DEIM Forum 2017 G2-1

Predicting Influential Cross-lingual Information Cascades on Twitter
Hongshan JIN††The University of Tokyo

and

Masashi TOYODA†

4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505 Japan

E-mail: †{jhs, toyoda}@tkl.iis.u-tokyo.ac.jp
Abstract Social network services (SNSs) have become new global and multilingual information platforms due to their
popularity. In SNSs with content-sharing functionality, such as "retweets'' in Twitter and "share'' in Facebook, posts are easilyand quickly shared among users, and some grow into large information cascades. Accompanied with such growth, cascades
can spread over regions and languages. The "ALS Ice Bucket Challenge'' is a good example of internationally influential
information that are widely spread and internationally reshared. Analyzing and predicting these influential cross-lingual
information cascades will help promote culture exchange and detect international news and issues. This type of research canalso provide insights into internal and external relationships among countries and languages. Though there has been a large
amount of research on information cascades, much has been focused on predicting just their growth, and little has been on
cross-region/lingual information cascades. In this work, we aim to build a robust model and detect influential cross-lingual
information diffusion on social networks. To the first attempt, we analyze and predict influential cross-lingual informationcascades on Twitter. With a large Twitter dataset, we conducted statistical analysis of growth and language distribution of
information cascades. Based on the analysis, we propose a feature-based model to detect influential cross-lingual informationcascades and successfully predict their size and language distribution.
Keyword Information cascades; cross-lingual cascades; cascade growth; multilingualism
into other languages. As a result, this event attracted many

1. Introduction
Socialnetwork

services(SNSs) havebecomeanimportant part of our daily life due to their widespreadparticipants and increased donations for ALS patientsworldwide.

adoption. Take Twitter as an example. As of June 2016,

Another example is "Oscars selfie" in 2014, which wasthere were 313 million monthly active users and more thanposted by show host Ellen DeGeneres on her Twitter

40 languages supported in Twitter. Other popular social

account. It became the most retweeted message of all time

media such as Facebook and Goo gle, have millions of[11]. People reposted and imitated this photo, diffusing it

monthly active users and support many languages as well.

across regions and languages at amazing speed and size.

There is no doubt that these SNSs have become moreAt the same time, host Ellen DeGeneres's selfie, taken

global and multilingual.

during the broadcast on a Samsung smart phone affectedWith easy access and less limitation, SNSs have become

the Samsung's global marketing.

a new kind of information platform. On Twitter, posts

As shown in those examples, accompanied with growth,reshared by users easily and quickly with retweeting andinformation is propagated across languages and regions.

mentionAnalyzing

functionality

and

some

grow

into

large

and

detecting

these

types

of

influential

information cascades. Accompanied with cascade growth,cross-lingual information diffusion will he lp detect world

hot topics and events have propagated across languages

issues and social problems. Use cross -lingual informationand national borders. Social netwo rk analysts were among

diffusion may contribute to philanthropy and global

the first to show that the social network approach made itmarketing. While a large amount of research has beenpossible to follow social ties as they crossed space andfocused on analysis and prediction of cascade growth,language.

little has been on cross-region and cross-lingual cascades.

The "ALS Ice Bucket Challenge", one of the hottest

The goal of this study was to detect influential

topics in 2014, was an activity involving dumping across-lingual information cascades that will spread widely

bucket of ice water on someone's head to promoteand be reshared internationally in an early stage. To theawareness of the disease amyotrophic lateral sclerosis

best of our knowledge, this is the first study on(ALS), encouraging donations to research. It went viral on

cross-lingual information diffusion on a large scale andsocial media from July to August 2014. The hashtag of the

propose a prediction model of cross-lingual cascades.

ice bucket challenge was used worldwid e and translated

The rest of this paper is organized as follows. We

introduce related work in Section 2, and describe technicaldifferent language communities [4]. When users do crosspreliminaries based on a large Twitter dataset and define

language communities, it was suggested that these users

influential cross-lingual information cascades in Section 3.

will engage in larger languages, particularly English [6].

We discuss our basic statistical analysis of information

These

cascadesmultilingualandcharacterizecross-lingual

information

studies

inspire

users

us

may

that

large

contributelanguagestocascades in Section 4. In Section 5, we propose ourinformation diffusion.

feature-based prediction model to predict influential

3. Definition of Cross-lingual Cascadecross-lingual cascades. Finally, we discuss future workandcross-lingual

Twitter allows convenient functions, such as retweetingand conclude the paper in Section 6.

and mentioning. Retweeting is typically used to spread

2. Related Work

information

2.1 Information Cascade

Mentions in the form of @username allow Twitter users to

The popularity of online SNSs has resulted in the
problem of large-scale information diffusion [9]. One ofreceivedfromfollowees

tofollowers.

refer to a specific user.
Content is shared easily by users using retweets andthe most widely studied research topics is information

mentions.

cascades.

