The investigated corpus of tweets represents a complex multilevel system, where conceptual knowledge and emotional perceptions are entwined on a number of levels. Tweets are made of text and include words, which convey meaning [32
]. From the analysis of word networks, we can obtain information on the organisation of knowledge proper of social media users, which is embedded in their generated content [16
]. However, tweets also convey meaning through the use of hashtags, which can either refer to specific words or point to the overall topic of the whole tweet. Both words and hashtags can evoke emotions in different contexts, thus giving rise to complex patterns [17
]. Similar to words in natural language, the same hashtags can be perceived and used in language differently by different users, according to the context.
The simultaneous presence of word- and hashtag-occurrences in tweets is representative of the knowledge shared by social media users when conveying specific content and ideas. This interconnected representation of knowledge can be exploited by simultaneously considering both hashtag-level and word-level information, since words specify the meaning attributed to hashtags.
In this section we use MERCURIAL to analyse the data collected. We do so by characterising the hashtag networks, both in terms of meaning and emotional profiles. Precedence is given to hashtags as they not only convey meaning as individual linguistic units but also represent more general-level topics characterising the online discourse. Then, we inter-relate hashtag networks with word networks. Finally, we perform the emotional profiling of hashtags in specific contexts. The combination of word- and hashtag-networks specifies the perceptions embedded by online users around the same entities, for example, coronavirus, in social discourses coming from different contexts.
3.1. Conceptual Relevance in Hashtag Networks
The largest connected components of the three hashtag networks included: 1000 hashtags and 8923 links for #italylockdown
; 720 hashtags and 5915 links for #sciacalli
; 6665 hashtags and 53,395 links for #italylockdown
. All three networks are found to be highly clustered (mean local clustering coefficient [39
] of 0.82) and with an average distance between any two hashtags of 2.1. Only 126 hashtags were present in all the three networks.
reports the most central hashtags included in each corpus of tweets thematically revolving around #iorestoacasa
. Two rankings are considered: (i) hashtag frequency in tweets revolving around a key hashtag, and (ii) closeness centrality, which in here quantifies the tendency for hashtags to co-occur with other hashtags expressing analogous concepts and, therefore, are at short network distance from each other (see Section 2
). Hashtags with a higher closeness centrality represent the prominent concepts in the social discourse. This result is similar to those showing that closeness centrality captures concepts which are relevant for early word acquisition [40
] and production [47
] in language. Additional evidence that closeness can capture semantically central concepts is represented by the closeness ranking, which assigns top-ranked positions to #coronavirus
in all three Twitter corpora. This is a consequence of the corpora being about the COVID-19 outbreak (and of the network metric being able to capture semantic relevance). Frequency and closeness rankings are only partially correlated, indicating that the two metrics highlight different hashtags of relevance in social discourse (Kendall
, for #iorestoacasa
, for #sciacalli
, for #italylockdown
In the hashtag network built around #italylockdown
, the most central hashtags are relative to the coronavirus, including a mix of negative hashtags such as #pandemia
(English: “pandemic“) and positive ones such as #italystaystrong
. Frequency misses this combination and highlights only positive hashtags. Similarly, the hashtag network built around #sciacalli
highlighted both positive (#facciamorete
(English: “let’s network“) and negative (#irresponsabili
—English: “irresponsible“) hashtags. However, the social discourse around #sciacalli
also featured prominent hashtags from politics, including references to specific Italian politicians, to the Italian Government, and hashtags expressing protest and shame towards the acts of a prominent Italian politician. Last but not least, closeness highlighted as prominent #mascherine
or face masks, whereas frequency missed that hashtag. The social discourse around #iorestoacasa
included many positive hashtags, eliciting hope for a better future and the need to act responsibly (e.g., #andratuttobene
- English: “everything will be fine“, or #restiamoacasa
- English: “let’s stay at home“). The most prominent hashtags in each network (cf. Table 1
) indicate the prevalence of a positive social discourse around #iostoacasa
and the percolation of strong political debate in relation to the negative topics conveyed by #sciacalli
. However, we want to extend these punctual observations of negative/positive valences of single hashtags to the overall global networks. To achieve this, we use emotional profiling.
