Twitter has been widely used as a tool for emergency response in previous crises and disasters and a large body of work has focused on optimizing communication in order to adjust or nudge human behaviour under such conditions [1, 2, 3, 4] . Research finds that during a time of crisis, when information is scarce and heavily sought after, social media enables the critical flow of information due to its collaborative nature [5, 6] . However, the underlying network and community structure is likely to have a significant influence over which information users are exposed to [7, 8] . In particular, retweet interactions have been shown to reflect real-life community structure, such as political parties [9] , and to characterize the information sharing dynamics of different clusters of users [10] . Likewise, communities of users sharing misleading news tend to be more connected and clustered [11] .
The structure of such information networks has been assessed in various disease outbreak scenarios, most prominently in the context of the Zika virus outbreak 2015-2016 in the US [12] . Among other findings the work suggests that key international experts acted as "boundary spanners", who were able to spread information quickly through the network. Even though it has been observed that false information travels faster in social networks [13, 14] , the work finds that Twitter users have made efforts to diffuse reliable information by such key experts. Related work demonstrates how the association between the geographical location of users dictates topics of conversations, thereby matching the temporal and geographical spread of the Zika outbreak [15] .
Previously listed work allows for anecdotal evidence of crisis-induced behaviour on social media, but compared to the COVID-19 crisis the events were 1) shorter in time scale 2) more localized and 3) orders of magnitude smaller in terms of analyzed data. As the virus spread across the world within only a few weeks, Twitter became the primary news source among expert groups and medical personnel. Reliable sources, such as peer-reviewed literature or other officially vetted channels, were simply too slow to be useful during this time [16] . However, this initially positive role of Twitter and other social media has now been overshadowed by the spread of numerous conspiracy theories and other low-quality mis-and disinformation about the pandemic, peaking in what the WHO now considers an "infodemic" [17, 18] . Preliminary work focused on this issue and used social network analysis in order to determine drivers of the conspiracy theory which claims a link between 5G and COVID-19 [19] . Although misinformation is a major concern, more recent work suggests that experts and authorities are being heard and have received disproportionate attention [20] , but that key specialists may experience low reachability [21] . Moreover, other work finds that key medical professionals and scientific experts may experience lower "sustained amplification", meaning that the attention given to this group has not been constant, and overall lower, compared to media outlets or key political figures [22] . Our work attempts to clarify the somewhat ambiguous premises about the role of experts during the pandemic.
With the help of a comprehensive dataset of more than 350 million tweets, we identify the key communities of English speaking Twitter users involved in the COVID-19 debate, starting from early January to June 2020. We then provide a detailed analysis of the evolution of this massive communication network and characterize the interaction dynamics regulating the sharing of information both within and between the communities.
In the giant component of the directed network (see Material and methods, 4.4), the out-degree distribution (i.e. number of retweets received by each user) follows a very skewed distribution, typical of many real-world networks ( Figure S1 ). Users with an out-degree higher than 1500 represent the 0.1% of the users in the network, but their tweets have been retweeted >200M times (77.0% of all retweets). The community detection algorithm reveals thousands of communities, spanning from millions down to duplets, with size decreasing very sharply ( Figure S2 ). By aggregating over multiple repetitions of the algorithm, we identify 15 communities (labeled with letters from A to O, in decreasing order of size) with more than 105 users, encompassing 97.9% of all users in the giant component. Figure 1a shows the network of 1M users (corresponding to 4.3% of all nodes) randomly sampled but forming a connected component. Even if the layout algorithm has no information about the communities we identified, it recovers a similar stratification of the nodes. We observe that communities A and B fill a great portion of the network, without being as densely connected as the other communities. Some communities (C, H, F, L) are more peripheral and exhibit a neat modular structure, which implies a high degree of internal connectivity (see also Fig S3) .
In order to characterize the 15 largest communities, we 1) infer the presumed location of the users and 2) use the results of a Machine Learning classifier, which was trained to predict the user category based on a user's description (see Materials and methods 4.2). Self-reported location information was available for 54% of nodes in the network, while a clear category could be assigned to 25% of considered users (Table S1 ). Since the diversity in nationality of the communities is of interest, we calculate the entropy of the distribution of countries for each community (similarly Figure 1 : Retweet network of a randomly sampled connected component of 1M users, colored by community. Node size is proportional to node out-degree. In the table, the column "S" designates the color of the super-communities used throughout this work, "C" lists the community color in the network layout and "Name" the respective community name. The "Dominant category" column specifies the most abundant user category in the community (excluding "Other"). "Size" denotes the ratio of users in the community with respect to the total number of users in the network.
to the alpha diversity index in ecological communities): a high entropy indicates an almost uniform distribution of users across all countries, whereas a low entropy implies an uneven distribution, possibly skewed toward a specific country. Some communities are strongly associated with user location, in particular communities D (UK), E (Philippines and Southeast Asia), F (India), G (African countries), J (Canada), L (Pakistan) and M (Australia). The US represents more than a half of the users from communities C, H, I, and K, while communities A, B, N and O are more heterogeneous in terms of location, containing a large fraction of tweets located outside of the US.
