# Cognitive networks identify the content of English and Italian popular posts
about COVID-19 vaccines: Anticipation, logistics, conspiracy and loss of trust

Massimo Stella  Department of Computer Science, University of Exeter, UK
Corresponding author at<EMAIL_ADDRESS>Michael S. Vitevitch
Department of Psychology, University of Kansas, USA  Federico Botta
Department of Computer Science, University of Exeter, UK

###### Abstract

Monitoring social discourse about COVID-19 vaccines is key to understanding
how large populations perceive vaccination campaigns. We focus on 4765 unique
popular tweets in English or Italian about COVID-19 vaccines between 12/2020
and 03/2021. One popular English tweet was liked up to 495,000 times,
stressing how popular tweets affected cognitively massive populations. We
investigate both text and multimedia in tweets, building a knowledge graph of
syntactic/semantic associations in messages including visual features and
indicating how online users framed social discourse mostly around the
logistics of vaccine distribution. The English semantic frame of "vaccine" was
highly polarised between trust/anticipation (towards the vaccine as a
scientific asset saving lives) and anger/sadness (mentioning critical issues
with dose administering). Semantic associations with "vaccine," "hoax" and
conspiratorial jargon indicated the persistence of conspiracy theories and
vaccines in massively read English posts (absent in Italian messages). The
image analysis found that popular tweets with images of people wearing face
masks used language lacking the trust and joy found in tweets showing people
with no masks, indicating a negative affect attributed to face covering in
social discourse. A behavioural analysis revealed a tendency for users to
share content eliciting joy, sadness and disgust and to like less sad
messages, highlighting an interplay between emotions and content diffusion
beyond sentiment. With the AstraZeneca vaccine being suspended in mid March
2021, "Astrazeneca" was associated with trustful language driven by experts,
but popular Italian tweets framed "vaccine" by crucially replacing earlier
levels of trust with deep sadness. Our results stress how cognitive networks
and innovative multimedia processing open new ways for reconstructing online
perceptions about vaccines and trust.

##  1 Introduction

Social media have given voice to millions of individuals. These massive
digital audiences are increasingly being studied to better understand how
socio-cognitive interactions create [1, 2, 3], manipulate [4, 5] and promote
[6, 7] specific perceptions about the online- and real worlds. On a more
descriptive level, social media represent de-localised information systems
where individual users share their own experiences and perceptions through
language [8]. In this way, identifying cognitive features [9] of the language
used on social media provides insight into how large audiences perceived and
coped with specific events [10, 8].

The present work used quantitative analyses to examine the language used on
social media, in particular its emotional spectrum [11] and semantic content
[12], to reconstruct popular perceptions about one specific event: the
announcement of mass production of COVID-19 vaccines at the end of 2020.
Taking inspiration from other recent works using social media as data for
monitoring current perceptions and emotional responses to the coronavirus
pandemic [13, 14, 15, 16], we here adopt the recent framework of cognitive
network science [17, 18] in order to reconstruct the conceptual and emotional
associations addressing COVID-19 vaccines. We focus our attention on popular
messages on Twitter, with popularity being identified by the platform itself
and corresponding to relatively high statistics of content sharing and liking.
Our reconstruction of the key semantic frames reported in popular social media
tweets represents a way to assess the online perceptions that reach massive
audiences liked by up to 495,000 users (the population of a medium-sized city
in the UK). Extracting and understanding these stances towards the COVID-19
vaccine is crucial in terms of identifying potential signals of distress [19,
20], e.g. social users highlighting key challenges or denouncing issues with
vaccine distribution that could promptly be solved once fully identified, or
negative attitudes of closure [21, 22, 23, 10], e.g. conspiracy jargon that
might hamper vaccination campaigns and have severe repercussions for pandemic
containment.

Building on the above necessity for quantitative investigations of socio-
cognitive attitudes towards the COVID-19 vaccine, we provide more details
about the cognitive aspects that characterise our approach and compare it
against other data-driven works in the relevant literature [22, 5, 16, 15,
24].

###  1.1 Related work: Reconstructing cognitions and perceptions with complex
networks

The identification of people’s views on something is known as stance detection
in computer science and psycholinguistics [25]. This task is key for
understanding how conversations portray specific topics, such as individuals
being in favour or against a given set of prescriptions about the COVID-19
pandemic. Studying stances through language has been historically performed
through human intervention, which involves a person reading text,
reconstructing syntactic and semantic associations between words in the text
and then classifying the result. Human intervention is clearly not sustainable
when dealing with thousands of interconnected stances as expressed in
thousands and thousands of online social posts [16]. The advent of social
media content gave voice to millions of internet users, a voice that could
potentially report stances of relevance for understanding how real-world
events are debated online [8]. Towards this direction, computer and data
science recently developed numerous approaches and frameworks to tackle stance
detection, mainly powered by machine learning and artificial intelligence, cf.
[11, 26, 25]. Although machine learning is usually highly accurate in
detecting whether a stance is positive or negative [22] or reconstructing the
polarity and intensity of the sentiment expressed in language [5, 26], it
provides little information about the underlying structure of the stance. That
is, such approaches are not able to understand how conceptual elements are
entwined in a given stance in order to determine its overall meaning and
emotional outline. Achieving more interpretable models of language processing
and stance detection remains an open challenge [25, 10].

The present work merges machine learning with cognitive networks [17], which
are network representations of how linguistic knowledge is connected and
processed within the human mind in a cognitive system known as the mental
lexicon [9]. There are multiple ways to build networks out of texts, like
using the relevance of words across paragraphs [27] or co-occurrence networks
to identify writing styles and authorship across manuscripts [28]. However, in
reconstructing a stance it is necessary to identify not only conceptual
associations but also emotional trends and sentiment patterns [25]. This
combination is necessary in order to identify how individuals semantically
framed specific concepts of social discourse (i.e. which associates surround a
specific word), and what emotions revolve around each concept/idea. The
inextricable connection between knowledge and affect recently led to the
framework of textual forma mentis networks (TFMNs), cf. [29], which can
extract syntactic, semantic, and emotional associations between words in text
to create a knowledge graph that reconstructs the structure of the knowledge
that authors embed in their posts. Networks like the ones used in the present
work have successfully highlighted how students and researchers perceived STEM
subjects [18], how trainees changed their own mindset after a period of formal
training [30], and have also identified key concepts in short texts [29].