The

contagious

process,

in

which

users

cataloged

reproduce content after having contact with the content

properties of information cascades [9][10], while others

influence new users to do the same, is defined as a cascade.

considered predicting the speed, size, and structure of

We define information reshare and information cascades

cascade growth [1][3].

as follows.

Someresearchers

analyzed

andFrom the empirical analysis of information cascades on

DEFINITION Information reshare: This refers toSNSs, some common properties can be observed. Most

retweets and mentions. If user u j retweeted or mentioned acascades are small [10] and usually occur in a short period

tweet of user u i , user u j is called a resharer. Accordingly,of time [9]. Based on cascade properties, researchers havethe retweet or mention is called the information reshare(orattempted

just reshare).

to

predict

the

size

of

cascades.

Many

researchers considered the cascade prediction task as a

DEFINITION Information cascade: This consists ofregression problem [1][8] or a binary classi fication

reshares. A set of all subsequent reshares star ting from the

problem [8]. One widely used approach to predicting

root node that originally create the content is considered

cascade size was the feature-based method. Researchers

as an information cascade(or cascade) and the number ofextracted an exhaustive list of potentially relevant features,

reshares in one information cascad e is defined as cascade

including content, original poster, network-structural, andsize k. We simply define large cascades as influential

temporal features [3]. Then different learning algorithms

cascades.

were applied to predict cascade size. The language

Since SNSs are global and multilingual platforms, we

distribution of cascades is seldom explored .

can access all types of content in different languages.

2.2 Language Community

When reshare occurs, resharers may share the content inWith the globalization and multilingualism of SNSs,less frequently used languages or translate them into theirlanguagenative languages. This phenomenon results in information

distribution and multilingualism in global SNSs [6][4].

diffusing internationally. As a matter of fact, reshares withSocial network services are international in scope, but not"retweet" or "share" functionality copy the content of theas multilingual as they should be [5]. Distance androot, thus; not changing the language of the content. We

language serve as barriers in social communication [5][7].

defineThis leads to networks having many clusters or groups ofcascades based on the main language of users.

severalrecentstudieshave

examinedmonolingualandcross-lingual

informationindividuals with the same language called language

DEFINITION Monolingual information cascade: If thecommunities [7]. Most content is only shared withinmain language of all resharers in a cascade are the same ascommunities.

that of the root user, the cascade is called a monolingual

Some researchers analyzed the role of multilingual

information cascade.

users [4][6] and languages [6][7] in language communities.

DEFINITION Cross-lingual information cascade: If a

Social network analysis of multilingual users indicatescascade contains a resharer whose main language differsthat multilingual individuals could help diminish thefrom that of the root user, the cascade is considered asegmentation

cross-lingual information cascade. Accordingly, languageofinformation

spheresbyconnecting

distribution of each cascade refers to the number of eachmain language of resharers in one cascade. The p roportionof cross-lingual resharers in a cascade is defined as the
cross-lingual ratio.

4. Analysis of Cross-lingual Cascade
While there have been many studies to observe theproperties

includingsize,

speed,

andstructureofinformation cascades and draw ou t the factors behindcascade growth, there has been very less research toanalyze the language usage of information cascades. In
order to observe and analyze the language distribution of
information cascades on social networks, we choose
Twitter as our data source.
Twitter is one of the most global and multilingual SNSs

Figure 1 Distribution of final cross-lingual ratio

and its data are publicly available through its API. We
have crawled more than 2 billion tweets and 1.5 million

Similar to the analysis of cascades growth [3], weusers from March 1 to July 5, 2014. Then we identified theobserved the correlation between the final cross-lingual

language of each tweet using the Language Detection API

ratio f(r) and cross-lingual ratio r of the first observed kdeveloped by Shuyo, which is 99% accurate for 53

resharers. We found that the median value of f(r)languages. We used tweets from March 1 to May 31 tolinear relationship (0.9 times) with the r of the first 10

analyze the profile of users and those from June 1 to July 5

resharers as shown in Figure 1. Even if we observe more k,

to observe the properties of information cascades.

the median value of f(r) shows a linear relationship with

4.1 Properties of Cross-lingual Cascade

the r of the observed k resharers. Only about 20% of

had a

We extracted 74 million information cascades from Junecascades would exceed the value of r after observing k

1 to July 5, 2014. The size of information cascades follow

resharers. It means that just keeping r over time is quiteadifficult. Our target of prediction is whether f(r) exceeds

heavy-tailed

distribution.

