-19 Dec-19 Jun-20 Jan-21
postcount avglikes hashtags followers
0%
20%
40%
60%
80%
100%
Jul-17
Sep-17
Nov-17
Jan-18
Mar-18
May-18
Jul-18
Sep-18
Nov-18
Jan-19
Mar-19
May-19
Jul-19
Sep-19
Nov-19
Jan-20
Mar-20
May-20
Jul-20
Selfie avglikes
0%
20%
40%
60%
80%
100%
Jul-17
Sep-17
Nov-17
Jan-18
Mar-18
May-18
Jul-18
Sep-18
Nov-18
Jan-19
Mar-19
May-19
Jul-19
Sep-19
Nov-19
Jan-20
Mar-20
May-20
Jul-20
Analytical AngerFear JoySadness
Positive Negative Neutralavglikes
0%
20%
40%
60%
80%
100%
Apr-18 Jul-18 Oct-18 Feb-19 May-19 Aug-19 Dec-19 Mar-20
postcountavglikes hashtags followers
0%
20%
40%
60%
80%
100%
Selfie avglikes
0%
20%
40%
60%
80%
100%
Analytical AngerFear JoySadness
Positive Negative Neutralavglikes
Received: 8 April 2020 Accepted: 7 October 2020
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