# Bridging Nations:
Quantifying the Role of Multilinguals in Communication on Social Media

Julia Mendelsohn,1 Sayan Ghosh,2 David Jurgens,1 Ceren Budak1

###### Abstract

Social media enables the rapid spread of many kinds of information, from memes
to social movements. However, little is known about how information crosses
linguistic boundaries. We apply causal inference techniques on the European
Twitter network to quantify multilingual users’ structural role and
communication influence in cross-lingual information exchange. Overall,
multilinguals play an essential role; posting in multiple languages increases
betweenness centrality by 13%, and having a multilingual network neighbor
increases monolinguals’ odds of sharing domains and hashtags from another
language 16-fold and 4-fold, respectively. We further show that multilinguals
have a greater impact on diffusing information less accessible to their
monolingual compatriots, such as information from far-away countries and
content about regional politics, nascent social movements, and job
opportunities. By highlighting information exchange across borders, this work
sheds light on a crucial component of how information and ideas spread around
the world.

##  1 Introduction

Social media facilitates worldwide diffusion of many forms of content, such as
pop culture memes, protest movements, and misinformation (Bruns, Highfield,
and Burgess 2013; Nissenbaum and Shifman 2018; Bridgman et al. 2021). However,
most connections on social media are formed between people who share a common
nationality or language (Ugander et al. 2011; Takhteyev, Gruzd, and Wellman
2012). How can information spread around the world when relatively few ties
cross geographic and linguistic boundaries? Multilingual users are believed to
be an important piece of this puzzle, but understanding how they act as
brokers in information flow across language communities remains underexplored
(Hong, Convertino, and Chi 2011). We carry out a set of causal inference
studies to quantify how multilingual users influence cross-lingual information
exchange across Europe on Twitter and show that the role of multilingual users
varies depending on the relationship between countries and the topic of
content shared.

Figure 1: Consider networks of users from pairs of countries $(C_{x},C_{y})$
(e.g. Germany and Turkey) with dominant languages $L_{x}$ and $L_{y}$ (e.g.
German and Turkish). Here, multilingual user A posts in both languages. We use
causal inference to quantify (i.) the structural role and (ii.) the
communication influence of A in cross-lingual information exchange.

We identify multilinguals based on the languages of their authored posts and
avoid making claims about users’ offline language competencies, which is an
ideologically and theoretically fraught notion (Cheng et al. 2021). Using this
operationalization of multilingualism, prior work has found that multilingual
users tend to have ties (e.g., following links) to multiple distinct language
communities, suggesting that they act as bridges in online social networks
(Eleta and Golbeck 2012; Hale 2014a). We expand upon earlier population-level
descriptive work by using causal inference techniques, namely propensity score
stratification, to more robustly isolate the effects of individual users’
multilingual behaviors on several outcomes. This approach is motivated by
recent work showing propensity score stratification aligns with experimental
results when measuring peer effects in link sharing behavior on social media
(Eckles and Bakshy 2020). We carry out two analyses using this framework.
First, we measure the structural role of multilinguals. Here, we quantify the
extent to which these users act as bridges using betweenness centrality
(Freeman 1978). We then measure multilingual users’ communication influence.
To do so, we ask how having a multilingual contact impacts the content,
specifically URL domains and hashtags, that monolingual users share from other
languages.

By analyzing information spread across country pairs with different dominant
languages, we show how multilinguals’ influence varies based on relationships
between countries. Takhteyev, Gruzd, and Wellman (2012) suggest that offline
country relationships (e.g., economic agreements or migration patterns) impact
transnational tie frequency online. Extending this conjecture, we hypothesize
that the role of multilinguals is not uniform across all country pairs.
Specifically, we expect multilinguals to have a bigger impact on country pairs
that are more geographically distant or have weaker bonds, in which case these
users would serve as gatekeepers of otherwise inaccessible information.

We additionally consider actual content, which affects the rate and shape of
information diffusion (Romero, Meeder, and Kleinberg 2011; Tsur and Rappoport
2012). However, this cannot be captured by analyses of network structure
alone. Our measures of multilinguals’ communication influence overcome this
limitation by enabling us to compare effects across content topics. We again
hypothesize multilingual users play a larger role in spreading content
otherwise inaccessible to an international audience. For example, we expect
multilinguals to have a bigger influence on spreading hashtags about local or
national politics compared to widespread entertainment sensations.

Our contributions are as follows: in large-scale causal inference studies of
European Twitter, we show that multilingually-posting users play a vital
structural role and communication influence on information diffusion across
languages. We compare how multilinguals’ influence varies based on
relationships between countries and find they have a greater effect among
country pairs that are more geographically distant, especially for Western
Europeans who post in Eastern European languages. We further measure how the
effect of multilinguals is driven by content topic and find that they have the
largest influence in spreading content about politics, developing health-
related social movements, and job opportunities.

Understanding how multilinguals affect information diffusion has immense
consequences for online platforms. For example, platforms may focus efforts on
empowering multilingual users to spread information that can support
knowledge-sharing, collaboration, crisis response, social progress, or other
beneficial outcomes (Eleta and Golbeck 2014). On the other hand, they may want
to discourage multilinguals from sharing dangerous content such as
misinformation or abuse.

##  2 Related Work

We draw upon prior work on multilinguals’ online behavior and information
diffusion across social networks.

###  2.1 Social networks and information diffusion

Online social networks tend to have relatively few ties connecting distinct
national and linguistic communities, leading to structural holes (Burt 2004;
Ugander et al. 2011; Hale 2012). Spreading novel information across these
communities thus relies on bridges that span structural holes (Easley and
Kleinberg 2010). Information spreads the most quickly across long-range
bridges, where the intermediary node greatly reduces the shortest path between
two other nodes (Kossinets, Kleinberg, and Watts 2008). Bridges in a social
network play a brokering role that adds to their social capital (Burt 2004).
We posit that multilinguals play an important role in cross-lingual
information exchange because they serve as bridges between language
communities.

Prior work measures how people influence others to propagate information
(Guille et al. 2013). For example, people are more likely to share information
their friends share, and overall, weak ties (e.g., acquaintances) are more
responsible for spreading novel information than strong ties (e.g., close
friends) (Bakshy et al. 2012). Although multilinguals are not necessarily weak
ties, they similarly can act as bridges between communities. Methodologically,
we draw from Eckles and Bakshy (2020), who use causal inference techniques,
namely propensity score stratification, to measure peer influence on link-
sharing behavior and show that their observational estimates are consistent
with experimental results.

Understanding information diffusion and influential brokers impacts research
across disciplines, including the development of activism and protest
movements (González-Bailón et al. 2011; Park, Lim, and Park 2015; Lee and
Murdie 2020), spread of misinformation (Bridgman et al. 2021), product
adoption (Talukdar, Sudhir, and Ainslie 2002), and online abuse (Spörlein and
Schlueter 2021).

###  2.2 The bridging role of multilinguals

Prior work has analyzed connections within and across language communities
online (Hale 2012, 2014b; Samoilenko et al. 2016). Hale (2014b) argues that
multilingual editors on Wikipedia are important for sharing knowledge across
language communities and facilitate access to more diverse knowledge; they are
more active than their monolingual counterparts overall and often write the
same article across multiple language versions. However, Kim et al. (2016)
suggests that language remains a barrier because multilinguals are less
engaged and less likely to edit complex content in a non-primary language.
While online bloggers primarily link to content within the same language,
cross-lingual links do exist, and some bloggers explicitly seek to connect
distinct language communities (Zuckerman 2008; Hale 2012). Authors of blogs
that bridge language communities often tend to be multilingual migrants or
language learners (Herring et al. 2007). On Twitter, multilinguals disseminate
information across different public spheres during events such as the Arab
Spring (Bruns, Highfield, and Burgess 2013).