3.2. Emotional Profiling of Hashtag Networks
Hashtags can be composed of individual or multiple words. By extracting individual words from the hashtags of a given network, it is possible to reconstruct the emotional profile of the social discourse around the focal hashtags #sciacalli
. We tackle this by using the emotion-based [18
] and the dimension based [31
] emotional profiles (see Section 2
The emotional profiles of hashtags featured in co-occurrence networks are reported in Figure 2
(top). The top section of the figure represents perceived valence and arousal represented as a circumplex model of affect [31
]. This 2D space or disk is called emotional circumplex
and its coordinates represent emotional states that are well-supported by empirical behavioural data and brain research [31
]. As explained also in the figure caption, each word is endowed with an
coordinate expressing its perceived valence
. Different points indicate different emotional combinations. For instance, (1,0) is the point of maximum/positive valence and zero arousal, that is, calmness; (0,−1) is the point of zero valence and minimum arousal, that is, lethargy; (−0.6,+0.6) represents a point of strong negative valence and positive arousal, that is, alarm.
reports the emotional profiles of all hashtags featured in co-occurrence networks for #italylockdown
(middle) and #iorestoacasa
(right). To represent the interquartile range of all words for which valence/arousal rating are available, we use a neutrality range. Histograms falling outside of the neutrality range indicate specific emotional states expressed by words included within hashtags (e.g., #pandemia
contains the word “pandemia“ with negative valence and high arousal).
3.2.1. Emotional Profiling of Hashtag Networks through the Circumplex Model
In Figure 2
(left, top), the peak of the emotional distribution for hashtags associated with #italylockdown
falls within the neutrality range. This finding indicates that hashtags co-occurring with #italylockdown
, a neutral hashtag by itself, were also mostly emotionally neutral conceptual entities. Despite this main trend, the distribution also features deviations from the peak mostly in the areas of calmness and tranquillity (positive valence, lower arousal) and excitement (positive valence, higher arousal). Weaker deviations (closer to the neutrality range) were present also in the area of anxiety.
This reconstructed emotional profile indicates that the Italian social discourse featuring #italylockdown was mostly calm and quiet, perceiving the lockdown as a positive measure for countering responsibly the COVID-19 outbreak.
Not surprisingly, the social discourse around #sciacalli
shows a less prominent positive emotional profile, with a higher probability of featuring hashtags eliciting anxiety, negative valence and increased states of arousal, as it can be seen in Figure 2
(center, top). This polarised emotional profile represents quantitative evidence for the coexistence of mildly positive and strongly negative content within the online discourse labelled by #sciacalli
. This is further evidence that the negative hashtag #sciacalli
was indeed used by Italian users to denounce or raise alarm over the negative implications of the lockdown, especially in relation to politics and politicians’ actions. However, the polarisation of political content and debate over social media platforms has been encountered in many other studies [12
] and cannot be attributed to the COVID-19 outbreak only.
Finally, Figure 2
(top right) shows that positive perception was more prominently reflected in the emotional profile of #iorestoacasa
, which was the hashtag massively promoted by the Italian Government for supporting the introduction of the nationwide lockdown in Italy. The emotional profile of the 6000 hashtags co-occurring with #iorestoacasa
indicate a considerably positive and calm perception of domestic confinement, seen as a positive tool to stay safe and healthy. The prominence of hopeful hashtags in association with #iorestoacasa
, as reported in the previous subsection, indicate that many Italian Twitter users were serene and hopeful about staying at home at the start of lockdown.
3.2.2. Emotional Profiling of Hashtag Networks through Basic Emotions
Hashtag networks were emotionally profiled not only by using the circumplex model (see above) but also by using basic emotional associations taken from the NRC Emotion lexicon (Figure 2
, bottom). Across all hashtag networks, we find a statistically significant peak in trust (z-scores > 1.96 ), analogous of the peaks close to emotions of calmness and serenity an observed in the circumplex models. However, hashtag networks included also negative emotions like fear, which is a natural human response to unknown threats and were observed also with the circumplex representations. All networks featured less disgust eliciting words than random expectation. The intensity of fearful, alarming and angry emotions is stronger in the #sciacalli
hashtag network, which was used by social users to denounce, complain and express alertness about the consequences of the lockdown.