###  1.2 Motivation: Cognitive networks operationalise semantic frame theory

In cognitive science, semantic frame theory [12] indicates that meaning is
attributed to individual concepts in language by means of syntactic/semantic
relationships and further specified by words that are associated with that
concept. In other words, the connotation denoted by an author to a concept
might be reconstructed by checking which words were associated to it. For
example, one text may frame the "gender gap" as a challenge that can be
tackled by celebrating women’s success in science, whereas another text may
describe the "gender gap" in more pessimistic tones (cf. [29]). Syntactic
dependencies and semantic links thus provide key information for
reconstructing the stance surrounding a given idea in terms of a network
neighbourhood of concepts associated to a given word/idea coming from a mental
lexicon [9]. Textual forma mentis networks operationalise semantic frame
theory by identifying how concepts/words are associated to each other in
sentences. In this network structure the first associates of a concept form a
network neighbourhood that identifies how a concept was framed by authors, and
which emotions populate that frame [8].

###  1.3 Manuscript aims and outline

This manuscript uses cognitive network science to reconstruct how popular
tweets, available to hundreds of thousands of users, semantically framed and
emotionally portrayed different aspects of COVID-19 vaccines. The Methods
section contains details about the Twitter dataset, and the language and image
processing methods implemented here. The Results section combines frequency-
and networks-based analyses of key words in popular tweets together with
quantitative inquiries of specific semantic frames reconstructed as networks.
The Results include also behavioural and picture analyses reporting the
emotions of content that was highly- or less-liked or -shared. The Discussion
section links the current findings with the relevant research on COVID-19 and
cognitive modelling. Emphasis is given to other works reporting analogous or
synergistic results in terms of social media analyses, COVID-19 and human
behaviour.

##  2 Methods

###  2.1 Twitter dataset

This work relied on a main collection of 1962 unique popular tweets in English
and 2413 unique popular tweets in Italian, gathered by the main author through
Complex Science Consulting’s Twitter-authorised account ($@ConsultComplex$).
Tweets were collected through ServiceConnect[] in Mathematica 11.3. Only
tweets including the word "vaccine" or the hashtag #vaccine were considered.
The flag "Popular” in ServiceConnect[] gave access to trending tweets as
identified by the Twitter platform.

Tweets were gathered between December 10 2020 and January 17 2021, a time
window covering the early announcements of vaccines becoming available for
mass vaccination and the subsequent discussion about the vaccination campaign.
Notice that the geographic location of tweets was not available in Mathematica
11.3, making it impossible to distinguish tweets based on their country of
origin (e.g. US vs UK). For each Tweet, statistics like the number of retweets
and the number of likes (at the time of the query) were registered. English
popular tweets were liked on average $20,000\pm 48,000$ times, indicating a
distribution of liked content critically skewed towards large values, and
including tweets being liked up to 495,120 times. These English popular tweets
were retweeted on average $3,000\pm 6,000$ times with a single tweet being
shared up to 57,821 times. Italian tweets registered lower values of liked
content ($1,000\pm 2,000$ with a maximum of 12,359 for a single tweet) and
sharing ($150\pm 200$ with a maximum of 2043 shares of a single tweet).

Twitter IDs and additional info like web links to pictures were also gathered
and processed.

After the temporary suspension of the AstraZeneca vaccine in several EU
countries, including Italy, we gathered an additional set of 228 popular
tweets in English and 180 popular tweets in Italian focusing on the keyword
astrazeneca.

###  2.2 Language processing and network construction

Text was extracted from each tweet in the dataset with the aim of building a
knowledge graph of syntactic, semantic, and emotional associations between
words, i.e. a textual forma mentis network (TFMN) [29]. Emojis in tweets were
translated in words by using Emojipedia (https://emojipedia.org/people/, last
accessed 1 July 2020), which characterises individual emojis in terms of plain
words. Hashtags were translated by using a simple overlap between the content
of the hashtag without the # symbol and English/Italian words (e.g., #pandemic
became “pandemic”). The resulting lists of words were then stemmed in order to
get rid of word declination (e.g. "loving" and "love" representing the same
stem "love"). Word stemming was performed by using WordStem[] in Mathematica
11.3 for English and SnowballC as implemented in R 3.4.4 for Italian (called
through the RLink function in Mathematica 11.3). Stemming is particularly
important for Italian, where nouns can be declined differently according to
their gender (e.g., “dottoressa” and “dottore” both indicate the concept of a
doctor). Stemming is important also in relation to the cognitive
interpretation of a forma mentis network and knowledge representation in the
human mind [9, 31]. In fact, overwhelming evidence from psycholinguistics
shows that different declinations of the same word do not alter the core
meanings and emotions attributed to their stem [31]. For instance, "loving"
and "loved" both activate the same conceptual construct related to love in
language processing by individuals. Hence, these words should be represented
with the same lexical unit in a cognitive network representing human knowledge
as derived from text.