In some communities, the most retweeted users have a strong association to political and cultural topics. In order to better understand these mixed communities, we investigate the predicted user categories for each community (see Materials and methods 4.2 and Figure S4 ). Out of the 13 categories, the category "Other" is the majority category for all considered communities, being assigned to 78% of all users. Science, being the second largest category (at 4.7% of all users), was represented at 9% or higher in communities B, D, G, J and M. Other communities, such as H or C, have a higher fraction of users who identify with a political movement.
In this study, we are particularly interested in the specific user categories "Science", "Healthcare", "Media", "Politics & Government", "Public Services", and "Political Supporter". The percentage of users in these six categories and the entropy measure for internationality were used for hierarchical clustering at the community level (see Figure 2 ). With the help of the emerging dendrogram, we recognize four super-communities, which we name as follows:
• International expert: communities exhibiting an increased number of users of category "Science" and a high international diversity.
• National elite: communities exhibiting an increased number of users belonging to official categories (i.e. "Science", "Healthcare", "Media", "Politics & Government", and "Public Services") and a low international diversity.
• Political: communities exhibiting an increased number of users associated with political activism (category "Po-litical Supporter") or involved in politics ("Politics & Government"). • Other: communities which are not linked to any of the categories of interest. This includes artists, entrepreneurs, and non-governmental activists.
The naming of super-communities is further validated by the manual inspection of the top users in the respective communities. In particular, only two communities (B and G) are assigned to "international expert", showing a clearly distinguishable pattern. These communities consist of a large fraction of users who are presumably working in scientific or health-related fields. 
In order to trace the evolution of the network throughout the pandemic, we reconstructed several networks by splitting the Twitter data into non-overlapping windows of 1 week. We collapse the 1-week retweet networks into networks with four nodes, corresponding to the four previously assigned super-communities (see Figure 3a ) and thereby compute the so-called mixing matrix [23] . In these networks, we draw a link from super-community i to j, with a weight w ij equal to the number of times supercommunity j retweeted i in a given week.
We assign each node a size attribute N i , representing the number of users of super-community i retweeting or being retweeted in the given week. Figure 3b shows the temporal evolution of this attribute. Two principal observations can be made: first, the value of N varies over time in correspondence with the phases of the epidemic. We identify an initial peak in total number of users in the beginning of February (peak a), and a second one at the end of March (peak b). These two peaks presumably correspond to the first diffusion of news about COVID-19 in China and, later, to their diffusion worldwide ( Figure S5 ). In the time between peak a and b the number of users in the COVID-19 debate has doubled, followed by a slow decay between April and June. Second, most of the change in the number of users stems from the "Other" super-community. This implies that the three super-communities of interest remained present throughout the entire observation period at a relatively static level.
In Figure 3c , we show the average attention per user of super-community i, defined as:
i.e. the weighted out-degree of super-community i, normalized by its size. The international expert super-community faces an increase in average attention per user in January and stabilizes at a higher level compared to other supercommunities until the beginning of March. After a narrow peak in March, the political super-community plateaus in April at roughly three times the attention level of national elites and international experts.
We then split the total attention, i.e. the sum of all weighted edges W , for each super-community i into an internal and external component for every weekly network:
represents the attention given to super-community i from the other supercommunities, while the internal component a int i quantifies self-amplification. Figure 3d shows that the external attention component is decreasing overall, indicating a decrease in attention between super-communities. This is particularly true for the international expert super-community, which received broad attention in the very beginning of the pandemic, peaking again in mid-February, and then decaying in a monotonic way until the end of our sampling. shows that the internal attention component is increasing overall, highlighting an increased selfamplification within the network. We observe that this increase is mostly driven by the political communities and to a lesser degree by the national elites. The international expert super-community, on the other hand, decreased internal sharing of content after March. Overall, we note that the dynamics partially mirror Figure 3c , since the internal attention component makes up most of the total attention given to the super-communities.