In addition to the politically-focused jargon highlighted by closeness centrality alone, by combining closeness with graph distance entropy (see Section 2
and Reference [33
]) we identify other topics which are uniformly at short distance from others in the social discourse around #sciacalli
, such as: #mascherine
(English: “protective masks“, which was also ranked high by using closeness only), #amuchina
(the most popular brand, and synonym of, hand sanitiser), #supermercati
(English: “supermarkets“). This result suggests an interesting interpretation of the negative emotions around #sciacalli
. Beside the inflaming political debate and the fear of the health emergency, in fact, a third
element emerges: Italian twitter users feared and were angry about the raiding and stockpiling of first aid items, symptoms of panic-buying in the wake of the lockdown.
3.3. Assessing Conceptual Relevance and Emotional Profiles of Hashtags via Word Networks
The above comparisons indicate consistency between dimension-based (i.e., the circumplex) and emotion-specific emotional profiling. Since the latter offers also a more precise categorisation of words in emotions, we will focus on emotion-specific profiling. Importantly, to fully understand the emotional profiles outlined above, it is necessary to identify the language expressed in tweets using a given combination of hashtags (see also Figure 1
, bottom). As the next step of the MERCURIAL analysis, we gather all tweets featuring the focal hashtags #italylockdown
, or #iorestoacasa
and any of their co-occurring hashtags and build the corresponding word networks, as explained in the Methods. Closeness centrality over these networks provided the relevance of each single word in the social discourse around the topic identified by a hashtag. Only words with closeness higher than the median were reported.
shows the cloud of words appearing in all tweets that include #sciacalli
, displayed according to their NRC emotional profile. Similar to the emotional profile extracted from hashtags co-occurring with #sciacalli
, the words used in tweets with this hashtag also display a polarised emotional profile with high levels of fear and trust. Thanks to the multi-layer analysis, this dichotomy can now be better understood in terms of the individual concepts eliciting it.
By using closeness on word networks, we identified concepts such as “competente“ (English: “competent“), “continua“ (English: “continue“, “keep going“), and “comitato“ (English: “committee“) to be relevant for the trust-sphere. These words convey trust in the expert committees appointed by the Italian Government to face the pandemic and protect the citizens. We find that other prominent words contributing to make the discourse around #sciacalli trustful are “aiutare“ (English: “to help“), “serena“ (English: “serene“), “rispetto“ (English: “respect“) and “verità“ (English: “truth“), which further validate a trustful, open-minded and fair perception of the political and emergency debate outlined above. This perception was mixed with negative elements, mainly eliciting fear but also sadness and anger. The jargon of a political debate emerges in the word cloud of fear: “difficoltà‘ (English: “difficulty“), “criminale“ (English: “criminal“), “dannati“ (English: “scoundrel“), “crollare“ (English: “to break down“), “banda“ (English: “gang“), “panico“ (English: “panic“) and “caos“ (English: “chaos“). These words indicate that Twitter users felt fear directed to specific targets. A speculative explanation for exorcising fear can be finding a scapegoat and then target it with anger. The word cloud of such emotion supports the occurrence of such phenomenon by featuring words like “denuncia“ (English: “denouncement“), “colpevoli“ (English: “guilty“), “vergogna“ (English: “shame“), “combattere“ (English: “to fight“) and “colpa“ (English: “blame“). The above words are reflected also in other emotions like sadness, which features also words like “cadere“ (English: “to fall“) and “miseria“ (English: “misery“, “out of grace“).
These prominent words in the polarised emotional profile of #sciacalli
, suggest that Twitter users feared criminal behaviour, possibly related to unwise political debates or improper stockpiling of supplies (as showed by the hashtag analysis). Our findings also suggest that the reaction to such fearful state, which also projects sadness about negative economic repercussions, was split into a strong, angry denounce of criminal behaviour and messages of trust for the order promoted by competent organisations and committees. It is interesting to note that, according to Ekman’s theory of basic emotions [24
], a combination of sadness and fear can be symptomatic of desperation, which is a critical emotional state for people in the midst of a pandemic-induced lockdown.
The same analysis is reported in Figure 4
for the social discourse of #italylockdown
(top) and #iorestoacasa
(bottom). In agreement with the circumplex profiling, for both #italylockdown
the intensity of fear is considerably lower than trust.