Knowledge representation was achieved through the building of a textual forma
mentis network, whose main idea is to use machine learning for unearthing the
complex network of syntactic relationships between words in sentences [8].
This network is not explicitly observed in the text (i.e., we do not see links
between words when reading this or other texts), but is mentally reconstructed
to associate the nouns, verbs, objects and specifiers in a sentence in order
to figure out the meaning of a certain message. Textual forma mentis networks
(TFMNs) are knowledge graphs enriched with cognitive perceptions about how
massive populations associate and perceive individual words. Connections
between lexical units/concepts are multiplex and indicate: (i) syntactic
dependencies (e.g., in “Love is for the weak” the meaning of “love” is linked
to the meaning of “weak” by the specifier “is for”) or (ii) synonyms (e.g.,
“weak” and “frail” overlapping in meaning in certain linguistic contexts).
Syntactic dependencies were extracted from each sentence in tweets by using
TextStructure[] in Mathematica 11.3, which relies on the Stanford NLP
universal parser. Synonyms were identified by using WordNet 3.0 and its
Italian translation [32]. The resulting syntactic/semantic network was
enriched with emotional features attributed to individual words/nodes.
Valence, arousal, and the emotions elicited by a given concept were attributed
to individual words according to external cognitive datasets (see next
subsection). In this way, TFMNs combine cognitive information about how
individuals associate concepts eliciting different sentiment, excitement and
emotions in texts.

The main English TFMN included 2190 words and 19534 links whereas the Italian
TFMN contained 1752 words and 24654 links. The networks built in the aftermath
of AstraZeneca’s vaccine temporary suspension included respectively 410 words
and 5953 links for English tweets, and 233 words and 2390 links for Italian
tweets. "Vaccine" had a network degree [17] of over 800 in main English and
Italian networks and of over 200 in networks based on popular tweets from the
aftermath of vaccine suspension. Meaning modifiers like negation words (e.g.
"not" or "no") were included in the network in order to keep track of meaning
negation in emotional profiling. Words linked to negations were changed to
their antonyms as extracted from WordNet 3.0 [32] and added to the semantic
frame when computing emotional profiles.

###  2.3 Cognitive datasets

This study examined two datasets to reconstruct the emotional profile of
language in texts: valence and arousal as coming from the psycholinguistic
task implemented by Warriner and colleagues [33] and the Emotion Lexicon by
Mohammad and Turney [11]. Both datasets summarise how large populations of
individuals perceive individual words, either by rating of pleasantness
(valence) or excitement (arousal) or by listing which emotions are elicited by
such words (e.g. "disease" elicits the emotion of fear). Valence and arousal
act as coordinates in a 2D space mapping several human emotions. This mapping
between language and emotions is known as the circumplex model [34] and it has
been successfully used in several psycholinguistic investigations [15]. The
emotional states reconstructed through the Emotional Lexicon were: Joy,
Sadness, Fear, Disgust, Anger, Surprise, Anticipation and Trust. Although the
first 6 emotional states are self-explanatory, the last 2 identify emotional
perceptions of either projecting one’s experience into the future
(anticipation) or accepting norms and following behavioural codes imposed by
others because of personal relationships or logical reasoning (trust) [35].

We used the valence and arousal data of English words in order to build 2D
density histograms identifying emotional trends in a given portion of
language. As a linguistic baseline for emotional neutrality we adopted the
interquartile range as computed from 13,900 English words in the Warriner et
al. dataset [33]. Clusters of words falling outside of the neutrality range
indicate the presence of an emotional trend in language [15].

We used the Emotional Lexicon in order to count how many words $n_{E}(w)$
elicited a given emotion $E$ in a given semantic frame/network neighbourhood
surrounding a concept $w$. We then compared each count against the expectation
of a random null model drawing words at random from the overall emotional
dataset. In each randomisation we drew uniformly at random as many words as
those eliciting for any emotion present in the network neighbourhood. After
repeating 500 random samplings, we computed a z-score for each emotion $E$,
namely:

$\centering z_{E}=\frac{n_{E}(w)-\langle
n_{E}^{r}(w)\rangle}{\sigma^{r}_{E}(w)},\@add@centering$ (1)

where $\langle n_{E}^{r}(w)\rangle$ is the average random count of words
eliciting a given emotion as expected in the underlying dataset (which
features more words eliciting for some specific emotions and less concepts
inspiring other emotions). $\sigma^{r}_{E}(w)$ is the standard deviation of
the random counts. Z-scores higher than 1.96 (significance level of 0.05)
indicate an excess of words eliciting a given emotion and surrounding the
concept $w$ in the structure of social discourse. We plot emotional profiles
as emotional flowers, where z-scores are petals distributed along 8 emotional
dimensions. Petals falling outside of a semi-transparent circle, namely the
rejection region relative to $z<1.96$, indicate a concentration of emotional
jargon more extreme than expectation from the word-to-emotion mapping in
common language (as coded in the sampled dataset and preserved by uniform
random sampling).

The valence-arousal dataset was translated from English into Italian through a
consensus translation using Google Translate, DeepL and Microsoft Bing. For
the Emotion Lexicon, the authors used the automatic translations provided by
Mohammad and Turney in Italian [11].

Figure 1 summarises the above steps for giving structure to the language and
pictures posted in popular tweets.

Figure 1: Infographics about how textual forma mentis networks can give
structure to the pictures and language posted by online users on social media.
Semantic frames around specific ideas/concepts are reconstructed as network
neighbourhoods. Word valence and emotional data make it possible to check how
concepts were framed by users in posts mentioning (or not) pictures showing
specific elements (e.g. people wearing a face mask). A flowchart with the
different steps of network construction is outlined too.

###  2.4 Picture processing

Tweets can often include images which are associated to the text. These can be
used to support the emotional content shared in the tweet, and provide a
visual medium which is complementary to text. Here, we analyse the image data
in a variety of ways to complement the analysis of textual data, and to
provide a further, deeper understanding of the emotional content shared on
Twitter.

####  2.4.1 Text extraction

We download all images associated to English tweets and we process them using
Google’s Tesseract-OCR Engine via the Python-tesseract wrapper
(https://github.com/tesseract-ocr/tesseract, Last Accessed: 22/03/2021). This
uses a neural network based OCR engine to extract text from images. The
resulting text from each processed image is then analysed using the language
processing methods described above. It is important to highlight that not all
images contain text, and in those cases the output of the OCR engine returns
an empty string. Manual verification of a sample of the text extracted from
the images has shown a good accuracy of the algorithm (above 95%).