Earlier work has also examined the structural role of multilinguals. Kim et
al. (2014) count edges between monolingual and multilingual “lingua” groups
within multilingual regions on Twitter. They find that monolingual groups tend
to be connected via multilingual groups, suggesting that multilingual users
are bridges between different language communities. Hale (2014a) similarly
argues that multilinguals collectively play an important bridging role in the
Twitter network. When removing all multilingual nodes, the largest connected
component becomes smaller and the number of small components increases, and
these changes are significantly larger than if the same number of randomly-
selected monolinguals were removed (Hale 2014a). Eleta and Golbeck (2012,
2014) characterize multilingual users’ ego-networks as gatekeepers (language
communities connected by only a few users) and language bridges (tightly-
connected language groups). The authors suggest that gatekeepers are essential
individuals for spreading information across linguistic, national, and
cultural boundaries.

Other work has measured multilinguals’ participation in information cascades
(resharing chains) on Twitter. Jin (2017) find that multilingual behaviors of
the original poster and their followers are predictive of information cascades
crossing languages. Chen et al. (2021) compare monolingual and multilingual
users in cascade trees of COVID-19 information, develop measures that capture
users’ bridging roles based on how much they propagate information, and find
that multilinguals have a bigger bridging role than monolinguals in nearly
two-thirds of information cascades.

We build upon prior work in several important ways. While structural analyses
suggest that multilinguals are important for cross-lingual information
exchange, our causal inference studies quantify the effect of multilingual
behaviors on both structural and content-sharing outcomes while accounting for
possible confounds such as posting frequency and overall popularity. We
further extend the evidence that online multilingual behaviors and connections
vary across countries by identifying systematic variation in the effect of
multilinguality across country pairs (Kim et al. 2014).

Figure 2: General design for causal inference studies (Ho et al. 2007; Stuart
2010). We first estimate the propensity of each unit to receive treatment and
use these scores to divide the sample into 25 strata. We then compare treated
and untreated units within each stratum through a weighted regression to
estimate the causal effect of the treatment.

##  3 Data

We construct an undirected network with Decahose data from 2012-2020, where
edges are mutual “mentions” between users. We consider all pairs of European
countries $(C_{x},C_{y})$ with dominant languages $L_{x}$ and $L_{y}$, and
select countries based on geography and Eurovision participation, a marker of
cultural affiliation. Using location inference (Compton, Jurgens, and Allen
2014), we extract network subsets for all $(C_{x},C_{y})$ pairs where nodes
are users from $C_{x}$ or $C_{y}$. We use tweets from 2018-2020 for
multilingualism and content measures due to limited text availability.

##### Definitions

Each pair of countries $(C_{x},C_{y})$ is a multilingual country pair (MCP)
with dominant languages $L_{x}$ and $L_{y}$. Users who post only in $L_{x}$
are $L_{x}$ monolinguals and users who post in both $L_{x}$ and $L_{y}$ are
$(L_{x},L_{y})$ multilinguals. Within each $(C_{x},C_{y})$ network we
separately measure the role of $(L_{x},L_{y})$ multilinguals located in
$C_{x}$ and $C_{y}$, which we refer to as loci. In Figure 1, (Germany, Turkey)
is an MCP, and we measure the role of (German, Turkish) multilinguals within
each locus Germany and Turkey.

##### Language identification

Twitter posts present a challenge to automated language identification
(LangID) due to their short length, informal style, and lack of ground truth
labels (Graham, Hale, and Gaffney 2014; Williams and Dagli 2017). We use
Twitter’s built-in language detector because it is computationally efficient
for a massive dataset, requires few additional resources, and is trained on
in-domain data.111https://blog.twitter.com/engineering/en_us/a/2015/
evaluating-language-identification-performance

We validate our decision with a comparison to 5 popular LangID packages:
langdetect222https://github.com/shuyo/language-detection, langid.py (Lui and
Baldwin 2012), and CLD2333https://github.com/CLD2Owners/cld2 use probabilistic
models, while fastText (Joulin et al. 2016) and
CLD3444https://github.com/google/cld3 use neural networks. As in prior LangID
evaluations, we randomly sample 1K tweets from 32 countries written in that
country’s dominant language, as labeled by Twitter (Graham, Hale, and Gaffney
2014; Lamprinidis et al. 2021). Like Graham, Hale, and Gaffney (2014), we
calculate intercoder agreement between all pairs of models. Table S3
(Supplemental Material) shows that Twitter’s LangID has a high agreement with
other models, even at higher rates than they agree with each other.

Like most LangID models, Twitter’s LangID has relatively low coverage of the
world’s languages and may struggle to distinguish similar languages (Lui and
Baldwin 2014; Williams and Dagli 2017). We mitigate these limitations by
selecting multilinguals and MCPs so that our analyses are not overly sensitive
to individual prediction errors.

##### Identifying multilingual users

Following Eleta and Golbeck (2014), an individual uses language $L$ if at
least 10% of their posts containing original content are written in $L$ (i.e.,
excluding retweets but including quote tweets and replies). A user is
monolingual if one language passes the 10% threshold, and multilingual in the
$(C_{x},C_{y})$ network if both $L_{x}$ and $L_{y}$ pass this threshold.
Following Kim et al. (2014), we collect language information for all users
with at least five posts in the Decahose. We additionally exclude users if
over 20% of their tweets are in unidentified languages because our
calculations for language use may be less accurate. We set these thresholds so
that estimates of users’ multilingualism are more reliable and robust to
language prediction errors of individual tweets. We emphasize that our
operationalization of multilingualism is based solely on users’ expression on
Twitter; we do not make claims about language knowledge or offline behavior.

##### Selecting network subsets for analysis

From an initial set of 50 European countries, we exclude 18 after three
filtering steps. First, we remove six micro-states with area under 500km2
because Compton, Jurgens, and Allen (2014)’s reported errors suggest that
location inference is less reliable within such small areas. Second, we
restrict our analysis to countries with a single official or dominant language
(i.e., used by at least 70% of the population), removing eight more
countries.555Data for the geographic area and population-level language usage
is from the CIA World Factbook. This step is necessary for our problem
formulation, which focuses on information spread across borders. The study of
users within highly-multilingual countries is beyond the scope of this paper.
Third, we calculate the distribution of tweets authored by users from each
country and exclude four countries where the majority of tweets’ languages are
unidentified or no tweets are identified as written in the country’s dominant
language. See Supplemental Material Table S1 for the list of included and
filtered countries.

We include an MCP $(C_{x},C_{y})$ if: (1) $C_{x}$ and $C_{y}$ have different
dominant languages, and (2) the $(C_{x},C_{y})$ network is sufficiently large
to rigorously estimate causal effects. Specifically, an MCP network is
included if it has at least 100 $L_{x}$ and $L_{y}$ monolinguals, 20
$(L_{x},L_{y})$ multilinguals, and 100 users with at least 1 $(L_{x},L_{y})$
multilingual neighbor.666While we deem such thresholds necessary for precise
causal effect estimation, we acknowledge the arbitrariness of these numbers as
a limitation of our study. See Supplemental Material Table S2 for included
MCPs.

##### Descriptive statistics

Our dataset contains information for 12.6M users from 32 countries. 8.0M users
pass the thresholds for posting activity and language identifiability and are
considered for analysis. Of these users, 1.1 million (13.6%) tweet in both
their country’s dominant language and another European language. Norway has
the largest percentage of bilingual users (46.9%), while the United Kingdom
has the least (3.3%). 232 MCP network subsets were selected based on our
inclusion criteria, covering 30 European countries and 7.7 million unique
users (Georgia and Moldova were considered, but no MCP network containing
these countries was included). MCP networks range in size from 4.7K nodes
(Latvia, Lithuania) to 3.6M nodes (United Kingdom, Turkey), with a median size
of 319K nodes.

##  4 Problem Formulation

Adopting the Neyman-Rubin framework of potential outcomes (Holland 1986), we
isolate the effect of multilingualism by addressing the counterfactual: how
would a multilingual user $u$’s influence be different if $u$ were
monolingual? Each study’s details vary (Table 1), but all have the same idea:
we define a set of units (users), some of whom receive a treatment indicating
multilingual behavior, and we estimate an outcome variable related to
influence on information exchange across languages.

