2.1 Limitations

Twitter is not only the social media of choice for politicians, but also the social media data source non plus ultra for social scientific research [11]. There has been a veritable explosion of research on Twitter in general and Twitter politics in particular (e.g. [12–17]). The Web of Knowledge database contained 10,653 articles with “Twitter” as a key word (as per 31st October 2019), with 4,112 papers produced between January 1, 2018—October 31, 2019. The combination of key words “Twitter” and “politics” finds 640 articles, with 279 published in 2018-2019—roughly one article about Twitter and politics every 3 days. Despite this large and growing body of research, there remain a number of fundamental issues with carrying out research on Twitter data, in particular in terms of delineation, sampling, and validation.

It has long been established that the delineation of the population are poorly addressed in many studies examining politics on Twitter [6, 18]. Researchers often assume that the relevant population consists of Twitter users who index their tweets with specific hashtags. However, there are some serious issues to consider. One is that Twitter users participating in debates do not necessarily use hashtags associated with a particular issue and Twitter users who do use those hashtags do not always participate in the discussion, raising difficult questions of how to decide whether a message is relevant or merely “noise” [19]. Moreover, we know that Twitter users with different political positions tend to use different hashtags [20]. While careful curation of hashtags can attenuate these problems, it cannot solve them; when tweets and users are selected through hashtags, the delineation of the population remains arbitrary to a (generally unknown) extent. When the population is arbitrarily defined, all subsequent analyses can provide evocative results at best. There are however notable attempts at tackling this issue, for instance, Bruns et al. [21] have mapped follower/followee relations to get a more relevant sample of the Australian Twittersphere.

A second issue is associated with the common use of Twitter’s free Streaming API for sampling tweets containing keywords. While research into the Twitter Streaming API is relatively sparse and may not be up-to-date due to constant API changes, the research that has been carried out has had troubling implications for the standard approaches to gathering Twitter data, in particular the use of search word or hashtag queries. For instance, when the free streaming API was compared with paid access to the Firehose API (which reportedly gathers 100% of all tweets), the sample became less representative as the number of parameters requested increased [22–24]. The issue here is not just that samples of selected keywords are not representative but that we do not know how samples are drawn and what their biases are. Other methods of sampling have been attempted by researchers through focusing on a core group of individuals, wherein all tweets can be gathered [16]. However, no attempts to standardize sampling at the scale of the TPD have yet been made.

A third problem with the way that Twitter data is used in research is linked to the lack of contextual or background information. Twitter data tend to be difficult to connect to other data sources, meaning that little is known on the identities and political leanings of the users. Consequently, validation is difficult. However, contextual information is essential if we want to compare between or within different political groups. In response to this issue, researchers often attempt to infer political viewpoints from behaviors such as a follower or friend network, and hashtag use [25–28]. For instance, if people use, or are listed as, #tcot (i.e., top conservatives on Twitter), it is assumed that they are conservatives. In this case, such a strategy may result in many false positives, but will also only identify a small subset of conservative users. Also, the political viewpoints garnered through these methods may not be representative of the wide variety of political attitudes that exist, where someone may share conservative beliefs in some points, but not in others [29, 30]. The issue of misidentifying users’ political leanings is aggravated by the presence of sock puppets, trolls, and bots (e.g. [19]). Moreover, inferring political views from behavior brings researchers into a legal and ethical grey zone since Twitter regulations forbid the algorithmic identification of users’ political viewpoints. In response to these challenges, researchers have developed more sophisticated methods (e.g. [30–33]) to identify political viewpoints, including linking Twitter data with survey data [30, 34]. In sum, there have been some interesting ways that researchers have tried to circumvent the methodological and technical challenges posed in classifying political viewpoints. We offer a different solution by sampling parliamentarians, wherein party affiliations are known, as well as draw upon existing databases for supplementary information.

2.2 Possibilities

If we want to exploit the opportunities for comparative and transnational research into politics that Twitter offers, the issues of delineation, sampling and validation have to be resolved. One way forward, which is the method employed by the database at hand, is to not define populations according to the content of their tweets but to construct a panel of Twitter users whose tweets are collected over time (e.g. [35, 36]). This approach has been taken in a number of studies that have focused on accounts of the United States’ Congress, for which reasonably reliable lists are available on Twitter (see, e.g. [37–39]). Some researchers have begun to study Twitter parliamentarians outside of the United States but often consider only one or at most two countries (e.g. [16, 40, 41]), often in the specific context of election campaigns [12, 42–45]. Thus, while there is considerable research focusing on Twitter use by parliamentarians, to our knowledge, there is no research that includes a large number of countries or that connects to existing databases on parties and parliaments. Hence, the database provides new opportunities for comparative research between multiple countries by making data available that has been gathered over several years from a clearly delineated population.