So far, in our analysis the observed dynamics of supercommunities is a result of the average behavior across all the users in the Twittersphere, while in reality most of the dynamics are driven by a relatively small set of users who receive disproportionate attention. Further, we have focused on the number of retweets (node out-degree) as a canonical measure of attention, but the retweet count of a single viral tweet might exaggerate the user's real impact in the overall debate.
In this section, we address these caveats. Here, we only consider the top 1000 users for each super-community in terms of retweets received, i.e. 4000 users receiving 55.0% of all retweets. Furthermore, we adopt an alternative measure to characterize the attention given to users, namely a retweet h-index, as previously introduced by [22] . Originally proposed in the context of academic citations [24] , the h-index in this case reflects that the user has received at least h retweets on h of their original tweets. Generally, users belonging to the political supercommunity are ranked highest both in terms of retweets and h-index, receiving most of the attention both on a punctual and an extended time scale. National elites and international experts behave very similarly: they rank medium to high in terms of h-index, but low in the number of retweets received (low virality). Lastly, the "Other" category ranks generally lowest in h-index and intermediate in retweets, thus is characterized by a higher virality in terms of attention.
In order to understand the temporal dimension of these results, we formulate the previous metrics in a timedependent fashion on the same set of top users. We consider all tweets and retweets posted within a rolling time window of 1 month width and a 1 week step. We then compute the rank by h-index ( Figure 4c ) and retweets (Figure 4d ) averaged by super-community. Additionally, we show the resulting data as a vector plot (Figure 4e ). We find that international experts scored very highly in both metrics initially and then experienced a drop in ranks. Similarly, national elites faced an initial decline in both metrics but then increased in ranks above the level of international expert after April. The temporal view of this data therefore adds nuance to the picture obtained in Figure 4b .
We conclude that although national elites and international experts share a significant overlap in the static view (cf. Figure 4b) , they reveal distinct temporal dynamics (cf. Figure 4c-e) . This result reflects how the international expert community faced a decline in attention, while the discussion has been moving onto more local grounds, with the political and national debate gaining momentum.
In this work we use a complex network approach to answer some relevant questions about the role of experts in relation to the COVID-19 pandemic in the English-speaking Twittersphere. Using a community detection algorithm, we identify 15 user communities, exhibiting a stable structure throughout the pandemic. We are able to group these communities into four main super-communities related to the prevalent user categories and the degree of internationality (namely international expert, national elite, political, and other) and assess their interaction patterns over time.
In the Twitter landscape of COVID-19 we identify a single major group of scientific experts with a highly international distribution of users, and multiple country-specific communities which appear to engage more in the respective national debates. Additionally, we find several large country-specific communities which are mostly characterized by political activism, thus highlighting the substantial politicization in the discussion surrounding the pandemic.
Our results emphasize the role of the international supercommunity of scientific experts in the beginning of the, at the time, largely unknown pandemic. This supercommunity received disproportionate attention and had broad reach across many cultural and political communities, as reflected by the high total volume of retweets received, in particular from non-expert communities. As demonstrated by the high internal attention component, international experts also shared content frequently among themselves, possibly in an attempt to rapidly share scientific insights about the novel virus.
In a second critical moment in March the number of COVID-19 cases exploded in almost all parts of the world, leading to massive media attention, which is well reflected in the increase in community sizes, as well as in the total number of tweets. It is noteworthy that this added attention has not been allocated to international experts but rather to political leaders, reflecting the loss of influence of the former in the evolution of the debate.
As the pandemic unfolded in April, and while many English speaking countries underwent a strict lockdown, we observe a growing politicization of the debate, reflected by the fact that content by the political communities is now shared most. Meanwhile, the analysis reveals the picture of an increasingly segregated international expert super-community. Furthermore, compared to January and February, and in contrast to all other groups, the international expert super-community also reduced interactions among themselves, even though their size remained constant.
The analysis of the sustained amplification patterns of highly retweeted users mostly confirms our previous analysis: compared to political leaders, top users in the international expert super-community received intermediate levels Our work allows for the resolution of some of the discrepancies in recent literature on the role of scientific experts in the COVID-19 pandemic by adding a temporal dimension to the picture. We are able to confirm that scientific experts were heard early on in the pandemic, as found by [20] . This also fits in well with previous literature, which confirms that Twitter users amplify information from trusted sources in crisis situations [25, 26, 27] . Further, we detect and trace these groups on the interaction network level and find that the reachability and attention of scientific experts, as received both from within the group and from outside, has declined over time, as indicated by earlier work [21] . We can partially confirm previous work [22] that suggested low sustained attention to top medical experts. However, by giving a temporal dimension to our analysis, we find that scientific experts ranked highest both in sustained and punctual attention in the very beginning of the pandemic and only later became increasingly isolated in terms of attention. We believe this is an important result which underlines the role of scientific experts as possible boundary spanners during the early phase of a pandemic.