Table 1: Overview of problem formulation for causal inference studies. Each
study focuses on the effect of multilingualism on cross-lingual information
exchange, but defines different samples, treatments, and outcomes.

| Structural Role | Communication Influence
---|---|---
| Study 1 | Study 2 | Study 3
Units |  |  $C_{x}$ users
---
posting in $L_{x}$
| Monolinguals
---
in $L_{x}$ from $C_{x}$
| Monolinguals
---
in $L_{x}$ from $C_{x}$
Treatment |  | Posting
---
multilingually
in $(L_{x},L_{y})$
| Having $\geq 1$
---
$(L_{x},L_{y})$ multilingual
neighbor
| Having $\geq 1$
---
$(L_{x},L_{y})$ multilingual
neighbor
Outcome |  | Betweenness
---
centrality
| Sharing domain
---
from $L_{y}$
| Sharing hashtag
---
from $L_{y}$

###  4.1 Measures of influence

Are multilinguals well-positioned in networks to spread information? In Study
1, we quantify their structural role by estimating the effect of multilingual
posting on betweenness centrality. A measure of the proportion of shortest
paths that must go through an intermediary node, betweenness centrality
quantifies the extent to which the intermediary is an information broker
(Freeman 1978). Studies 2 and 3 focus on multilinguals’ influence on content
flows across languages. If an $L_{x}$ monolingual has a multilingual neighbor
in the $(C_{x},C_{y})$ network, how much more likely are they to share $L_{y}$
content? We consider two forms of content: URL domains and hashtags (Hong,
Convertino, and Chi 2011).

##### Betweenness centrality (Study 1)

For each $(C_{x},C_{y})$ MCP, we consider a sample of users from locus $C_{x}$
who post in $L_{x}$. The treatment is if a user is multilingual in
$(L_{x},L_{y})$, and the outcome is the user’s betweenness centrality in the
$(C_{x},C_{y})$ network, scaled by $10^{6}$ and log-transformed.

##### Domain sharing (Study 2)

We first identify URL domains associated with each language. We filter out the
200 overall most-frequent domains. Like stop word removal (Jurafsky and Martin
2000), excluded domains are largely uninformative for measuring content
sharing across languages (e.g. instagram.com, unfollowspy.com). To account for
sampling variability, we exclude domains that appear fewer than ten times
(Monroe, Colaresi, and Quinn 2008). We then consider “domains associated with
$L_{x}$” to be the 100 domains most overrepresented in $L_{x}$ tweets
(excluding retweets) relative to all European tweets based on the weighted log
odds ratio with an informative Dirichlet prior (Monroe, Colaresi, and Quinn
2008). For each MCP $(C_{x},C_{y})$ and locus $C_{x}$, the sample is the set
of $L_{x}$ monolinguals from $C_{x}$. A user $u$ is treated if they have at
least one $(L_{x},L_{y})$ multilingual neighbor, and the outcome is whether
any of $u$’s Decahose tweets contain at least one domain associated with
$L_{y}$, the language $u$ does not use. Note that we exclude retweets for
language-based measures, such as identifying multilinguals and language-
specific domains/hashtags, but include retweets for information-sharing
measures because they are an important component of information diffusion on
Twitter.

##### Hashtag sharing (Study 3)

We similarly identify hashtags associated with each language. Unlike domains,
language-associated hashtags can change rapidly because they may refer to
short-term events, such as elections, protests, or upcoming TV shows. We thus
separate the Decahose data into 60 14-day intervals, and use the log-odds
ratio with informative Dirichlet priors to identify $H_{x}^{i}$, the set of
100 hashtags most associated with $L_{x}$ in interval $i$, after again
filtering out 200 most-frequent hashtags (e.g. #blog, #radio), those occurring
fewer than ten times in $i$, and excluding retweets. A user $u$ “shares” a
hashtag from $L_{x}$ if any of $u$’s Decahose tweets contain at least one
hashtag $h\in H_{x}^{i}$ during $i$ or the subsequent period $i+1$ (including
retweets). Resulting hashtags reflect entertainment, sports, politics, and
everyday life; see Supplemental Material Table S4 for examples of language-
specific domains and hashtags.

###  4.2 Causal inference setup

We use propensity score stratification to estimate the causal effects of
multilingual treatment variables on information diffusion outcomes (Rosenbaum
and Rubin 1983). Our procedure, shown in Figure 2, closely follows the
guidelines provided by Stuart (2010) and MatchIt (Ho et al. 2007).

We first fit a logistic regression model to calculate propensity scores, which
represent the probability of receiving treatment as a function of the
specified covariates (Rosenbaum and Rubin 1983). Covariates are shared for all
studies and capture aspects of users’ Twitter behavior that may affect our
outcomes (Stuart 2010). These include verified status, network degree,
follower and following counts, account age, number of tweets in the Decahose
sample, “favorites” count, and post rate, all log-scaled. Covariates for each
user are based on their most recent tweet in our sample.

We then conduct propensity score stratification where units are separated into
25 strata based on propensity scores and verify sufficient balance for all
covariates with absolute standardized mean difference less than 0.1 (Ho et al.
2007). Although five strata have commonly been used in practice, more strata
can yield less biased estimates for larger samples sizes (Eckles and Bakshy
2020). Propensity score estimation, stratification, and balance checks are
performed using the MatchIt R package (Ho et al. 2007).

We compare treated and untreated units within each stratum by fitting a
regression of each outcome on treatment status, weighted by the matching
weights. In particular, we estimate the average treatment effect on the
treated (ATT); this estimates how the outcome among treated units is different
than in the counterfactual scenario where they are not treated (Stuart 2010).
For all studies, we include covariate adjustment to control for direct effects
that pre-treatment covariates may have on the outcome.

Because each study defines units, treatment, and outcomes differently, the
specific details of ATT estimation vary. In Study 1, we fit a linear
regression after propensity score stratification to estimate the difference in
(scaled and log-transformed) betweenness centrality. Here, multilingualism
increases betweenness centrality if ATT $>$0\. In Studies 2 and 3, the
outcomes are binary so we use logistic regression to estimate ATT as an odds
ratio, a measure used in prior work to compare domain and hashtag sharing
behaviors across languages (Hong, Convertino, and Chi 2011). For some treated
user $u$ who is monolingual in $L_{x}$, the ATT estimates the odds that $u$
shares a domain (hashtag) associated with $L_{y}$ divided by the odds that $u$
would share a domain (hashtag) associated with $L_{y}$ in the counterfactual
scenario where $u$ is not treated. ATT $>$ 1 indicates that having a
multilingual $(L_{x},L_{y})$ contact increases an $L_{x}$ monolingual’s
likelihood of sharing domains (hashtags) from $L_{y}$.

In each study, we estimate the ATT on aggregate data from both loci in all MCP
networks in order to get a single causal estimate of the role of multilinguals
in cross-lingual information exchange. We additionally estimate separate ATT
values for units from each locus in each MCP network, and analyze the
variation across locus-specific causal effects later. To ensure sufficient
data across strata, we only estimate locus-specific causal effects for locus
$C_{x}$ in MCP $(C_{x},C_{y})$ if there are at least 100 treated units.
Furthermore, we only include locus-specific causal effects for Studies 2 and 3
if at least 100 units in the combined treated and untreated sample (all
$L_{x}$ monolinguals) have an outcome of 1 (share at least one domain or
hashtag from $L_{y}$).