Focusing on parliamentarians limits the scope of research, but the advantage is that the TPD attempts to resolve several important issues regarding the delineation of the population and sampling in a way that is straightforward, transparent, and verifiable. An additional advantage is that parliamentarians are public figures, which significantly reduces ethical issues regarding privacy protection and increases possibilities to match data obtained from Twitter with data from other sources. In an effort to capitalize on these advantages, the database presented in this article extends earlier work by: including a much greater number of countries; complementing Twitter data with data drawn from other sources; and developing elaborate procedures to maintain data accuracy. The TPD data enable a broad range of Twitter research (e.g. [12, 46–50]). While previous research focused on a limited number of cases or had to rely on convenience samples (e.g. [25, 31, 49]), the TPD allows for studying these topics at scale and in comparative and transnational perspective. As such, we present a variety of analyses to demonstrate the different functionalities of the TPD.

Several research domains can reached by the TPD through linking it to other databases, such as; the Electoral System Design Database (ESDD), the Manifesto Project, the Chapel Hill Expert Survey (CHES) and ParlGov—meaning that the TPD can extend existing comparative work to Twitter, to examine differences in for example the ways parliamentarians express themselves and engage with one another, within nations, within parties as well as transnationally. To illustrate the TPD’s potential, we focus on three domains of comparative research as a demonstration of how the database could begin to answer questions in these areas.

We use several methodologies to illustrate the TPD’s potential for cross-national and comparative research. First, we can use the TPD to investigate national differences in the way parliamentarians use Twitter. Political science literature has a long-standing tradition of comparing political culture and political debate among various nations [51–55], showing important differences across countries and different types of electoral and political systems. We use this literature as a way both of showing how the structure of national retweet networks can shed new light on Lijphart’s classic ideas [51] on the relationship between democracy types and national political communication. Second, the TPD allows for comparison of hashtag use between different parties. Twitter provides textual data that captures the way parliamentarians express themselves and frame political issues. Hence, the TPD constitutes a powerful tool for studying subsets of political party discourse. Despite the apparent potential, there has been limited research on content of parliamentarians’ tweets, and none that are on a large international scale [56]. Therefore, the TPD offers the opportunity to contribute to comparative work on discursive conflicts [57, 58] that so far had to rely on newspaper data that cover only a very small portion of political claims [59]. A third domain in which the TPD can be used is transnational communication. Since social media are often regarded as conduits for breaking geographic boundaries [60], there is a need for systematic analysis of communication flows between countries. Since the TPD not only records interactions within but also between countries, it allows for the examination of the prevalence, nature, sources, and topics of international communication networks on Twitter.

2.3 Limitations

The limitations of exclusively following parliamentarians on Twitter should be acknowledged. First, as mentioned, the focus on parliamentarians limits the scope of the research, since parliamentarian tweets only constitute a subset of political discussions on Twitter. This limits usefulness in relation to, for instance, campaigning research, as the TPD only gathers data on parliamentarians who are already in office. It should also be noted that in some cases, parliamentarians may choose not to individually interact with constituents but rather present themselves through the party account. Further, the timeliness of updating the database following elections is largely dependent on the updating of official government websites, which may not occur in some countries until coalitions are formed. In rare cases, this may be several months. While the TPD has a limited scope outside of incumbent parliamentarian communication, it may serve as a starting point for questions about other political debates, by using various techniques to expand from parliamentarian users to other parts of Twitter.