####  2.4.2 Face and mask detection

Face masks have been one of the trademarks of the COVID-19 pandemic, with the
majority of countries worldwide introducing rules and recommendations on when
and where face masks should be worn. Face masks have also often been a
controversial topic, with polarised views from the general public on their
perception in relation to personal freedom. As such, it is to be expected that
a range of images associated to our tweets contain people wearing face
coverings. This is of relevance to our analysis, since the public perception
and polarisation about face masks will undoubtedly influence one’s emotions
about COVID-19 and vaccines.

However, the appearance and widespread use of face masks across the globe is
mostly a recent phenomenon. Whilst face detection is a challenge which has
been widely studied in the image processing community, detection of face masks
has not been so prominent until recently. Detecting face coverings can be
broken down into two different, sequential tasks: first, the algorithm has to
detect the presence (and location) of faces within an image; then, for each
detected face, the algorithm has to identify whether it is wearing a face
mask. To analyse the images in our data set, we use a recently developed face
mask detection algorithm made available via the facemask-detection Python
package (https://github.com/ternaus/facemask_detection, Last Accessed:
22/03/2021). This offers a pre-trained algorithm which carries out both the
face detection step, and then assigns a probability to each detected face
corresponding to the probability of there being a face covering. The face
detection step uses a recently developed face detector, known as RetinaFace
[36]. This algorithm uses state of the art deep learning techniques to output
the location, in terms of a bounding box, of each face detected in an image. A
key strength of RetinaFace is its ability to detect faces at various scales,
where faces can be present both at the front as well as in the background of
an image. On top of the RetinaFace layer, the face mask detection step uses a
pre-trained set of deep neural networks to output a probability value for each
face detected by the RetinaFace step. Probability values larger than 0.5
correspond to faces which the algorithm detected as wearing a face mask.

In our analysis, we process all images and, for each image, we collect the
number of detected faces by the RetinaFace layer, as well as how many of those
faces have been assigned a probability larger than 0.5 of having a face mask.
Note that we include in this analysis all images, regardless of whether they
contain any text or not. This is because we have observed that some images
contain text overlayed on top of a normal image, which in some cases contains
faces and masks. Therefore, the face mask detection step is applied to all
images in the data set of English tweets.

Finally, it is worth highlighting that, whilst manual inspection of the
results of the face and mask detection step show a very good accuracy, there
are undoubtedly some cases in which this method fails to identify all faces or
masks correctly. Whilst to be expected, such a limitation must be kept in mind
when drawing conclusions from the results of our analysis.

####  2.4.3 Dominant colour analysis

Another key component of the visual aspect of images associated to tweets is
that associated to colours. Colour analysis of images posted to Instagram has
revealed a link between Hue, Saturation and Value (HSV) and individuals with
depression [37]. This suggests that images may, to an extent, reflect the
emotional and well-being status of individuals who choose to share those
images online. Here, we extract the dominant colour values in terms of the hue
value. The hue value represents the colour on the light spectrum, with low
values representing red and large values representing blue and purple. To
extract a single hue value from each image, we perform a two-step analysis on
each image which does not contain textual data. First, we run the k-means
clustering algorithm on the HSV values of each pixel for each image. We then
extract the centroid of the largest cluster found, and consider the
corresponding HSV values as the dominant values of the image. Note that this
is only an approximation of the dominant colour, and we use $k=5$ in each
image. After this initial step, each image is represented by its dominant HSV
values. Visual inspection of the resulting dominant HSV values across all
images analysed indicates the presence of two strong clusters in the hue
component, with one cluster centred on low values of hue (in the red spectrum)
and a second cluster centred on large values of hue (in the blue spectrum).
Based on this finding, we perform a second clustering step, using the k-means
clustering algorithm with $k=2$ to group the images in two clusters: one with
dominant hue values in the red area; and one with dominant hue values in the
blue area. More detail on this are provided in the Supplementary Information
figures.

## Results

This section outlines the results of the analysis of popular tweets in terms
of: (i) prominent concepts in social discourse captured by frequency of
occurrence and network centrality, (ii) focus on the emotional-semantic frames
of "vaccine" and "vaccino" in social discourse, (iii) other semantic and
emotional frames of prominent concepts in social discourse, (iv) behavioural
comparisons of tweet sharing and liking depending on the emotional profile of
posts, (v) picture-enriched analysis of online language.

###  2.5 Prominent concepts in social discourse captured by frequency of
occurrence and network centrality

This part of the study focused on identifying the key ideas reported in social
discourse about the COVID-19 vaccine. Table 1 provides the 20 top-ranked
concepts identified through word frequency and degree in the TFMN. Word
frequency identifies how many times a single word was repeated across popular
tweets and were potentially read by users. Degree counts how many different
syntactic/semantic associations were attributed to a given concept, and
captures in the textual forma mentis network semantic richness, i.e. the
number of connotations and semantic associates attributed to a single word
[15, 17].

As highlighted in Table 1 (left), English popular tweets featured mostly
jargon relative to the idea of "people receiving their first dose of vaccine".
This pattern was consistent between the ranks based on word frequency and
semantic richness/network degree, respectively. These prominent words and
additional key jargon related to the semantic sphere of time (like "week",
"now" and "when") together indicate a social discourse dominated by a
projection into the future, relative to the logistics of vaccine distribution.
Network degree identified also Trump as a key actor of popular tweets.
Differently from word frequency, semantic richness highlighted how "workers"
and "work" were prominently featured in popular tweets.

The Italian social discourse also featured key words related to people
receiving their first dose of the vaccine. Key actors of social discourse in
the Italian twittersphere were Pfizer and Moderna. Italian users mentioned
medical jargon (e.g. "doctor", "virus", "effects") more prominently than
English speakers, in terms of both semantic richness and frequency. As in
English, Italian discourse was also strongly dominated by words related to the
semantic sphere of time, including prominent words like "time", "hour" and
"day".