The

large

informationcascades are quite rare and only 2% of cascades consisted

the first observed r.

of more than ten reshares, as proven in previous studies

4.2 Factors behind Cross-lingual Cascade[3][9]. We also investigated how soon a reshare appears

Feature-based method is one of widely used approaches

and an information cascade grows. By observing theto predict cascade size. In order to detect cross-lingual

duration of each reshare in June, we found that 94% ofcascades, we considered several language related factors

reshares occurred within 1 day. For each cascade, weof root users and their neighbors.

investigated the speed of cascade growth and found that

Effect of Root Users’ Main Language

98% of cascades grew within 1 week and tended toLarge languages such as English can serve as bridgestabilize after one week. Then we filtered out about 1

among language communities [6]. The main language of

million cascades with the final cascade size f(k) larger

root users could be a factor affecting cross-lingual

than 10 and calculated the final cross-lingual ratio f(r) of

cascades. For different main languages of root users, weeach cascade during one week.

investigated the frequency of cascades with di fferent

We investigated the correlation between cascade size

range of f(r).

and cross-lingual ratio of cascades. We grouped theAs our assumption, English root users have morecascades into the same final cascade size f(k) andcross-lingual cascades than monolingual cascades andcalculated the mean value of final cross-lingual ratio f(r).

cascades with higher f(r) are less. Most cascades from

As a result, we found the distribution of cascade size andJapanese, Arabic, and Thai speakers are monolingual.

cross-lingual ratio is independent, and we need to detect

Those of root users who speak European languages,

influential

cross-lingual

predicting

theseparately.

cascadecascadesbyIndonesian tend to be more cross-lingual. We assume thecross-lingual

ratiomain language of root users affects the cross-lingual ratio

information
size

and

of their cascades.

Effect of Multilingual Root Users

contained topics related to World Cup 2014 in English and

We investigated the effect of multilingual users on

Europeanlanguages.

The

top

languages

used

in

cross-lingual cascades. Since multilingual users may

monolingual cascades were English, Japanese and Arabic.

belong to several language communities, they have the

The analysis of root tweets indicates the languages andpotential to propagate information across languages.

topics of root tweets are also important for cross-lingualDue to the difficulties in language detection for short
text, one user is considered to use a particular language
when the usage rate of a language is at least 20% and morecascade prediction.

5. Prediction of Cross-lingual Cascade
Detectinginternationallyinfluential

meaningful

challenging.

information

than four tweets are in that language. A multilingual user

cascadesuses two or more languages. Among all users in oursimplified this task as a classification problem to predict

dataset, 8% were considered multilingual users. The usage

cascade size and cross-lingual ratio of cascades. Then we

rate of languages other than the main language is defined

extracted several features and used machine learning toas the multilingual ratio of the user.

testify the performance of our feature-based model.

Wegroupedthecascades

based

on

root

users'

is

and

We

first

5.1 Problem Definition

multilingual ratio and calculated the average of f(r). The

According to previous research [3], we de fine themultilingualism of root users has a positive relation to thecascade size prediction task as a binary classi ficationf(r) of their cascades, and cascades from multilingual root

problem to predict whether the final cascade size f(k) of ausers tend to be cross-lingual.

cascade reaches the median size during one week after

Effect of Multilingual Neighbors of Root Users

observing the first k reshares of that cascade. For

Even though some root users are not multilingual, theirdetecting influential cascades, we also consider othertweets can also be cross-lingual and influential if they areclassification problems to predict whether the f(k) reaches

internationally famous with followers worldwide. To

a specialized size such of 100, 500 or 1000.

discuss the influence of international popularity of users

For predicting cross-lingual cascades, we predicted theon cross-lingual cascades, we analyzed a directed reshare

final cross-lingual ratio f(r) of cascades. We de fine thegraphcross-lingual

extracted

fromusers'

previous

reshares

and

ratio

prediction

task

as

a

binarydetermined their monolingual and multilingual neighbors.

classification problem to predict whether f(r) exceeds the r

Monolingual neighbors refer to neighbors (followers/of the first k reshares in one week. As shown in Section

followees) who share one dominant main lan guage and3.2, the fraction of such cascades is only 20%. We

multilingual neighbors refer to neighbors (followers/

evaluated the performance of our prediction model byfollowees) who share more than one language and theadjusting the task to predict higher f(r) from lower r.

proportion of the second language is larger than 0.2. The

For the influential cross-lingual cascade prediction task,

multilingual ratio of neighbors is defined as the proportion

we considered the multi-classification problem to predictof languages other than the dominant main language which

both the size and cross-lingual ratio of cascades. To

reflect the internationality of the user.

simplify the evaluation, we define the task as a binary

We investigated the average f(r) of root users whoseclassification problem based on whether f(k) will reachneighbors were monolingual and multilingual. Cascades

the threshold value and the f(r) will reach r of the first kfrom root users with higher multilingual ratio of neighbors

reshares during one week.

had higher f(r). As a result, multilingual followers, which
represent the international popularity of root users, had
higher f(r).