4.1 Comparing countries: How do politicians use Twitter?

We begin by looking at the similarities and differences between countries in terms of Twitter use: What are the differences between countries in parliamentarian Twitter interactions? While Twitter offers the same functionalities to parliamentarians everywhere, how those functionalities are used varies significantly between countries. These differences may point to differences in political cooperation across countries. Firstly, the percentage of parliamentarians that actively use Twitter differs. Some countries may have an extremely high active Twitter base (99%, United States) where most parliamentarians tweet almost daily, whereas in other countries, the parliamentarians may have Twitter accounts, but they rarely tweet. However, on average 80% of parliamentarians per country are active on Twitter. The frequency of Twitter use varies across countries. On average, parliamentarians tweet 2.8 times per day, although there is some deviation; parliamentarians in Iceland tweet less than once per day, whereas parliamentarians in Turkey tweet 6 times per day (min = 0.7;max = 6.6;σ = 1.4).

Retweeting and mentioning exclusively between national parliamentarians make up an average of only 21% of total politician Twitter activity (min = 7%, max = 36%), with Poland having the highest proportion of Twitter activity between their parliamentarians, and Iceland with the lowest. Further, as we see in Fig 2, while mentions to other parliamentarians tend to be used much more than retweeting, there are stark differences between countries: Icelandic parliamentarians very rarely retweet, while Greek, Turkish and Canadian parliamentarians have more retweets than mentions. Conversely, we see the opposite pattern when looking at retweets and mentions to non-parliamentarians, as parliamentarians retweet more than mention when communicating with those outside of parliament.

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Fig 2. Ratio of retweets & mentions.

Retweets and mentions per country as a percentage of total tweets for all countries in the database.

https://doi.org/10.1371/journal.pone.0237073.g002

The data on retweets and mentions can however be employed in much more powerful ways, providing new data to questions that have long been central to political science. For instance, a large literature in political science focuses on international comparison of politician cooperation, and the relation to aspects of the countries’ democratic systems. This literature follows not least from Lijphart’s [51] suggestion that consensus democracies, usually employing proportional representation systems, leads to “kinder, gentler” political cultures than majoritarian systems (see also e.g. [63–65]). The authors postulate that the structures of power distribution represented by the democratic system of a country (e.g. majoritarian or consensus) may encourage attitude polarization (and in turn elite conflict), or instead, foster cooperation between politically dissimilar parties [51, 52, 66, 67]. With this reasoning, it is thought that proportional systems lead to increased cooperation [51]. Conversely, other scholars argue that due to lower barriers of entry for smaller, single-issue parties, political fragmentation is instead increased in proportional systems [66–69].

Academic work on the coalitions and divisions within parliamentarian communication networks has been limited by lack of suitable empirical data. The TPD can thus provide a useful data source for studying the coalitions and divisions among parliamentarians. By viewing retweets as endorsements (c.f. [70, 71]), the patterns of retweeting can be revealing of the political alliances within a country. Retweets can be treated as edges in a network, wherein the structure can reveal coalitions and divisions amongst parliamentarians. Such networks can be analysed in different ways, capturing different aspects of the structure of endorsements within a country. For this demonstration, we focus on the networks with 30 or more parliamentarians, and filter the networks by their giant component. We here aim only for a tentative exploration, leaving an in-depth and rigorous analysis for future research.

A simple but powerful way of operationalizing the level of cohesion within a country is to compare the fraction of retweets made to members of external parties. Countries whose parliamentarians frequently use retweets to endorse members of other parties can be assumed to have more amicable between-party relations compared to countries whose parties mostly retweet internally. S2 Table documents the electoral system, and the average fraction of external retweets per country. It shows that there are clear differences between countries in terms of the fraction of external retweets. Although these patterns need further exploration, we see that majoritarian (M) systems tend to have a lower fraction of external retweets compared to countries with mixed and proportional representation (PR) systems. PR systems on the other hand, show wide variation in the average fraction of external retweets. Countries with a high fraction of external RTs all employ PR systems.

However, this quantitative approach has certain limitations, as it fails to capture, for instance, divisions involving multiple parties in coalitions or situations in which a fraction of a party is strongly divided from other parties. As there are many forms that divisions can take in these networks, we turn to a more qualitative approach to network analysis: Visual Network Analysis [72]. This highly flexible approach allows us to categorize the endorsement networks according to their structure, identifying various forms of divisions and alliances within a country.

To look beyond descriptive metrics for examining the differences in parliamentarian twitter interactions, for this preliminary demonstration we focus on the networks with 30 or more nodes and filter the networks by their giant component. We plot the networks using the ForceAtlas2 visualization algorithm, which uses a number of properties to structure the networks in such a way that highly connected nodes tend to be closer to each other, and less connected nodes further away from each other [73]. In Fig 3, the nodes are colored according to their party.