Our claims are based on a comparatively large dataset encompassing a total of 354M tweets by 26M users. This dataset can be considered comprehensive (see Materials and methods 4.1). However, a limitation of this work is that a substantial part of the network consists of the supercommunity labelled as "Other", encompassing around 50% of all users. It is difficult to make general statements about the true nature of this group, as it includes a very diverse set of users, with most of them reporting unspecific profile information. As our work is based on the self-reported expert status of users, future research is required to properly understand their true nature.
Our work leads to two main conclusions: 1) Under the unique circumstance of an emerging virus causing a global pandemic, the Twitter platform allowed thousands of international experts to quickly and efficiently exchange information, while also being amplified by non-expert communities. 2) As the pandemic developed, Twitter users directed more of their attention towards the national debates, overall leading to more segregated communities.
As the world faces a range of societal and economical issues related to the COVID-19 pandemic, there is a growing need for a coherent communication strategy by trusted sources in order to combat misinformation. In light of our results, it is challenging to envision a strategy which is globally applicable. However, our work informs the development of temporally and locally adaptive communication strategies, which may involve the inclusion of key influential figures in order to reach the niches of the increasingly segregated network of the COVID-19 debate on Twitter.
Twitter data was collected through the Twitter API, specifically through the filter streaming endpoint, using the Crowdbreaks platform [28] . The data used in this work consists of a total of 353,993,900 tweets (thereof 267,026,740 retweets) posted by 26,262,332 users in a 146 day observation period, i.e. from January 13 to June 7, 2020. These tweets have been identified by Twitter to be in English language and match one or more of the keywords "wuhan", "ncov", "coronavirus", "covid" and "sars-cov-2". Note that keywords have changed over time, as the new names for virus were introduced (for details refer to SI 1.1 and Table S2 ). The data is complete with respect to these keywords, except during a period between mid-March to mid-April when volume exceeded the 1% threshold imposed by Twitter and was subsampled by an (unknown) degree.
In order to be able to interpret the identified network communities, accounts were categorized by occupational role and account type. This categorization was conducted using a Machine Learning classifier which was trained to determine the category of an account solely based on the user description (user bio). The classifiers were first published in the context of related work on the attention given to experts during COVID-19 [20] . In this work, we use the published English language BERT model (bert-english-pt) in order to determine the category of each account in our dataset. In the aforementioned study [20] , the categories have been determined in an iterative coding process to best categorize users into 13 categories, which are: Adult content, Arts & Entertainment, Business, Healthcare, Media, Non-governmental organization (NGO), Political Supporter, Government & Politics, Public Services, Religion, Science, Sports, and Other. For further details on the coding process or the training of the machine learning classifiers, please refer to the referenced study [20] .
Tweet objects contain both structured and unstructured forms of geographical information. In this work, we employed a procedure to geo-localize tweets on the country level using the Python library local-geocode ( 3 , please refer to SI 1.2 for detailed explanations). A user's country location was determined from the majority of the user's geo-localized tweets. Geolocation could be inferred for 75% of tweets belonging to 54% of the considered users.
We study the full directed retweet network, consisting of all retweets collected during the entire 147 day observation period (267M retweets). The nodes of the network represent users who have at least once retweeted or have been retweeted by another user. An edge was established from user A to user B if B retweeted A at least once during the whole period of data collection. Therefore, the edge direction indicates the flow of information. We assigned a weight to this edge equal to the number of times user B has retweeted user A. The reconstructed (weighted and directed) network has 22.9M nodes and 177M unique edges. In order to study the communities in this network, we consider only the largest connected component of the network, consisting of 22.5M nodes and 176M unique edges. The discarded components only consisted of isolated nodes or duplets, making up 1.57% of the nodes and 0.13% of the edges in the original network. We ran Louvain's community detection algorithm [29] , as implemented in Python's package Networkit [30] . The algorithm attempts to detect clusters of nodes by recursively maximizing the network's modularity. Standard modularity was adopted as a scoring function, meaning that intra-cluster edges were counted with the same weight as inter-clusters edges. For the community detection task, we considered the network edges as undirected in order to reduce the computational burden. Due to the stochasticity of the clustering algorithm, we ran 50 trials and assigned each node to the community it was most frequently associated with. The identification of largest communities was found to be stable both among repeated runs of the algorithm ( Figure S6 ) as well as when comparing monthly time windows of the dataset ( Figure  S7 , please refer to SI 1.3 for further details). Thus, the results of the community detection can be considered as fairly robust. The coordinates of the network layout in Figure 1 were processed by Gephi software [31] , using the ForceAtlas2 algorithm [32] with gravity set to 0.05 with the "stronger gravity" option enabled.