Effect of Content of Root Tweets
The content or the topics of tweets may be considered
an important factor affecting cross-lingual cascades. We
extracted frequently used words of cascades with di fferentf(r) and in different languages. For instance, for cascadeswith f(r) larger than 0.8, the main languages were Korean
and Thai containing keywords related to famous Korean
singers and stars. Cascades with f(r) from 0.2 to 0.7,

5.2 Feature Extraction
We now describe the features for prediction. Many
researchers have proposed several features of the root andthe first k nodes to predict cascade growth. Combined withthe features proposed in a previous study [3], we designed
several novel language related features according to theanalysis in the section 4. Finally, we grouped the features
into six types: root-user, resharer, content, structural,
temporal, and language features.
To deal with multilingual content data, we trained a
topic model based on Wikipedia articles. Multilingual arti-

Table 1: Results of influential cross-lingual prediction

from the accuracy, precision, recall, and F-score on thetask after observing 10 resharers

test set. The baseline classifies all cascade to reach thethreshold. The overall performance of our feature-based
prediction model for the final cascade size f(k) and the
final

cross-lingual

ratio

f(r)

prediction

tasks

after

observing 10 resharers is shown in Table 1. All the threetasks performs better than the baseline.
To illustrate the general performance of the features, we
contrasted the performance of each feature separately. As
shown in Figure 2, language features were significantly
better than other features. By correlation coe fficient
analysis, we found that the multilingual ratio of users'
neighbors was the most significant feature. It was
followed by the multilingual ratio of the root user and k
resharers. Among content features, we found some of the
topics, such as music and movies, resulted in cross-lingual
information cascades.
We examined the sensitivity of prediction performanceto the thresholds of cross-lingual ratio. We chose cascadeswith r less than or equal to 0.1, and predicted theperformance of our mode when changing the thresholdvalue (0.1 and 0.3). Our model performed far better thanFigure 2: F-score of f(r) prediction for each feature

the baseline, even when the threshold was 0.3. We
extensively examined how the prediction performancecles were grouped into one document by using thechanged as more resharers observed. Our model showedinter-language link2 of articles and modeled using thebetterLatent Dirichlet Allocation(LDA) topic model [2]. We

performance of the cascade size prediction was slightly

specified the topic number as 200 and inferred theimproved as k increased.

probabilities of topics for each tweet by using this

6. Conclusion

multilingual LDA model.
Languagefeaturesprediction

performance

regardless

of

k.

The

We analyzed and detected growing large cross -lingual

contained

the

main

language,

information cascades on Twitter. It was the first to define

multilingualism, multilingual ratio of the main language,

theand language distribution vector of the root user and kespecially with high r were rare and keeping r over time

resharers. For k resharers, we calculated the ratio of

was quite difficult. Based on analysis of properties andmultilingual resharers and their multilingual n eighbors.

factors behind cascades, we proposed a feature-based

We also computed the average language distribution

model to predict the size and the cross-lingual ratio of

vector of their tweets and that of their neighbors.

information cascades.

5.3 Experimental Results

cross-lingualcascade.

Cross-lingualcascades,Though the features extracted in our study performed

We extracted 1.4 million cascades larger than 10 frombetter than the baseline, more detailed importance-

June 1 to July 5, 2014. As a training set, we randomly

performance and error analysis for each feature is required.

sampled 300,000 cascades, the root tweets of which appear

As mentioned in related studies, feature extraction andduring June 1 to 21. As a test set, we sampled 100,000

extensive training are crucial for this approach, andcascades, the root tweets of which appeared from June 22

performance is highly sensitive to the quality of theto 28. User and reshare graph features were extracted fromfeatures. Predicting growing cross-lingual cascades is justMarch 1 to May 31. We used a linear support vector

the primary stage in detecting influential cross-lingualmachine model to conduct the experiments. We trained

information diffusion. For future work, we have toclassifiersconsider cascade clustering and information flow between

on

the

training

set

using

10-fold

crossvalidation and evaluated the performance of our model

language communities.

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