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Fig 3. Parliamentarian retweet network archetypes.

Shows the 4 distinct types of retweet networks. Each node represents an individual parliamentarian. The networks are plotted with the ForceAtlas2 visualization algorithm which plots highly connected nodes close to one another, and less connected nodes further away. The nodes are colored by party.

https://doi.org/10.1371/journal.pone.0237073.g003

Using this qualitative method identify four distinct types of political network structures (see Fig 3). Type 1 networks show a highly divided structure: there are clear divisions between clusters and very few cross-cutting ties. Type 2 networks show two large clusters that have dense connections amongst themselves and fewer external ties. Type 3 networks show a large, densely connected structure with one or two outlying parties, which are weakly connected to the others. A clear example of this is the Netherlands, where the outsider is radical-right party PVV and Germany, where the outsider is the radical-right AfD, which entered parliament in the 2017 election. Lastly, Type 4 structures exhibit one large cluster of dense connections. All the individual retweet networks and their classifications can be found in S1 Fig.

We can furthermore use additional measures to compare the categories of networks identified using VNA, to demonstrate that the networks are not only visually distinct. S2 Table also reports the modularity, number of clusters, and average clustering coefficient per country to provide measures of clustering within the networks. To confirm the stability of the modularity measure, we used the leidenalg python library to measure each network 1,000 times. We report the average modularity from these runs, as well as the most frequent number of clusters per run. Cluster assignment was based on the most frequent cluster assigned per node across the 1,000 runs. The results indicate that type 1 networks tend to have higher modularity, as well as a greater number of clusters than other networks. Type 3 networks tend to have fewer clusters than the other network types. Type 1 and 2 networks tend to have higher clustering coefficients whereas type 4 networks have lower clustering coefficients. This would indicate that type 1 networks tend be more divided than types 3 and 4. The fraction of external retweets also shows that parties in type 1 and 2 networks retweet other parties much less than those in types 3 and 4. Additionally we see that most type 3 networks tend have negative kurtosis of their degree distributions, which implies that members in the network retweet each other to a similar degree, rather than rallying around a few leaders. Finally, we see that type 1 networks tend to have lower normalized eigencentrality scores, implying that nodes are less well-connected within the network.

We also look at the relationship between cluster and party membership through χ² and Cramer’s V measures, wherein Cramer’s V show the strength of that relationship, thereby indicating how ‘neatly’ the networks cluster based on party: fragemented networks are expected to have a stronger relationship between party-cluster membership, whereas a weak relationship would indicate more overall cohesion in the network. For brevity, we only report the Cramer’s V value in S2 Table, as all χ² results indicated a significant relationship between party and cluster membership (p < 0.00). We see that type 1 networks have much stronger relationships between party and cluster membership than type 4 networks. Therefore, it is clear that type 4 networks generally retweet across party lines.

All in all, visual analysis and basic network measures can be combined to interpret the types of network structures that emerge from the data. The measures provided are by no means exhaustive and may differ depending on the research question. Using the information from the ESDD [8], we can explore differences in parliamentarian endorsement not only between countries, but between democratic systems. We see that Type 1 networks show a divided network with little endorsement between parties. Type 2 networks appear bipolar, with two large contending groups, and are the most common category for Majoritarian systems. Lastly, types 3 and 4 are more consensual, due to many cross-cutting endorsements beyond party lines, and are largely comprised of PR systems. These results are tentative and not exhaustive. However, they do illustrate that the database offers opportunities to compare coalitions and divisions between different countries and political systems. While our purpose here is to illustrate the potential of the dataset for studying cross-national differences through exploratory analysis, it also includes data that allows for more formal and quantitative measurement, and points to several fruitful directions for future research.

4.2 Comparing language between parties: Examining Hashtags

Textual data from Twitter provides an avenue into seeing how parliamentarians navigate different issues and engage in discursive conflict. Despite this potential, there has been limited research on content of parliamentarians’ tweets, and none that has focused on international comparison [56]. The TPD offers the opportunity to contribute to comparative work on discursive conflicts [57, 58] that so far had to rely on newspaper data that typically do not provide a comprehensive coverage of political claims [59] and is time-consuming to collect and process [74]. Since the TPD furthermore links to the Manifesto Project, Chapel Hill, and ParlGov databases, it has the information necessary for connecting such textual analysis to a large body of work that compares different political parties and party families [7, 75–77]. In this section, we explore the possibilities for using the TPD to take a comparative approach to the study of political discourse, focusing on the differences in the use of hashtags between political parties.