The above rankings indicate how social discourse in popular tweets about the
COVID-19 vaccine were prominently projected towards future plans for dose
distribution. According to the above simple semantic analysis, it can be
postulated that the specific semantic frame of "vaccine" (and "vaccino" in
Italian) also should be populated by emotions like anticipation into the
future.

Rank | Degree | Frequency
---|---|---
1 | vaccine | vaccine
2 | will | we
3 | get | covid
4 | people | will
5 | dose | get
6 | take | people
7 | say | first
8 | receive | now
9 | first | all
10 | new | dose
11 | make | take
12 | trump | coronavirus
13 | govern | million
14 | out | when
15 | worker | new
16 | distribute | need
17 | work | health
18 | million | after
19 | week | rollout
20 | need | virus

Rank | Degree | Frequency
---|---|---
1 | vaccino | vaccino
2 | prima (first) | dose
3 | dose | contro(against)
4 | stato (state) | più (plus)
5 | tutti (all) | covid
6 | contro (against) | prima (first)
7 | fatto (fact) | pfizer
8 | chi (who) | italia
9 | persona (person) | tutti (all)
10 | casi (cases) | tempo (time)
11 | dati (data) | moderna
12 | virus | fatto (fact)
13 | medico (doctor) | solo (only)
14 | tempo (time) | virus
15 | prendere (take) | ora (now/hour)
16 | parte (part) | giorno (day)
17 | morti (deaths) | ansa
18 | paese (country) | oggi (today)
19 | passare (transit) | dati (data)
20 | arrivare (arrive) | effetti (effects)

Table 1: Top-20 key concepts in the English (left) and Italian (right)
corpora. Words are ranked according to their degree in textual forma mentis
networks and their frequency in the original tweets. Italian words were
translated in English for an easier visualisation.

###  2.6 Semantic frames and emotional profiles of "vaccine" in popular tweets

Figure 2 focuses on the semantic frame surrounding "vaccine" in both English
(top) and Italian (bottom) popular tweets. Three representations are compared:
(i) a word cloud of words in the neighbourhood of "vaccine" with size
proportional to their degree in the overall TFMN and a sector chart counting
the proportion of words eliciting a certain emotion, (ii) an emotional flower
indicating excess of emotions in the same neighbourhood as z-scores/petals
(see Methods), and (iii) a circumplex model of emotions plotting a 2D
valence/arousal histogram of words populating the neighbourhood of "vaccine".
Both the circumplex model and the emotional flower (models relying on
different datasets) agree in indicating a polarised emotional profile of
"vaccine" in popular tweets, concentrating around both positive/calm and
negative/alerted emotional states. As reported in the emotional flower,
anticipation into the future was the strongest emotional state populating the
semantic frame of "vaccine". However, the petals/z-scores falling outside the
rejection region (white circle) indicate also a concentration of words
eliciting trust and joy, but also anger, disgust, and sadness that is higher
than random expectation.

As described in the Introduction, TFMNs enable a direct access to the semantic
frame surrounding key ideas in social discourse. The above results indicate
that popular tweets were indeed mostly dominated by words related to future
events (anticipation), as indicated also by the above prominence analysis.
However, the framing of vaccines in tweets was not emotionally uniform, but
rather strongly polarised around alarming and more positive/calm tones. The
word cloud in Figure 2 (top) identifies how emotions where associated with
different concepts.

Figure 2: Emotional analysis and word clouds of concepts in the semantic frame
of "vaccine" (in English) and "vaccino" (in Italian). The circumplex model
indicates how the neighbors of vaccine/vaccino populate a 2D arousal/valence
space. The emotion flower indicates an excess of emotions detected in the
semantic frame compared to random expectation. The sector chart reports the
raw fraction of words eliciting a certain emotion. The word cloud reports the
top 10% concepts with the highest degree centrality and associated with
vaccine. Words are distributed according to the emotions they elicit.

###  2.7 Semantic frames and emotional profiles of other prominent concepts of
social discourse

The rankings based on degree/semantic richness and frequency of words in
social discourse show that a key theme of popular tweets was "people getting a
dose of vaccine". This key topic and the overall dominance of anticipation
into the future, as reported in the previous section, both underline how
relevant the logistics of vaccine distribution in the health system was in the
considered COVID-19 tweets. This makes it important to investigate how
concepts like "distribute" and "health" were framed by social discourse.

Figure 3 (bottom) reports the semantic frame surrounding "health" (bottom
left) and "distribute" (bottom right). In both the visualisations, semantic
frames are identified as network neighbourhoods and organised in communities
as identified by the Louvain algorithm [38]. Network communities correspond to
more tightly interconnected clusters of words, reflecting different semantic
aspects of a given frame [17]. The network visualisations are complemented by
emotional flowers. Discourse surrounding "health" was mostly dominated by
syntactic/semantic associations with other positive jargon, featuring both:
(i) actors of the health system (e.g. doctors, hospitals, volunteers), and
(ii) aspects of vaccine delivery and administration. Popular tweets
importantly linked vaccine distribution with vulnerable groups and mentioned
the urgency of suitable plans for administering the vaccine despite the
current crisis.

Importantly, popular tweets also drew a conceptual association between
"health" and "racism", making a point about the necessity of fair measures of
health provision. The overall emotional profile of all the above aspects was
dominated by anticipation but included also sadness (related to the
difficulties of the current crisis) and trust towards the health system, its
actors and its supporters, like countries, nations and science.

Popular tweets were less positive when framing the specific concept of
"distribute", whose semantic frame was mostly populated by anticipation into
the future. The forma mentis network highlighted many negative associations
indicating a potentially challenging and disastrous management of the limited
available resources (e.g. "failure", "badly", "hoard", "suffer", "demand",
"scandal"). Positive associates were generally confined to the semantic area
of product delivery and democratic, quick management of resources.
Institutions, administrations and nations (e.g. "president", "trump",
"administrate") were tightly connected with concepts related to the semantic
spheres of speed and time (e.g. "week", "month", "speed", "warp"). This
represents additional evidence that popular tweets about the COVID-19 vaccine
underlined the need for a quick administering of vaccine doses.