Data availability. All data and code can be found on our public GitHub repository https://github.com/ FraDurazzi/twitter-network-covid19. The full Twitter dataset used in this work is available on Zenodo [33] . Competing interests. The authors declare no competing interests.
International expert communities on Twitter become more 1 Supplementary text
Collection started on January 13, 2020 a few weeks after first reports about a disease outbreak in Wuhan, China surfaced. Throughout the collection period, the keywords were changed in order to accommodate for the various ways the virus was referred to (see table 2 ). Initially the virus was referred to as "wuhan virus" and later as 2019-nCoV (2019 novel coronavirus). On February 11 the ICTV (International Committee on Taxonomy of Viruses) changed the official name to sars-cov-2 and COVID-19, for the virus and the disease respectively. Due to the high volume of data small interruptions occurred during data collection when no data was collected. Four interruptions were for longer than one hour, the longest being 9 hours on April 11.
In order to geo-localize a tweet the following procedure was performed:
1. Geo coordinates (0.1% of original tweets in dataset): Tweet contains coordinates (longitude and latitude) information.
2. Place (2.9%): Users can tag a tweet with a named place. Tweets with place indication contain structured geo information, including a geographical bounding box.
3. Parsable user location (61.9%): We use the Python library local-geocode 1 in order to parse the user location field. This field contains unstructured text and may reference one or multiple places and/or countries. It also sometimes contains humorous or imaginary places (e.g. "the end of the universe). The local-geocode library makes use of the geonames database and performs substring matching against place names in this database in order to obtain structured geographical information (also known as geocoding). In the matching, only places with a population larger than 30k are considered. local-geocode has been compared against geopy 2 (using the Nominatim library), which is frequently used for this task. Visual inspections of the country-level disagreements between both tools, indicate that local-geocode generally performs better in this task. This is likely due to the fact that local-geocode only considers relatively well known places, therefore ignoring imaginary names whereas geopy attempts to provide a (wrong) result in these cases. However, human-level benchmarking would need to be conducted in order to come to a final conclusion on the performance of both tools for Twitter user location decoding.
Community detection. We applied Louvain's community detection algorithm (implemented in Python's Networkit package, PLM function), setting the default resolution parameter γ = 1. Since each run of the algorithm produces different results, we run the algorithm for 50 trials and assigned each user to the community it was mostly found into. On average, about 15 communities reached a size larger than 10 5 (15.42 ± 0.09) (see Supplementary Fig. 2) . In order to assign each user to a community, we counted how many times each node appeared in the same community along the 50 trials (the same community was hypothesized to be that of maximal overlap within all trials). The ratio of times each node was found in the same community was used as a 0-1 score ("community score") about goodness of identification of the community associated to each node ( Supplementary Fig. 6 ). Furthermore, we analyzed the overlap of user IDs in communities obtained from the full network and from networks reconstructed with data aggregated per month. Retweets posted during January and February were lower than the rest of observational period, so we joined the two months into a single time-window. This means that four temporary networks were built aggregating the retweets sent during January-February, March, April, May 2020 separately. A fair stability over time was observed overall (see Supplementary Fig. 7) . Temporal stability was highest for the largest communities (labelled from A to H), having an average overlap of 72% (min 44%, max 94%) with the most overlapping temporary communities. Also smaller communities, in particular L, M, and J, showed a fair temporal stability (avg. overlap 57%, min 20%, max 89%).
Top users characterization. The network's communities are composed by users with different roles and centrality inside the network. For a finer characterization of the authorities of this retweet network, we selected as top users the 1000 most retweeted users for each super-community. We computed well-known centrality measures, with the Python's package Networkit, and show their correlation in Supplementary Figure 9 . The node betweenness centrality for each user in the network was estimated considering the shortest paths between 100k randomly sampled nodes. Correlation between centrality measures do not display different patterns for different super-communities. Out-degree and in-degree have the meaning of the number of retweets respectively received and sent. The distribution of received retweets is centered on the highest value for the Political super-community, meaning more attention received. Clustering coefficient is centered on the lowest value for Other, meaning a sparser and less modular community. Figure 11 : Heatmap of community locations. Each user was assigned to the country code mostly represented in its tweets. Only country codes represented at least by 5% in a community are displayed. 