However, when investigated in conjunction with words, the overall emotional profile of #italylockdown appears to be more positive, displaying higher trust and joy and lower sadness, than the emotional profile of #iorestoacasa. Although the difference is small, this suggests that hashtags alone are not enough to fully characterise the perception of a conceptual unit, and should always be analysed together with the natural language associated to them.
The trust around #italylockdown
comes from concepts like “consigli“ (English: “tips“, “advice“), “compagna“ (English: “companion“, “partner“), “chiara“ (English: “clear“), “abbracci“ (English:“hugs“) and “canta“ (English: “sing“). These words and the positive emotions they elicit suggest that Italian users reacted to the early stages of the lockdown with a pervasive sense of commonality and companionship, reacting to the pandemic with externalisations of positive outlooks for the future, for example, by playing music on the balconies. This phenomenon was also mimicked in other countries later, and extensively reported by traditional media, see https://tinyurl.com/balconicovid
, Last Access: 20 April 2020).
Interestingly, this positive perception co-existed with a more complex and nuanced one. Despite the overall positive reaction, in fact, the discourse on #italylockdown also shows fear for the difficult times facing the contagion (“contagi“) and the lockdown restrictions (“restrizioni“), and also anger, identifying the current situation as a fierce battle (“battaglia“) against the virus.
The analysis of anticipation, the emotional state projecting desires and beliefs into the future, shows the emergence of concepts such as “speranza“ (English: “hope“), “possibile“ (English: “possible“) and “domani“ (English: “tomorrow“), suggesting a hopeful attitude towards a better future.
The social discourse around #iorestoacasa brought to light a similar emotional profile, with a slightly higher fear towards being quarantined at home (quarantena (English: “quarantine“), comando (English: “command“, “order“, emergenza (English: “emergency“). Both surprise and sadness were elicited by the the word “confinamento“ (English: “confinement“), which was prominently featured in the network structure arising from the tweets we analysed.
In summary, the above emotional profiles of hashtags and words from the 101,767 tweets suggest that Italians reacted to the lockdown measure with:
a fearful denunciation of criminal acts with political nuances and sadness/desperation about negative economic repercussions (from #sciacalli);
positive and trustful externalisations of fraternity and affect, combined with hopeful attitudes towards a better future (from #italylockdown and #iorestoacasa);
a mournful concern about the psychological weight of being confined at home, inspiring sadness and disgust towards the health emergency (from #iorestoacasa).
3.4. Hashtag Co-Occurrence Contextually Influences Hashtag Emotional Profiles
In the previous section we showed our findings on how Italians perceived the early days of lockdown on social media. But what about their perception of the ultimate cause of such lockdown, COVID-19? To better reconstruct the perception of #coronavirus
, it is necessary to consider the different contexts where this hashtag occurs. Figure 5
displays the reconstruction of the emotional profile of words used in tweets with #coronavirus
and either #italylockdown
, or #iorestoacasa
Our results suggest that the emotional profiles of language used in these three categories of tweets are different. For example, when considering tweets including #sciacalli, which the previous analysis revealed being influenced by political and social denounces of criminal acts, #coronavirus is perceived with a more polarised fear/trust dichotomy.
Although #coronavirus was perceived as trustful as random expectations when co-occurring with #sciacalli (z-score: 1.69 < 1.96), it was perceived with significantly higher trust when appearing in tweets with #iorestoacasa (z-score: 3.05 > 1.96) and #italylockdown (z-score: 3.51 > 1.96). To reinforce this picture, the intensity of fear towards #coronavirus was statistically significantly lower than random expectation in the discourse of #iorestoacasa (z-score: −2.35 < −1.96) and #italylockdown (z-score: −3.01 < −1.96).
This difference is prominently reflected in both the circumplex model (Figure 5
, right) and the NRC emotional profile (Figure 5
, left), although in the latter both emotional intensities are compatible with random expectation. These quantitative comparisons provide data-driven evidence that Twitter users perceived the same conceptual entity, that is, COVID-19, with a higher trust when associating it to concrete means for hampering pathogen diffusion like lockdown and house confinement, and with a higher fear when denouncing the politics and economics behind the pandemic.
However, social distancing, lockdown and house confinement clearly do not have only positive sides. Rather, as suggested by our analysis, they bear complex emotional profiles, where sadness, anger and fear towards the current situation and future developments have been prominently expressed by Italians on social media.