A shift into the future is present also in other semantic frames, see Figure 3
(top). Whereas previous investigations reported semantic frames for "pandemic"
filled with negative emotions [15], in the popular tweets analysed here the
current pandemic was contrasted with overwhelmingly positive jargon, featuring
concepts like "care", "create", "live" and "shield". These conceptual
associations balanced out negative emotions and provided an overall frame of
associates for "pandemic" mostly devoid of any emotion except for anticipation
into the future.

Negative emotions like disgust or sadness were found in the associates of
"dose" and "worker", together with more positive emotional states like trust
and joy. More in detail:

  * •

The associations attributed to "dose" identified aspects like "delay",
"trial", "waste", "fear" and "conspiracy", highlighting expressions of
concerns about the validity of a dose of vaccine;

  * •

Sadness around "workers" had as semantic associations "vulnerable", "expose",
"funeral", "essential", "suffer" and "severe", indicating how popular tweets
highlighted the importance for exposed workers to receive a vaccine.

  * •

The above trend co-existed with positive emotions originating from celebratory
jargon ("thanks", "celebrate") identifying the importance of workers during
the pandemic.

Whereas Italian popular tweets did not feature jargon related to conspiracy
theories, English popular tweets provided a rather highly clustered network
neighbourhood for "hoax", devoid of negations of meaning and featuring mostly
jargon related to the future. Associations of "hoax" with ideas like "censor",
"pandemic" and "vaccine" indicate a concerning portrayal of conspiracy
theories within the considered sample of popular tweets. This represents
quantitative evidence that conspiracy theories revolving around the COVID-19
vaccine were capable of reaching large audiences online through highly shared
and liked (i.e. popular) tweets.

Figure 3: TFMNs capturing conceptual associations in social discourse around
"pandemic", "dose", "worker" and "hoax" (top) and around "health" and
"distribute" (bottom). Positive (negative) concepts are cyan (red). Neutral
concepts are in blue. Associations between positive (negative) concepts are
highlighted in cyan. Purple links connect concepts of opposite valence. Green
links indicate overlap in meaning. The emotional flowers indicate how rich the
reported neighbourhoods are in terms of emotional jargon. Petals falling
outside of the inner circle indicate a richness that differs from random
expectation at $\alpha=0.05$. Each ring outside of the circle corresponds to
one unit of z-score.

###  2.8 User behavioural trends on Twitter based on emotions

To test how users reacted to emotional content in popular tweets we studied
the emotional profiles of highly-/less-retweeted and liked messages. Notice
that these distinctions were based on retweet or like counts being higher or
lower than their respective medians. Figure 4 reports the emotional profiles
of highly-/less retweeted (left) or liked (right) tweets in English (top) and
in Italian (low). In English, highly retweeted or liked tweets contained
language with an emotional content drastically different to the one embedded
in less shared or liked content. An excess of sadness, joy, and disgust
characterised highly retweeted text messages, emotions absent in popular yet
less frequently retweeted content. The measured levels of trust and
anticipation remained high across all the considered classes.

Figure 4: Multi-language analysis of the emotional profiles of highly-/less
retweeted (left) or liked (right) tweets in English (top) and in Italian
(low). Petals indicate z-scores and are higher than the threshold of 1.96 when
falling outside of the semi-transparent circle.

These results indicate that the behavioural strategies behind content sharing
and the tendency for users to retweet content displayed an emotional
interplay. Trust and anticipation, the strongest emotions surrounding
"vaccine", did not change significantly between highly/less re-tweeted and
liked content. Instead, positive emotions of high arousal (i.e. joy) or
negative emotions eliciting risk-averse behaviour (i.e. disgust or sadness)
corresponded to a boost of content spreading in English. Interestingly, in
another language, i.e. Italian, only a small difference in joy was detected.
Notice how, with like counts, sadness characterised less liked popular tweets
and was not found in highly liked messages. This indicates a tendency for
users not to like sad content while still actively engaging with it through
retweeting.

###  2.9 Processing together pictures and text: Colours, people and face masks

In addition to text processing, we enriched our analysis with an investigation
of the multimedia content promoted in popular tweets.

An analysis reading the text of pictures included in popular tweets (see
Methods) identified portions of language sharing the very same emotional
content of the shorter text of tweets themselves. This finding indicates an
overall consistency of language between the header of a tweet and the content
of the picture attached to it.

With no difference detected in the text reported in pictures, we focused our
attention on pictures portraying negligible, e.g. a single word, or no text at
all. An analysis of the hue values identified mainly two predominant colours
in these pictures, as contained in popular tweets, namely hue values in the
red region of the spectrum and hue values in the blue region (see
Supplementary Figure 1). Whereas the circumplex model identified a
polarisation of emotions being present across all tweets and ranging between
calmness to alarm, emotional profiling through the Emotional Lexicon
identified a lower level of anticipation into the future in the language
describing specifically pictures with a predominant blue colour (see
Supplementary Figure 2). A human coding of pictures revealed that blue was the
main colour for the backgrounds and foregrounds in pictures displaying people
being vaccinated. Hence the observed decrease in anticipation indicates that
when describing the specific event of vaccination, language becomes less
projected into the future.

In order to better investigate how language and pictures are entwined, we
performed a specific content analysis based on machine vision and focusing on
the portrayal of people and pandemic-related objects. Since the detection of
syringes or vaccine vials would be problematic, we focus on objects more
tightly connected to people like face masks.

###  2.10 Investigating the language of tweets with pictures of people
wearing, or not, face masks

We built three textual forma mentis networks each based on one of the
following corpora of tweets: (i) posts including pictures of no people, (ii)
posts including pictures of people wearing no face masks and (iii) posts
including features of people wearing face masks. Note that we include in this
third category all tweets for which an image contains at least one face mask,
even though there may be other faces without a mask.