##  5 Overall effects of multilingual behavior

Table 2: Summary of causal effect of posting multilingually (Study 1) or
having a multilingual friend (Studies 2 and 3) for users in each country
(locus) across all multilingual country pairs (MCPs). Despite considerable
variation across loci, each treatment has positive effects on information
diffusion-related outcomes far more than negative effects. We estimate are
average treated effect on the treated (ATT), and significance is determined at
$p<0.05$ with robust standard error estimation.

| Outcome
---|---
|  | Betweenness
---
Centrality
(Study 1)
| Domain
---
Sharing
(Study 2)
| Hashtag
---
Sharing
(Study 3)
# Eligible MCPs | 214 | 158 | 199
# Eligible Loci | 317 | 205 | 284
% Loci w/ sig. pos ATT | 46.37% | 56.10% | 50.00%
% Loci w/ no sig. ATT | 51.42% | 40.49% | 46.48%
% Loci w/ sig. neg ATT | 2.21% | 3.41% | 3.52%

All 3 studies support our hypothesis that multilinguals play an important role
in cross-lingual information exchange.

##### Betweenness centrality (Study 1)

Multilingual $(L_{x},L_{y})$ users have higher betweenness centrality in the
$(C_{x},C_{y})$ network than their monolingual peers, suggesting that these
users serve as local bridges and thus are well-positioned to spread novel
information across the network (Freeman 1978; Granovetter 1973). The overall
ATT is 0.034 ($p<0.0001$, with robust standard error estimation). In other
words, multilingual posting increases the outcome of log-transformed
betweenness by 0.034, which corresponds to a 13.5% increase in betweenness
centrality.

##### URL domain sharing (Study 2)

Having a multilingual $(L_{x},L_{y})$ friend increases the odds of a
monolingual $L_{x}$ user sharing a domain from $L_{y}$ by a factor of 15.57
($p<0.0001$). For interpretability, we also estimate the average marginal
effect with a marginal effects model on the logistic regression used for
estimating the ATT as an odds ratio. We find that having a $(L_{x},L_{y})$
friend increases an $L_{x}$ monolingual’s probability of sharing an $L_{y}$
domain by 20.0%.

##### Hashtag sharing (Study 3)

Having a multilingual $(L_{x},L_{y}$) friend significantly increases the odds
of an $L_{x}$ monolingual sharing a hashtag from $L_{y}$ by a factor of 3.98
($p<0.0001$). Through estimating the average marginal effect, we find that
this corresponds to an increase in the probability of sharing an $L_{y}$
hashtag by 32.7%. While the odds ratio for hashtag sharing is lower than for
domains, the probability increase is greater because extremely low domain-
sharing probabilities result in inflated odds ratios.

Table 2 summarizes locus-specific causal effect estimates. Due to minimum
inclusion criteria, we only estimate ATTs for a subset of all 464 loci
corresponding to 232 MCPs. All three treatments increase information-sharing
outcomes in about half of the loci, and decreases outcomes in under 4% percent
of loci. Locus-specific estimates reinforce that multilinguals facilitate
information exchange across language boundaries. However, the distribution of
positive, negative, and insignificant effects across loci as shown in Table 2
suggests wide variation across MCPs and loci.

Figure 3: Maps of causal effects (ATTs) of multilingual treatments on
information diffusion outcomes. The left map shows the ATTs of multilingual
posting on betweenness centrality (Study 1), where each edge indicates ATT
among users from the destination node who post in both the source and
destination nodes’ dominant languages. The right map shows the ATTs of having
a multilingual friend on cross-lingual domain sharing (Study 2), where each
edge indicates ATT among monolingual users from the destination node on
sharing URL domains associated with the source node’s dominant language.
Negative effects are shown with red arrows and positive are shown with blue
arrows. The magnitude of the causal effect is indicated by arrow shading and
width. Only statistically significant estimates ($p<0.05$) with robust
standard error estimation are shown.

##  6 Variation across country pairs

All three studies show that multilingual behaviors increase cross-lingual
information exchange. However, locus-specific causal estimates indicate
substantial heterogeneity in the magnitude of their effects across MCPs and
loci. We conduct regression analyses to characterize how geographic,
demographic, economic, political, and linguistic relationships between country
pairs correlate with the effects of multilingual behaviors on European
Twitter.

###  6.1 Regression Setup

We fit linear regression models to characterize how the causal effects of
multilinguals vary across countries. For a given MCP $(C_{x},C_{y})$ and locus
$C_{x}$, we define 3 dependent variables: the estimated causal effects (ATT)
of multilingual behavior in each of the 3 described studies. Independent
variables capture relationships between $C_{x}$ and $C_{y}$.

Demographic variables include the population ratio of $C_{x}$ to $C_{y}$ from
2019 based on CEPII’s Gravity Dataset (Head and Mayer 2014; Conte, Cotterlaz,
and Mayer 2021). Using 2017 World Bank migration data, we consider the
fraction of $C_{x}$’s population born in $C_{y}$, $C_{y}$’s population born in
$C_{x}$, and each country’s population who are foreign-
born.777worldbank.org/en/topic/migrationremittancesdiasporaissues/brief/migration-
remittances-data

Geographic variables are the distance (in km) between $C_{x}$ and $C_{y}$’s
population centers and time difference: the number of hours $C_{y}$ is ahead
(further east) of $C_{x}$.

Economic variables include i.) the ratio of $C_{x}$ to $C_{y}$’s GDP per
capita, ii.) if $C_{x}$ and $C_{y}$ are in an RTA (Regional Trade Agreement,
which includes the EU), and iii.) trade flow between $C_{x}$ and $C_{y}$
averaged over both country’s reports and both directions, normalized by the
total population of both countries. Geographic and economic variables use 2019
data from CEPII’s Gravity Dataset.

Political variables, specifically material conflict between $C_{x}$ and
$C_{y}$, are determined by querying the GDELT event database (Leetaru and
Schrodt 2013). These include the percent of $C_{x}$’s external conflict
actions inflicted on $C_{y}$, and $C_{y}$’s external conflict actions
inflicted on $C_{x}$.

The last fixed effect is linguistic distance between $L_{x}$ and $L_{y}$ using
Glottolog (Hammarström et al. 2021). Inspired by Samoilenko et al. (2016)’s
measurement of shared language families, we define a 4-level measurement of
linguistic distance between $L_{x}$ and $L_{y}$: i.) no relationship (e.g.
Spanish and Hungarian), ii.) in the same primary family (e.g. German and
Polish are Indo-European), iii.) in the same branch (e.g. English and Swedish
are Germanic), and iv.) in the same sub-branch (e.g. Spanish and Italian are
Romance).888We do not use graph-based measurements of distance because there
is wide variation across language branches’ structures due to an uneven
interest by linguists across languages.

We avoid multicollinearity issues by ensuring that all variables’ variance
inflation factor is under 4. We thus exclude highly-correlated variables, such
as EU membership and each country’s population. We weight each regression
model by the number of treated units from each locus. Finally, we scale all
variables by z-score to facilitate direct comparisons.

Table 3: Geographic, demographic, economic, political, and linguistic aspects
of relations between countries $C_{x}$ and $C_{y}$ and their associations with
the role of multilinguals in spreading information from $C_{y}$’s dominant
language to $C_{x}$. Coefficients are from weighted linear regression models
where the dependent variables are the locus-specific causal effects (ATTs) of
multilingual behaviors in Studies 1-3.

| Dependent variable:
---|---
| Betweenness | Domain | Hashtag
| Centrality | Sharing | Sharing
Geographic distance | $0.020^{***}$ | $0.434$ | $0.412^{***}$
Time difference | $0.015^{**}$ | $0.815^{***}$ | $0.001$
Pop. $C_{x}$ / $C_{y}$ | $-0.030^{***}$ | $-0.031$ | $0.017$
% $C_{y}$ foreign-born | $0.021^{**}$ | $-1.100^{**}$ | $-0.058$
% $C_{y}$ pop. born in $C_{x}$ | $0.017^{**}$ | $-0.044$ | $-0.043$
% $C_{x}$ foreign-born | $-0.002$ | $-0.031$ | $0.064$
% $C_{x}$ pop. born in $C_{y}$ | $0.007$ | $-0.197$ | $-0.040$
GDP per capita $C_{x}$ / $C_{y}$ | $0.038^{***}$ | $0.318$ | $1.171^{***}$
RTA | $0.010$ | $-0.411$ | $0.216^{*}$
Tradeflow per capita | $-0.013^{*}$ | $0.425$ | $0.109$
% $C_{x}$’s conflicts vs. $C_{y}$ | $0.019^{**}$ | $-0.159$ | $-0.032$
% $C_{y}$’s conflicts vs. $C_{x}$ | $0.002$ | $-0.257$ | $0.088$
Linguistic distance | $-0.027^{***}$ | $0.449$ | $0.146^{*}$
Observations | 317 | 205 | 284
R2 | 0.266 | 0.193 | 0.448
Note: |  ∗p$<$0.1; ∗∗p$<$0.05; ∗∗∗p$<$0.01