The use of labels in political discourse reveal the different ways that opposing political parties discuss the same issues. This is apparent through brief exposure to political debate: while one party may speak of “tax reform,” the other focuses on “tax relief.” Using different labels for the same issue indicates how central labelling is to politics [78]. The way an issue is labelled influences how we view that issue, what issues we see in the first place and enable us to make sense of what we are reading. Using certain labels over others may lead to exacerbating political divides: when every group identifies and labels its own issues in its own way, it makes conversations across partisan lines more difficult [79].

Analyzing labels used on Twitter is made easier by Twitter providing affordances for explicitly labeling tweets, in the form of hashtags. While hashtags serve many purposes, broadly speaking they can be used to index conversations [80], convey a particular point of view [48], or for issue positioning and labelling [25, 26, 48, 81]. Importantly, hashtags aid in building public perceptions of an issue by ensuring maximum visibility, and allow anyone to jump into the conversation [82]. In order to demonstrate the database as a tool for comparing issue labelling between parties, we therefore we look at hashtags, using the guiding question: how do political parties differ in their use of hashtags and what is their common ground?

To take a first step toward a comparison of how different parties label issues, we develop a simple computational method that captures which hashtags are partisan and which are shared. To study this, we look at the two largest parties in all countries, in terms of the number of active users on Twitter (note that these are not necessarily the parties that have the most seats in parliament). We count each hashtag used by these parliamentarians and then normalize according to the total number of hashtags used by the party. If a hashtag is used by both parties, the overlapping part of the use is seen as the intersection between the parties. As in a Venn diagram, the intersection of the sets are the common hashtags, and the non-intersecting parts are hashtags characteristic of the party. The relative size of the intersection thereby gives a measure of the similarity in hashtag use between the two parties, which corresponds to the Jaccard similarity measure [83]. Since dynamics of discursive conflicts are likely different in multiparty systems, we here focus on the majoritarian systems categorized by the ESDD: the UK, USA, Canada and Australia. The results are shown in Fig 4. While the set-based method used here is intuitive and therefore useful for exploratory analysis, a common statistical way of comparing corpora for the most overrepresented words is Log-Likelihood [84]. This analysis can be found in S3 Table, and largely matches the results in Fig 4.

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Fig 4. Hashtag use amongst the 2 largest parties.

This figure shows partisan and common hashtags in Australia, the United States, Canada, and the United Kingdom. The middle panel shows the common ground while the side panels show the distinctive hashtags for two largest parties. The size of the middle panel is proportional to the size of the common ground relative to the parties. In the word clouds, the size of words is proportional to the frequency of use as a fraction of total hashtag use.

https://doi.org/10.1371/journal.pone.0237073.g004

A first striking takeaway from this Fig 4 is that the common ground hashtags represent only a small fraction, where the intersection is 15.7% (in the United Kingdom) or less. Most of the hashtags that are equally represented in each party (proportional to the number of total hashtags used by that party), are usually smaller, less commonly used tags. This indicates that hashtags are largely partisan. Hence, to understand what these fractions mean, we need to look closer at the way hashtags are employed by politicians.

A first thing to note is that the most common intersecting hashtags for Canada and Australia are “cdnpoli” and “auspol” respectively, although they are not used with the same relative frequency by each party. These are general country-level hashtags for marking the nation which the discussion concerns, and are broadly employed for political debate also outside of professional politicians (these hashtags have been broadly studied, e.g. [24, 85, 86]). The UK and the US do not seem to have any corresponding hashtags, which may be due to the US and UK being large enough to be the norm on the platforms, while Canadian parliamentarians need to demarcate that they are speaking of Canadian politics.