The emotional profiles of "vaccine" contained in these three categories of
multimedia are reported in Figure 5. Different emotions are found to populate
the semantic frame of "vaccine" across these three categories. The language of
popular tweets including pictures with no people is strongly polarised between
trust/joy and disgust, emotions that are absent in popular tweets portraying
people.

Tweets showing people wearing face masks corresponded to an emotional profile
for "vaccine" different from the one of tweets showing people without masks.
Pictures showing the full face of a person were accompanied with more trustful
and slightly more joyous language - when associated to the idea of the vaccine
- in comparison to the messages accompanying tweets with people wearing face
masks. No difference was found in terms of anticipation, which permeates all
the considered semantic frames of "vaccines" in Figure 5. The combination of
anticipation, trust and joy in popular tweets with people wearing no face
masks is a marker for hopeful emotional states, projected with positive affect
into the future. This pattern is confirmed by the association between
"vaccine" and "hope" in the respective semantic frame (see Fig. 5 bottom).
This hopeful framing vanished in the language of messages reporting people
wearing face masks.

Figure 5: Emotional flowers and valenced semantic frames for "vaccine" in
those tweets including pictures with: (i) no people (left), (ii) people
wearing no face masks and (iii) people wearing face masks. On the top part of
the panel there are example pictures that were taken from Pixabay for
demonstrating how the implemented Python library works. Bottom: Semantic
frames reporting only negative and neutral words associated to "vaccine".

###  2.11 Aftermath of the temporary suspension of AstraZeneca’s vaccine: Loss
of trust in the Italian twittersphere

On March 15th 2021, several European countries, including Italy, temporarily
suspended the use of the COVID-19 vaccine developed by AstraZeneca, following
sparse reports of serious side effects.

Figure 6 reports the emotional profiles of "vaccine" and "astrazeneca" in
popular tweets gathered in the next few days after the suspension. In
comparison with the popular perceptions observed in December 2020 and in
January 2021, as summarised in Figure 2, the temporary suspension of the
AstraZeneca vaccine had drastic effects over social discourse in the Italian
twittersphere but not in the English one.

English popular tweets still framed the idea of the vaccine along strong
signals of anticipation into the future and trust. Trust was found in the
semantic frame of "vaccine" but not with regards to the syntactic/semantic
associates of "astrazeneca", indicating a potential shift in trust between the
general concept of a COVID-19 vaccine and the concrete one by AstraZeneca’s as
described in popular tweets.

A more drastic shift in the emotions of vaccines was found in the Italian
twittersphere. By comparing Figures 1 (bottom) and 6 (bottom) one notices how
trust, joy and anticipation expressed by Italian users when mentioning
"vaccine" in December and January vanished completely in the aftermath of the
AstraZeneca temporary suspension in mid March 2021. Positive emotions
disappeared from the semantic frame of "vaccine" and were replaced with a weak
signal of sadness, indicating concern as expressed by Italian users in popular
messages.

Notice how sadness permeated the semantic frame of "vaccine" but not the
language surrounding "astrazeneca" in Italian, which was rather associated
with concepts mildly eliciting trust. A human coding of the popular tweets
revealed that the detected trustful language was mainly relative to
epidemiology experts trying to convey the relevance of the vaccination
programme and the unfairness of incorrect statistical biases towards
vaccination and its risks as portrayed by news media.

Figure 6: Emotional flowers for "vaccine" and "astrazeneca" in popular tweets
gathered after the suspension of the AstraZeneca vaccine in several EU
countries in mid March 2021. These results should be compared with the
emotional profiles reported in Figure 2 and relative to months before the
suspension.

## Discussion

This work investigated social media language around COVID-19 vaccines. Focus
was given to popular messages on the Twittersphere, i.e. content identified by
the platform as being highly re-tweeted and liked by online users.

Social media provide crucial data for understanding how large audiences
perceive and react to events [5, 8]. Past approaches used social media traffic
for inferring electoral outcomes in massive voting events [4, 3] and more
recent approaches adopted social media language to understand how massive
populations coped with the COVID-19 pandemic [13, 15, 20, 24]. The overarching
theme of these approaches, including the current one, is that language is a
driver of emotional content and conceptual knowledge that is transferred from
people’s minds into digital posts [8]. Accessing and modelling knowledge in
social discourse becomes thus a proxy for reconstructing how massive audiences
framed - semantically and emotionally - events in their cognitive
representations of the world [12]. In particular, understanding how popular
posts frame ideas and emotions is crucial because popularity can lead to one
single tweet being read and endorsed by up to 500k users. While not every user
might be human [1, 6], these numbers indicate how crucial popular content can
be in influencing people’s perceptions of real-world and online events, as
confirmed also by recent works [4, 3].

Popular tweets were found to portray mainly logistic aspects of vaccine
distribution. Frequent and semantically rich [9] concepts of social discourse
were relative to the necessity for people to receive as soon as possible their
first dose of vaccine despite the issues of administering massive amounts of
vaccine.

English discourse in popular tweets was found to be strongly emotionally
polarised. Emotional profiles featured at the same time strong signals of
trust/joy and sadness/anger. Negative emotions were found to be channelled in
language denouncing the issues of vaccine distribution rather than in the
pandemic by itself, differently from what was found in the early stages of the
pandemic [13, 20, 24, 15]. Trust and joy were rather relative to jargon
celebrating science and its success in delivering a tool for fending off the
pandemics. As a future research direction it would be interesting to identify
whether these opposing emotions were expressed by the same set of users over
time or were symptoms for the creation of different topics coexisting in the
online information flow (like detected in [5] and more recently in the socio-
semantic analysis by [39]).

Almost no emotional polarisation was found in the semantic frame of "vaccine"
in Italian, where an excess of positive emotions like joy and trust were
found, in addition to anticipation. According to Ekman’s atlas of emotions
[35], these basic emotions in language can give rise to nuances like positive
expectations for the future, i.e. hope.