###  6.2 Regression results

#### Geography

Multilinguals have a larger effect on cross-lingual information exchange in
MCPs where $C_{x}$ and $C_{y}$ are further away from each other (Table 3). We
visualize this pattern in Figure 3, which shows the relationship between
geography and effects of multilinguals on betweenness centrality and domain
sharing (see Figure S4 in Supplemental Material for the hashtag sharing map).

In Figure 3, the effect of multilinguals in MCP $(C_{x},C_{y})$ and locus
$C_{x}$ is shown as a directed edge from $C_{y}$ to $C_{x}$. Only edges
corresponding to significant estimates are drawn. Negative effects are red,
positive effects are blue, and greater magnitude is represented with darker
and thicker edges. For example, the dark blue edge from Russia to Spain in
both maps indicates that Russian-Spanish multilinguals are especially
important for bringing Russian information to Spain. In contrast, the faint
edge from Portugal to Spain means that Portuguese-Spanish multilinguals have a
smaller role in importing Portuguese information to Spain.

We believe stronger treatment effects across longer distances are due to
information accessibility. Information from faraway places is not readily
accessible for monolinguals, so they may need to rely more on their
multilingual friends to serve as information brokers. On the other hand,
information between nearby countries such as Norway and Sweden may be more
easily accessible with more channels for diffusion, possibly via more
multilinguals, so users rely less on individual multilinguals to spread
information.

In addition to distance, the time diference between $C_{x}$ and $C_{y}$
significantly predicts multilinguals’ effects (Table 3). Multilinguals have
the largest impact when $C_{y}$ is 2-3 hours ahead (i.e., further east) of
$C_{x}$ (see Figure S1 in Supplemental Material). In other words,
multilinguals have a large impact on spreading information from Eastern
European languages to Western Europe. This asymmetric East-West pattern is
visible in both maps of Figure 3 (e.g. the many dark blue outgoing links from
Russia indicate a large influence of multilingual users from other countries
who post in Russian).

Why are Western European users of Eastern European languages so influential in
cross-lingual information exchange? Fully answering this question is left for
future work, but we speculate that it is a consequence of historical
inaccessibility to information across strict Cold War-era borders.
Additionally, offline connections may explain these users’ online role as
bridges; Eastern migrants in Western Europe have been characterized by more
transnational and circular offline networks since the early 2000s (Favell
2008).

#### Demographics

Multilingualism increases betweenness centrality more for users in smaller
countries who post in more populous countries’ languages, but this
relationship is not significant for content sharing outcomes. While
multilinguals in smaller countries are better positioned within MCP networks
to spread novel information, they do not necessarily “import” information from
the larger to smaller country.

When $C_{y}$ has a higher proportion of foreign-born residents,
$(L_{x},L_{y})$ multilingualism has a greater impact on users’ structural role
in $C_{x}$, but not communication influence. In fact, having a multilingual
friend has a negative impact on sharing a domain from $L_{y}$ when $C_{y}$ has
more foreign-born residents; perhaps this is because shared links from $L_{y}$
feature more content with multicultural or international appeal, so people
rely less on multilinguals as information brokers. We observe no significant
effects of the foreign-born population of $C_{x}$ on either the structural
role or communication influence of multilinguals.

#### Economics and politics

Multilinguals’ effects are associated with GDP per capita: multilinguals in
$C_{x}$ have more influence when $C_{x}$ is wealthier than $C_{y}$ (although
this is not significant for domain sharing). However, government-level
relationships including national trade agreements, tradeflows, and political
conflict are not significant predictors.

#### Linguistic similarity

We observe mixed results for linguistic distance. Our information
accessibility hypothesis suggests $(L_{x},L_{y})$ multilinguals would play a
larger role when $L_{x}$ and $L_{y}$ are very different because their
monolingual peers would rely more heavily on them. This pattern appears to
hold for communication influence outcomes but is not significant. Contrary to
our hypothesis, multilinguals’ structural role is amplified when $L_{x}$ and
$L_{y}$ are closely related. Though we do not have a clear explanation for
this pattern, it may be driven by deeper differences in the structures of MCP
networks connecting countries with similar dominant languages (Herring et al.
2007).

##  7 Variation across topics

Different aspects of content, such as topic, framing, sentiment, and
subjectivity, could amplify or hinder multilinguals’ influence. Therefore,
here we extend Study 3 to investigate how the role of multilinguals depends on
the topic of shared hashtags using unsupervised topic modeling (Kim et al.
2014; Jin 2017). Specifically, for each MCP $(C_{x},C_{y})$ we measure how
having a multilingual $(L_{x},L_{y})$ friend affects the odds of an $L_{x}$
monolingual from $C_{x}$ sharing a topic-specific hashtag from $C_{y}$.

Table 4: Fifteen topics of interest with example hashtags

ID | Description | Example Hashtags
---|---|---
1 | TV Shows | skamitalia, masterchefgr, thearchers, ibes
10 | Fandoms | jungkook, choicefandom, saveshadowhunters
19 | Art | painting, etsy, vintage, fasion, architecture, arte
24 | Romance TV | loveisland, poweroflovegr, liebesgschichten
44 | TV Promo | comingsoon, luciferonnetflix, skyupnext
3 | Job Promo | career, hiring, jobs, startup, sales, jobsearch
16 | Giveaways | giveaway, freebiefriday, sorteo, winwin, free
31 | Music Promo | radio, youtube, hits, newmusic, magicfm, live
11 | Government, News | bbcnews, afd, noafd, labour, parlament, orban
23 | Covid, Crisis, Tech | covid19france, koronawirus, polizei, tech, gdpr
30 | International Politics | france, syria, venezuela, eeuu, isis, migranti
41 | Health | mentalhealth, autism, clapfornhs, discapacidad
46 | Requests | stop, shoplocal, stopgiletsjaunes, helpme
47 | Equality | metoo, 8demarzo, weltfrauentag, racisme, lgbt
48 | Sports | arsenal, halamadrid, futbol, fcporto, rusia2018

###  7.1 Measuring multilinguals’ role across topics

#### Identifying topic-specific hashtags

We train a multilingual contextualized topic model (CTM) to identify topics
(Bianchi et al. 2021). This approach uses multilingual sentence-BERT (Reimers
and Gurevych 2019) as input to a variational autoencoder topic model to
support zero-shot topic prediction for texts in unseen languages during
training (Srivastava and Sutton 2017; Bianchi et al. 2021). Crucially, the CTM
uses the same topics in all languages, making direct comparisons across
languages possible. The CTM is trained for 20 epochs on a random sample of 1M
English-language tweets from the European decahose data. We set the total
number of topics to 50 and retain default values for all other
hyperparameters. We use the trained CTM to predict topic distributions for all
tweets containing any hashtag highly associated with any language in any time
interval. Tweets are assigned to the single topic with the highest
probability, and hashtags are then assigned to single topics based on the most
frequent topic of tweets in which it appears.