We focus here on the US for a deeper look into the ways parties make use of hashtags, and the relation to how political issues are labelled. Looking at Fig 4, we see that politicians from both parties address many of the same issues, while using very different labels. The 2018 tax reform is a major talking-point on both sides, but while Republicans refer to it using “taxreform” or “taxcutsandjobsact,” Democrats instead use “goptaxscam”. Interestingly, there is no common ground hashtag to denote the bill. A similar state of affairs can be identified in relation to the 2018 government shutdown, which both parties attempt to attribute to the other party: Democrats refer to it as the “trumpshutdown,” while Republican use “schumershutdown”. An interesting point of note is that Democrats use the hashtag “trump,” while this is not among the major hashtags for Republicans. Republicans focus on “Venezuela,” as an example of the putative dangers of left-wing politics, while Democrats speak of “climatechange,” an issue that is not featured among top republican talking-points.

The most important intersecting hashtags found in Fig 4 include tags which describe arenas of contention rather than specific topics of disagreement, for instance, “scotus,” referring to the Supreme Court, in which highly a contested process of electing new judges played out during 2018. Another example of this is “farmbill”, referring to the primary agricultural and food policy bill of the US government, which is renewed every 5 years and deals with affairs under the purview of US Department of Agriculture. While there may be disagreements about its contents, the common use hashtag suggests that representatives from both parties at least agree they are discussing the same issue.

Other important intersecting hashtags point to common ground values, to which both parties are happy to profess their support. These include “veterans,” “neverforget,” and “stem”. Similar common ground is found in national emergencies, such as the hurricane striking Puerto Rico. However, even here the language differs somewhat between the parties: Republicans are more likely to refer to the event as “hurricaneirma”, while Democrats speak of its impact on “PuertoRico”. DACA (Deferred Action for Childhood Arrivals)—an Obama-era executive action, now turned Trump-vetoed bill with broad bipartisan support among voters—is an important hashtag in the intersection, but is driven in particular by Democratic politicians, who also employ the related hashtags “dreamact”, “dreamers”, “protectdreamers”. There is, perhaps somewhat tellingly, broad bipartisan use of the hashtag “mepolitics,” denoting a criticism of the country’s polarized political discourse.

As we have demonstrated, we see that politicians use hashtags to express partisan claims. While there is some common ground (for instance when it comes to the importance of caring for veterans), politicians generally use hashtags in outspokenly partisan ways: they use specific hashtags to push different kinds of issues on the agenda or to express a partisan take on the same issue. This case has served to illustrate the range of possible research opened by the TPD in examining language and discourse of politicians on Twitter.

4.3 Transnational communication: What is the structure of the transnational mention network?

The TPD not only allows for cross-national comparative research but also for research on international and transnational politics, by enabling analysis of how parliamentarians of different countries communicate with one another [87–89]. Through facilitating communication and mobilization of opinion across borders [90], social media platforms open up for the possibility of a cross-national dialogue. The TPD allows us to examine systematically and comprehensively whether parliamentarians are employing the affordances of the platform to engage in transnational debate. To demonstrate, we look at all mentions between politicians in the European Free Trade Association (EFTA). When a parliamentarian uses the Twitter mention functionality, it refers to another Twitter user and notifies the user that they have been tweeted about. Mentions in this case are tweets in which another parliamentarian’s Twitter account is signalled with the ‘@’ symbol. This does not include direct retweets, but it does include retweets where additional text has been added. Mentions were chosen over retweets as they are more indicative of a dialogue or debate (rather than an endorsement), as well as to provide illustration of analysis of Twitter’s affordances beyond retweets in the TPD. Luxembourg and Slovenia were excluded from the analysis due to their low number of international mentions. Moreover, since we are interested in communication between national parliamentarians, we do not include the European Parliament in this analysis (see [91–93] for studies on the European Parliament). As illustrative questions, we ask; what is the structure of interaction between parliamentarians in the transnational debate?

We begin by examining the network of mentions between individual parliamentarians, filtered by its giant component (see Fig 5). In most countries (with the exception of Ireland and Spain), less than 55% of parliamentarians have mentioned a parliamentarian from another EFTA country. S4 Table contains the proportion of parliamentarians that made external retweets per country. The extent to which the international network is split along national lines is striking: the parliamentarians are organized in clusters that almost perfectly cut along national lines, with only a small number of parliamentarians being located outside their respective national cluster. Moreover, when there are connections between national clusters, they tend to occur between neighboring countries, with nations like Ireland and UK situated next to one other in the network.

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Fig 5. International mention network.