Regretfully, the hopefulness expressed by Italians in popular tweets vanished
completely after the case of the temporary suspension of the AstraZeneca
vaccine in Italy and in several other EU countries. Our retrieved emotional
profiles identified a drastic shift in the emotional portrayal of vaccine in
online popular tweets. We registered a transition from a trustful and hopeful
perception to a subsequent connotation of "vaccine" permeated by sadness, with
no anticipation, joy or trust surrounding it. This is a concerning finding
because of increasing psychological evidence that a lack of trust towards the
health system and the institutions regulating is a marker for a reluctance to
adopt health-related social norms like vaccination [40, 22], with concrete
negative repercussions for global health.

A key innovation of our approach was combining cognitive networks of
linguistic associations [17, 29] together with pictures and AI-based methods
of image analysis [37, 36]. In addition to evidence of emotional coherence
between the text embedded in pictures and the one coming from tweets, our work
identified some differences in the language accompanied by specific categories
of pictures. English popular tweets showing no pics of people were found to
frame the idea of "vaccine" along with contrasting emotions of trust and
disgust. A semantic network analysis for these emotional profiles identified
their source in discussion focused on potential side effects and, more
importantly, in associations reporting vaccines as ways to mitigate the impact
of COVID-19.

Language accompanying tweets with people wearing a face mask exhibited almost
no signal of trust or joy in the semantic frame of "vaccine", differently from
the portrayal of vaccines produced by messages including pictures with people
wearing no face mask. Although co-occurrence does not imply causation, it must
be noted that the adoption of face masks in public places has been met with
mixed results (for a review see [41]) by most Western countries. Masks hinder
expressiveness and can create discomfort if worn for long times but there are
also additional psychological elements. In fact, recent psycholinguistic
studies showed how face masks became strongly associated with negative
concepts like sickness and disease in the cognitive perception of the COVID-19
pandemics [23]. In this way, by merging pictures and social media discourse,
our results indicate that the overall perception of people wearing face masks
is biased, i.e. poorer in terms of trust and joy when compared to common
portrayals of people wearing no face protection. Future research should
explore the emotional profiling of face masks and other aspects of COVID-19
vaccines on larger scales.

We also identified a behavioural tendency for English users to re-share more
emotionally extreme content, i.e. featuring stronger signals of disgust,
sadness and joy, but also to favour less content enticing more sadness. These
patterns were absent in the Italian twittersphere. The interplay between
emotions and content re-sharing extends previous results related to sentiment
polarity [5] which highlighted a positive bias in content sharing and liking,
i.e. positive sentiment promoting content sharing and endorsement. Our
findings indicate that also negative, inhibiting emotions like disgust can
amplify content sharing while sadness inhibits endorsement of online posts.

Last but not least, our approach identified concerning associations between
vaccines and conspiratorial jargon. Conceptual associations between hoaxes and
vaccines have been traced in many other studies, cf. [22]. However, in this
case these associations were found in popular messages and not in borderline
peripheral content, like the one produced by malignant social bots [1, 6]. A
negative framing of vaccines in terms of hoaxes driven by popular messages to
large populations could self-evidently have negative consequences for the
vaccination campaign. A recent study from cognitive neuroscience found that
conspiracy-like misinformation can decrease pro-vaccination attitudes by
exploiting the emotion of anger [42] (an emotion detected here) rather than
through fear (which was not detected here). Anger can activate and amplify
reactions like feeling frustrated or fooled by the establishment, which then
lead to behavioural changes [35]. In this way both the conspiratorial semantic
associations and emotional signal of anger in the semantic frame of "vaccine"
in popular English tweets should serve as early warning signals of
misinformation hampering the vaccination campaign.

Notice that our analysis is subject to some limitations. Textual forma mentis
networks can adapt their structure but not their valence/emotional labels to
the text being analysed. This is because affect data comes from predetermined
large populations and it is representative of the way common language portrays
concepts [33, 26, 29]. Because of contextual shifts, it might be that the
affective connotation of specific concepts in a given discourse could be
different [30]. For instance, "vaccine" by itself was rated as mostly neutral
in the dataset by Warriner and colleagues [33] but it was associated with
mostly positive jargon in social discourse (see Figure 2, bottom). This
limitation underlines the importance of considering words as connected with
each other and not in isolation. This is because TFMNs enable the
reconstruction of contextual shifts in affect by considering how words were
associated with each other in language. Another limitation of the current
approach is that the multi-language support stems mainly from automatic
translation. Hopefully in the future more cognitive datasets will be built by
considering native speakers’ data.

As a future research direction it would be interesting also to merge the
current semantic-emotional analysis together with content credibility
dynamics, as recently quantified in social discourse about the vaccine by
Pierri and colleagues [16].

## Conclusions

Our work provides a methodological framework for reconstructing trending
perceptions in social media via language, network and picture analyses.
Warning signals of conspiratorial content and a dramatic loss of trust towards
the vaccination campaign were unearthed by our investigation. Our results
stress the possibilities opened by innovative and quantitative analyses of
semantic frames and emotional profiles for understanding how large populations
of individuals perceive and discuss events as extreme as the global pandemic
and ways out of it like vaccines.

## Acknowledgements

The authors acknowledge Riccardo Di Clemente for insightful discussion.

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## SUPPLEMENTARY INFORMATION

This section provides additional information and quantitative evidence
supporting the main text.

Figure 7: *

Supplementary Figure 1 \- Left: elbow plot showing how the within cluster sum
of squares varies as the number of clusters increase, when clustering the
images based on their dominant hue values. As the plot indicates, two clusters
seem to be the optimal choice. Right - histogram of the dominant hue values
detected from the images (note: only those not containing any text were
analysed in this scenario). As visual inspection suggests, we observe two
clusters centred on the red and blue tones of hue.

Figure 8: *

Supplementary Figure 2 \- Top: Word cloud of the most frequent words in tweets
with pictures with blue or red as predominant. Bottom: Emotional flowers and
circumplex model for the emotions of the language used in tweets with pics of
different predominant colours.