We then manually inspect the ten most-frequent hashtags per topic to identify
15 topics of interest, where hashtags are coherent, meaningful, and reflect
different types of content. These 15 topics account for 52.1% of European
tweets from our dataset, and 66.8% of unique hashtags associated with any
language at any time, which rises to 82.5% when accounting for hashtag
frequency (see topic distributions in Figure S3 in Supplemental Material).
Brief topic descriptions are in Table 4. To understand how content shapes
multilinguals’ influence, we separate the 15 topics into four macro-
categories: entertainment, politics, sports, and promotion (e.g., giveaways).
The distribution of hashtag macro-categories across languages are shown in
Figure S2 in the Supplemental Material.

#### Evaluation

We evaluated our multilingual hashtag topic assignment method with intrusion
tests, following common practice (Hoyle et al. 2021). Four hashtags were
sampled from each topic $t$ with frequency weighting, and one hashtag was
sampled from one of the 49 other topics based on frequency. Annotators tried
to identify which hashtag does not belong to $t$. We evaluated the 15 topics
of interest in English, German, Spanish, Italian, and Turkish, selected based
on annotators’ language proficiencies. We conducted ten intruder tests for
each topic in English, and 5 for each topic in the other languages. Three
annotators completed the intruder test for English hashtags with an average
accuracy of 0.73 and interannotator agreement of 0.67 (Krippendorff $\alpha$).
Among tasks where at least two annotators selected the same intruder, accuracy
rose to 0.78, and further rose to 0.89 for tasks where all three annotators
agreed.

Table 5: Annotator accuracy on topic intrusion tests, averaged over 3
annotators for English.

Language | English | Spanish | German | Turkish | Italian
---|---|---|---|---|---
Accuracy | 0.731 | 0.747 | 0.813 | 0.587 | 0.627

#### Estimating causal effects

We extend Study 3’s design to compare multilinguals’ effects across topics.
For a given MCP $(C_{x},C_{y})$ and locus $C_{x}$, units are $L_{x}$
monolinguals from $C_{x}$ and the treatment is having at least one
multilingual $(L_{x},L_{y})$ contact. For each topic $t$, the outcome is
whether a user shares a hashtag that belongs to topic $t$ and is associated
with $L_{y}$. Our analysis focuses on overall causal effect estimates for all
15 topics. We also estimate locus-specific effects for each macro-category,
with a summary of results and maps in the Supplemental Material (Figure S4).

Figure 4: Causal effect (ATT) of having a multilingual friend on cross-lingual
hashtag sharing across topics. All estimates have heteroskedasticity-
consistent standard errors below 0.03 and are significant (p$<$0.0001). The
x-axis shows log odds ratios, with 0 being no effect of having a multilingual
friend, and the dashed line at 1.38 shows the overall effect on hashtag
sharing. Colors represent macro-categories.

###  7.2 Results

Figure 4 shows the effect of multilinguals by hashtag topic and macro-
category. Three key findings support our hypothesis that multilinguals play a
greater role in spreading information that is otherwise less accessible to
their peers.

First, multilinguals have a greater communication influence on the cross-
lingual diffusion of political content than entertainment. Political discourse
likely occurs more in regional or country-centric public spheres (Schünemann
2020), so there is a greater reliance on multilingual individuals to broker
political information across borders. Correspondingly, of all political
topics, multilinguals play a smaller role for topics with widespread
transnational popularity and awareness, such as: Equality, which includes
hashtags for global gender equality movements like #metoo and #8m, and
International Politics, which includes country names and non-European
political organizations. Contrary to most political hashtags, entertainment
hashtags often reflect globally-popular phenomena (e.g., K-pop fandoms)
despite being associated with specific languages, and more cross-lingual
information cascades involve entertainment content (Jin 2017). We believe that
individual multilingual contacts play a smaller role in entertainment because
there are more ways for that content to spread.

Second, we argue that multilingual individuals are crucial for nascent social
movements to gain global traction, but have a relatively small influence
within well-established transnational movements. Figure 4 indicates that
multilinguals have a large impact on spreading health-related information,
which includes hashtags advocating for mental health awareness, COVID-related
activism, and disability rights. In contrast to racial and gender equality
movements reflected in the Equality topic, organizing for disability rights
gained traction as a social movement later than for race and gender both
offline and on Twitter, where the public sphere about disability is still
growing (Scotch 1988; Sarkar et al. 2021). The initial adoption of burgeoning
social movements across countries relies heavily on direct contacts, such as
multilingual friends (McAdam and Rucht 1993). Information diffusion about more
well-established social movements do not depend on multilingual bridges
because it occurs via many channels of communication, including news and
television media (McAdam and Rucht 1993).

Third, multilinguals play an especially important role in sharing information
about job searches and career opportunities. This parallels Granovetter
(1973)’s argument about the strength of weak ties for job-seeking purposes.
Even though monolingual users’ ties with multilinguals are not necessarily
weak, multilinguals similarly serve as bridges between different parts of a
social network and thus facilitate access to novel information, such as job
opportunities that users may not have otherwise been aware of.

##  8 Discussion

Gaining a complete picture of global information diffusion requires
understanding how information crosses languages. We design three studies to
investigate how multilingual users participate in this process, which we use
to quantify their structural role and communication influence in information
exchange across European languages on Twitter. For each pair of countries with
different dominant languages, we construct networks where users are connected
if they have mutually “mentioned” each other. We use these networks in Study 1
to quantify the extent to which multilinguals’ positions in these networks
facilitate spreading novel information, as measured by betweenness centrality.
In Studies 2 and 3, we quantify how having a multilingual contact influences
the odds of monolingual users posting content from the other country’s
dominant language.

Results from all three studies show that multilinguals play an outsize role in
cross-lingual information exchange compared to their monolingual peers.
Effects vary widely but systematically across country pairs and topics. We
conduct regression analyses to measure how the role of multilinguals is
associated with demographic, geographic, economic, political, and linguistic
aspects of the relationship between country pairs. To compare multilinguals’
influence across topics, we augment our study design for hashtag sharing with
multilingual contextualized topic modeling.

In general, multilingual individuals have a greater influence on the spread of
information that is otherwise less accessible to their monolingual peers, as
they play more of a gatekeeping role. Multilinguals have a greater effect on
information diffusion between dominant languages of countries that are
geographically far apart, with Western European multilinguals who post in
Eastern European languages having an especially big influence. We identify a
similar pattern for topics, where multilinguals have greater influence on
cross-lingual information exchange for topics discussed in more restricted
public spheres: national or regionally-oriented politics over entertainment
which can have international appeal, nascent health-related social movements
over established racial and gender equality movements, and job opportunities
previously known only to small communities.

We acknowledge that this work has important limitations. First, our studies do
not account for multilinguals who use minority languages (e.g., Basque) or
reside in highly multilingual countries (e.g., Switzerland). Imperfect
performance of location inference and language detection also limited the set
of countries and languages studied. Furthermore, we make the simplifying
assumption that tweets are written in one language, which does not adequately
account for code-mixing within posts and the users who engage in such
practices (Androutsopoulos 2015). While code-mixed tweets are a relatively
small percent of Twitter communication (Rijhwani et al. 2017), accurately
recognizing these tweets at scale has proven challenging due to the absence of
labeled data for training models (Jurgens, Tsvetkov, and Jurafsky 2017). As
the performance and efficiency of language detection of code-mixed tweets
improve, we anticipate that incorporating such information would be fruitful,
and analyzing the relationship between code-mixing strategies and information
diffusion could yield interesting theoretical insights.

To avoid making assumptions about people’s offline language usage or
competence, we intentionally define multilinguals based on their performance
on Twitter. However, this presents a significant limitation: users who only
tweet in one language but understand multiple may also play an important, and
perhaps different, role in information diffusion. Future research could employ
different methodologies to highlight these users, such as linking social media
activity with survey data about users’ language backgrounds.