This network shows all mentions between parliamentarians in the database. The network is made in Gephi, using the ForceAtlas2 algorithm. Node colors are set according to country.

https://doi.org/10.1371/journal.pone.0237073.g005

To arrive to a more precise measure for quantifying the level of external communication between countries, we look at the fraction of external tweets, network diameter, average path length and modularity. The modularity indicates the strength of the division of the network. The network diameter measures the longest distance between any two nodes in the network, whereas average path length counts the average graph distance between all pairs of nodes. These measures help to understand the structural connectivity amongst individual national parliamentarians on a European level. Aggregating on country level, we examine centrality with PageRank. The network diameter is 18 and the average path length is 5.8, indicating that there is quite some distance between distant nodes in this network. The network has a modularity of 0.678, suggesting that the network is relatively divisible into separate clusters. All these numbers suggest that parliamentarians communicate mostly with their compatriots, resulting in a very sparse and highly divided international communication network.

Although international communication accounts for a low proportion of the total communication (1.9% of all mentions are directed to a parliamentarian in a different country), in absolute terms it is still substantial cross-national traffic (16,955 total mentions). Looking closer at these mentions gives insight in patterns of communication across EFTA countries. We examine the fraction of external mentions per country, to determine the proportion of mentions that are dedicated to international debate [94]. When looking at fractions of external mentions, we see in Fig 6 that some countries participate minimally in international debate, through a low fraction of incoming and outgoing international mentions. Moreover, we see that the majority of all international mentions are directed towards a small number of countries, namely the United Kingdom and Germany, and to a lesser extent, France. Additionally, we see that there is a large imbalance in outgoing and incoming mentions for some smaller parliaments such as Austria and Iceland. Small countries appear relatively outward facing, as there are simply fewer parliamentarians to mention within these small countries, and more external parliamentarians, resulting in a higher fraction of external mentions. Moreover, it is noted that smaller countries tend to be more globalized than larger countries [95].

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Fig 6. Incoming and outgoing mentions.

Shows the normalized fraction of outgoing and incoming mentions. The fraction of incoming mentions shows which countries are more central to the debate whereas the outgoing mentions shows how much mention activity per country is directed to international debate.

https://doi.org/10.1371/journal.pone.0237073.g006

To further examine the extent of the transnational debate, Figs 7 and 8 show that the international debate is uneven, being dominated by Germany and United Kingdom. To check if countries contribute equally to the international debate, we used an independent samples Kruskall-Wallis test. We compared the relationship between normalised cross-border tweets and country. The results indicate that some countries have significantly more external ties than others (p = 0.000). This implies that some countries participate more than others in the transnational debate. For a more precise estimate of the relative importance of different countries, we calculate PageRank centrality on the adjacency matrix of the aggregated connections between countries. The results are shown in Fig 9. The figure shows that while France has a large and active parliament, they are relatively peripheral in the international debate compared to Sweden, Denmark and the Netherlands—all whom are significantly smaller in parliamentary size but higher in centrality. The sheer volume of mentions emitted by UK parliamentarians—gives it a central place in the transnational debate as shown by its PageRank. Germany, by contrast, has significantly fewer external mentions but are are just as central in terms of PageRank and relatively more connected, which is also shown by their fraction of incoming external mentions. Thus, Germany also occupies a central position in European debates.

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Fig 7. Incoming and outgoing mentions.

Shows the fraction of outgoing and incoming mentions per country. It includes only international mentions, thereby showing the importance of each country in the international debate.

https://doi.org/10.1371/journal.pone.0237073.g007

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Fig 8. Absolute and normalized adjacency matrices.

Shows absolute (left) and normalized (right) adjacency matrix for the mention network. (Note that the color scheme is logarithmic).

https://doi.org/10.1371/journal.pone.0237073.g008

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Fig 9. PageRank centrality per country.

To investigate which countries are most central in the international debate in Europe, this figure shows the PageRank centrality of the aggregated network.

https://doi.org/10.1371/journal.pone.0237073.g009

In summary, the results of our demonstration suggest that debates among national parliamentarians remain, by and large, contained within national boundaries. The TPD not only makes it possible to study the degree of transnational communication, but also to examine the position of countries, parties, or individual parliamentarians within that debate and the role of different issues. While our exploratory analysis has shown which countries are more central to transnational debates within Europe, the TPD makes it possible to study transnational politics on Twitter at a grater scale and in more detail than data sources have previously allowed for.