Further research can also improve upon our study designs. We adopt a
traditional causal inference setup, which considers treatment status binary to
emulate randomized experiments. Thus, all of our studies involve collapsing
underlying continuous variables into binary indicators. A possible next step
would involve adapting our studies to account for continuous treatments; this
would facilitate investigation of how cross-lingual information exchange is
impacted by a user’s degree of multilingualism (Study 1) or the number and/or
strength of a user’s ties to multilinguals (Studies 2 and 3).

Beyond addressing these limitations, there are numerous avenues for future
work. For example, we adopt a microscopic perspective on information diffusion
by examining how multilingualism impacts individual users’ roles in
information diffusion; we focus on local influence because it is more precise
and less random than observations of information cascades (Bakshy et al.
2011). Nevertheless, an interesting extension that takes a macroscopic
perspective, perhaps involving simulations of cross-lingual information
cascades (Chen et al. 2021), could help contextualize how these individual-
level effects, in aggregate, shape the global flow of information. Another
future direction would involve considering other forms of information that may
spread via different mechanisms than URLs and hashtags, such as meme
templates, images, videos, and text outside of hashtags. There is likely
variation in the role of multilinguals across semantic dimensions beyond
topic, such as emotional valence or misinformation. Finally, future research
can assess the generalizability of our findings beyond the scope of European
Twitter by applying our methodology to study other regions, languages, and
platforms.

##  9 Broader impact and ethical considerations

Understanding the role of multilinguals in information diffusion has immense
consequences. Platforms like Twitter can empower multilinguals to spread
information that supports positive outcomes such as knowledge-sharing,
collaboration, crisis response, or social progress, thus enabling different
language communities to benefit from a truly global social network (Eleta and
Golbeck 2014; Hale 2014a). Our study not only highlights this potential but
also identifies how it varies across topics and with respect to the
geographical, linguistic, and political relationship between the countries.
For instance, our research suggests that multilinguals can be better utilized
to spread political news as opposed to entertainment. We also see that their
importance is more pronounced for supporting information spread across
countries further away from each other. Such findings not only highlight the
contexts where multilinguals already play an important role but also help us
identify the barriers for cross-lingual diffusion; in such situations,
platforms may benefit more from technologies such as machine translation.

Our research can also help platforms address dangerous consequences of global
networks by focusing efforts on nudging multilinguals to mitigate the spread
of harmful information such as misinformation, conspiracy theories, or online
abuse. Past network science research shows the value of betweenness centrality
in identifying nodes that can limit the spread of such information
(Golovchenko, Hartmann, and Adler-Nissen 2018). Here, we show that
multilingual nodes tend to have high betweenness centrality. Furthermore, our
study shows that multilinguals play a particularly important role in the
spread of political topics, common targets for malicious actors aiming to
spread propaganda and disinformation.

Despite the potential for positive impact, we acknowledge the ethical risks of
this work. Rather than stem the flow of harmful content, our work may inspire
malicious agents to target and manipulate multilinguals into propagating such
information. In addition, our focus on users of politically and socially-
dominant language varieties and use of automated language detection excludes
people whose posts contain endangered or minority languages, non-prestige
dialects of dominant languages, or code-mixing. Although our work does not
present direct harm to individuals, these decisions systematically exclude
marginalized groups whose online behavior deserves equal consideration.

To promote transparency and future research, we publicly share data, code, and
models but take steps to preserve user privacy. The datasheets shared for
causal effect estimation include only variables necessary to replicate our
results. We do not share user IDs, raw text, or other personally-identifiable
information. While the location inference tool used presents a privacy risk by
inferring users’ specific geo-coordinates, we only store information at the
country level.

##  10 Conclusion

By developing a set of causal inference studies that measure users’ structural
role and communication influence, we show that multilingually-posting users on
European Twitter are particularly important for information diffusion across
languages. These users have an especially large influence in situations where
they serve more as gatekeepers in information flow, particularly in spreading
information from places and topics that are otherwise inaccessible to their
monolingual peers. This work is crucial for understanding how information is
shared around the world, and has implications for platforms to support
beneficial consequences of global social networks while mitigating potential
harms. Publicly-available code, models, and aggregated data can be found at:
https://github.com/juliamendelsohn/bridging-nations.

## Acknowledgments

This research was supported by the National Science Foundation (Grants
IIS-1815875 and IIS-2007251) and through funding from the Volkswagen
Foundation.

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## Supplemental Material

Table S1: All countries initially considered, the set of included countries
with dominant language code, and set of excluded countries after filtering.

Included Countries | Excluded Countries
---|---
Alpha-2 | Country | Language | Alpha-2 | Country | Language | Country | Reason
AM | Armenia | hy | IE | Ireland | en | Andorra | size
AT | Austria | de | IL | Israel | iw | Albania | no Albanian tweets
AZ | Azerbaijan | tr | IS | Iceland | is | Bosnia | multilingual
BG | Bulgaria | bg | IT | Italy | it | Belgium | multilingual
BY | Belarus | ru | LT | Lithuania | lt | Switzerland | multilingual
CZ | Czechia | cs | LV | Latvia | lv | Cyprus | multilingual
DE | Germany | de | MD | Moldova | ro | Croatia | 67% undetected lang
DK | Denmark | da | NL | Netherlands | nl | Liechtenstein | size
EE | Estonia | et | NO | Norway | no | Luxembourg | multilingual
ES | Spain | es | PL | Poland | pl | Monaco | size
FI | Finland | fi | PT | Portugal | pt | Montenegro | multilingual
FR | France | fr | RO | Romania | ro | North Macedonia | multilingual
GB | United Kingdom | en | RU | Russia | ru | Malta | size
GE | Georgia | ka | SE | Sweden | sv | Serbia | 70% undetected lang
GR | Greece | el | SI | Slovenia | sl | Slovakia | no Slovak tweets
HU | Hungary | hu | TR | Turkey | tr | San Marino | size
|  |  |  |  |  | Ukraine | multilingual
|  |  |  |  |  | Vatican City | size

Table S2: All eligible multilingual country pairs (MCPs). Abbreviations are
ISO 3166-1 alpha-2 country codes.

(AM, IE) | (AZ, SE) | (DK, IT) | (FI, SE) | (DE, NL) | (IE, IL) | (LT, LV) | (PL, PT)
---|---|---|---|---|---|---|---
(AM, RU) | (AZ, GB) | (DK, NL) | (FI, TR) | (DE, NO) | (IE, IT) | (LV, NL) | (PL, RU)
(AM, GB) | (BG, BY) | (DK, NO) | (FI, GB) | (DE, PL) | (IE, LV) | (LV, PL) | (PL, SI)
(AT, CZ) | (BY, CZ) | (DK, PL) | (DE, FR) | (DE, PT) | (IE, LT) | (LV, RU) | (ES, PL)
(AT, FI) | (BY, FR) | (DK, ES) | (FR, GR) | (DE, RU) | (IE, NL) | (ES, LV) | (PL, SE)
(AT, FR) | (BY, DE) | (DK, SE) | (FR, HU) | (DE, ES) | (IE, NO) | (LV, TR) | (PL, TR)
(AT, GR) | (BY, IE) | (DK, TR) | (FR, IS) | (DE, SE) | (IE, PL) | (GB, LV) | (GB, PL)
(AT, IE) | (BY, LV) | (DK, GB) | (FR, IE) | (DE, TR) | (IE, PT) | (LT, NL) | (PT, RO)
(AT, IT) | (BY, PL) | (EE, FI) | (FR, IL) | (DE, GB) | (IE, RO) | (LT, PL) | (PT, RU)
(AT, NL) | (BY, ES) | (EE, FR) | (FR, IT) | (GR, IE) | (IE, RU) | (LT, PT) | (ES, PT)
(AT, NO) | (BY, TR) | (DE, EE) | (FR, LV) | (GR, IT) | (IE, SI) | (ES, LT) | (PT, SE)
(AT, PL) | (BY, GB) | (EE, IE) | (FR, LT) | (ES, GR) | (ES, IE) | (LT, SE) | (PT, TR)
(AT, PT) | (BG, IE) | (EE, IT) | (FR, NL) | (GR, TR) | (IE, SE) | (LT, TR) | (GB, PT)
(AT, RU) | (BG, RU) | (EE, NL) | (FR, NO) | (GB, GR) | (IE, TR) | (GB, LT) | (ES, RO)
(AT, ES) | (BG, GB) | (EE, PL) | (FR, PL) | (HU, IE) | (IL, RU) | (NL, NO) | (RO, TR)
(AT, SE) | (CZ, FR) | (EE, PT) | (FR, PT) | (HU, IT) | (ES, IL) | (NL, PL) | (GB, RO)
(AT, TR) | (CZ, DE) | (EE, RU) | (FR, RO) | (HU, NL) | (GB, IL) | (NL, PT) | (ES, RU)
(AT, GB) | (CZ, IE) | (EE, ES) | (FR, RU) | (HU, PL) | (IT, LV) | (NL, RO) | (RU, SE)
(AZ, BY) | (CZ, IT) | (EE, SE) | (FR, SI) | (HU, PT) | (IT, LT) | (NL, RU) | (RU, TR)
(AZ, FI) | (CZ, PL) | (EE, TR) | (ES, FR) | (ES, HU) | (IT, NL) | (ES, NL) | (GB, RU)
(AZ, FR) | (CZ, RU) | (EE, GB) | (FR, SE) | (HU, SE) | (IT, PL) | (NL, SE) | (ES, SI)
(AZ, DE) | (CZ, SI) | (FI, FR) | (FR, TR) | (HU, TR) | (IT, PT) | (NL, TR) | (SI, TR)
(AZ, IE) | (CZ, ES) | (DE, FI) | (FR, GB) | (GB, HU) | (IT, RO) | (GB, NL) | (GB, SI)
(AZ, IT) | (CZ, SE) | (FI, IE) | (DE, GR) | (IE, IS) | (IT, RU) | (NO, PL) | (ES, SE)
(AZ, NL) | (CZ, TR) | (FI, IT) | (DE, HU) | (IS, NL) | (IT, SI) | (NO, PT) | (ES, TR)
(AZ, PL) | (CZ, GB) | (FI, NL) | (DE, IE) | (ES, IS) | (ES, IT) | (ES, NO) | (ES, GB)
(AZ, PT) | (DK, FR) | (FI, PT) | (DE, IT) | (IS, SE) | (IT, SE) | (NO, SE) | (SE, TR)
(AZ, RU) | (DE, DK) | (FI, RU) | (DE, LV) | (IS, TR) | (IT, TR) | (NO, TR) | (GB, SE)
(AZ, ES) | (DK, IE) | (ES, FI) | (DE, LT) | (GB, IS) | (GB, IT) | (GB, NO) | (GB, TR)

Table S3: Intercoder agreement (Krippendorff’s $\alpha$) between each pair of LangID models. Bolded values represent the highest agreement score for each column. Twitter’s LangID model has the highest average agreement with other models. Model | Twitter | fastText | langid.py | langdetect | CLD2 | CLD3
---|---|---|---|---|---|---
Twitter | - | 0.87 | 0.84 | 0.82 | 0.82 | 0.77
fastText | 0.87 | - | 0.86 | 0.80 | 0.79 | 0.75
langid.py | 0.84 | 0.86 | - | 0.79 | 0.76 | 0.74
langdetect | 0.82 | 0.80 | 0.79 | - | 0.70 | 0.70
CLD2 | 0.82 | 0.79 | 0.76 | 0.70 | - | 0.71
CLD3 | 0.77 | 0.75 | 0.74 | 0.70 | 0.71 | -
Mean | 0.824 | 0.814 | 0.798 | 0.763 | 0.760 | 0.734
Table S4: Examples of hashtags (from one selected time interval) and domains associated with five different languages.  German | Portuguese | Turkish | Polish | English
---|---|---|---|---
cdu | fcporto | çağlarertuğrul | pis | oddoneout
spd | todosportugal | sustunuz | konwencjapis | remain
merkel | capricórnio | pazartesi | topmodel | eastenders
klimaschutz | aquário | cumartesi | thevoiceofpoland | liarjohnson
noafd | sportingcp | burcuözberk | kaczyński | ncfc
tagesschau.de | publico.pt | tele1.com.tr | wpolityce.pl | manchestereveningnews.co.uk
faz.net | record.pt | haber.sol.org.tr | niezalezna.pl | whounfollowedme.org
spiegel.de | maisfutebol.iol.pt | diken.com.tr | dorzeczy.pl | theneweuropean.co.uk
Figure S1: Treatment effect of multilinguals on bringing information
associated with country $C_{y}$’s dominant language to country $C_{x}$ as a
function of the timezone difference between the two countries. To account for
population differences, we average ATTs over all treated users, even though
there is a single ATT value per country in each pair. The relationship between
ATT and timezone is shown for all three measures of influence: betweenness
centrality (Study 1, left), domain sharing (Study 2, center), and hashtag
sharing (Study 3, right). Effects are greatest when $C_{y}$ is 2-3 hours ahead
(i.e. much further east) of $C_{x}$. Figure S2: Fraction of unique hashtags
associated with sample of languages for each of four macro-topics, sorted left
to right from largest to smallest proportion of political hashtags. Finnish
and German hashtags are largely political, while Russian, Spanish, and
Hungarian hashtags tend to be more entertainment-oriented. Portuguese hashtags
are much more focused on entertainment and sports compared to politics. This
figure also illustrates how language choice can be a meaningful
sociolinguistic variable. For example, while Spanish hashtags are more
entertainment-oriented, Catalan hashtags tend to be more political; this is
consistent with prior work that shows Catalan is used online to mark political
identity (Stewart, Pinter, and Eisenstein 2018).

Figure S3: Distributions of tweets and hashtags assigned to all 50 topics with multilingual contextualized topic modeling. The top plot shows the fraction of all tweets assigned to each topic. The middle plot shows the fraction of unique hashtags assigned to each topic (out of all hashtags associated with at least one language in one time interval), and the bottom shows distribution of total hashtags over topics accounting for frequency in the Decahose data. Table S5: Summary of locus-specific causal estimates for each topic macro-category. Out of the 464 possible loci from 232 MCPs, ATT estimates were only calculated for a subset of loci with sufficient data. The remaining columns show the percent of estimates where multilinguals have a significantly positive, negative, and no significant effect (based on heteroskedasticity-consistent standard error estimation with $p<0.05$. Macro-Topic |  | Num.
---
Estimates
% Positive | % Negative |  | % No Sig.
---
Effect
Politics | 240 | 0.433 | 0.054 | 0.513
Entertainment | 228 | 0.474 | 0.035 | 0.491
Sports | 199 | 0.432 | 0.030 | 0.538
Promotion | 146 | 0.370 | 0.034 | 0.596

Figure S4: Causal effect (ATT) of having a multilingual friend on overall
cross-lingual hashtag sharing (top left) and four macro-topics: politics (top
right), entertainment (middle left), sports (middle right), and promotion
(bottom right). Each edge indicates effect on the likelihood of monolingual
users from the destination node to share a hashtag associated with the source
node’s dominant language. Negative effects are in red and positive are blue.
The magnitude of the causal effect is indicated by arrow shading and width.
Only statistically significant estimates ($p<0.05$ with robust standard error
estimation) are shown.
The heatmap on the bottom right shows Pearson correlations between locus-
specific causal estimates over all 7 outcomes (the 3 main studies, and 4
topic-specific outcomes). Given that the betweenness centrality outcome is
based on a different sample, treatment, and estimator than the communication
influence measurements, it is not surprising that betweenness centrality has
the weakest correlation with other outcomes. Nevertheless, there is still
positive correlation with most other outcomes, particularly domain and
political hashtag sharing. Correlation between the communication influence
outcomes are much higher, especially for domain and political hashtag sharing.
This suggests that there may be commonalities in the type of content, with
language-specific domains primarily focusing on news and political content.