DEIM Forum 2016 B2-5

Analysis of Growing Cross-lingual 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

Recent years, online social network such as Twitter, Facebook has become an indivisible part of our daily life.

As a result of easy access and globalization, information propagated quickly and even cross the regions and languages. This
paper aims to study the growing cross-lingual information cascades on Twitter. First, we studied 615 million tweets anddetected the language distribution of tweets and multilingual users. Then, we measured the size and languages of informationcascades and analyzed several features may influence information cascades.
KeywordInformation diffusion, information cascades, cascade growth, cross language

1. Introduction
Social network services have become an importantpart of our daily life. Take Twitter as an example, byMarch 2015, there are 302 million monthly active
users posting 500 million tweets every day. Also,
77% of the accounts are outside the United States andover 30 languages are supported in Twitter [18].
Similar to Twitter, other popular social medias suchas Facebook and Google, have millions of monthly
active users and support many kind of languages aswell. There is no doubt that these social network
services have become more global and multilingual.
With easy access and globalization, Social network
services have become new kind of information
platforms. On Twitter, contents are shared by users
easily and quickly with retweeting and mentionfunctionality and some grow to large informationcascades. Accompanied with the cascade growth,
there are many hot topics and events propagatedacross the language and national borders.
"Ice Bucket Challenge", one of the hottest topics
in 2014, was an activity involving dumping a bucketof ice water on someone's head to promote awarenessof the disease amyotrophic lateral sclerosis (ALS)
and encourage donations to research. It went viral onsocial media during July–August 2014. The hashtagof ice bucket challenge was used all over the world
and translated into other languages as well. As aresult, this event attracted many participants andincreased donations for ALS patients all over theworld.
Another example is "Oscars selfie" in 2014, which
posted by show host Ellen DeGeneres on her Twitter

account. It became the most retweeted message of all
time. People reposted and imitated this photo,making it diffused cross regions and languages atamazing speed and size. At the same time, host Ellen
DeGeneres's selfie, taken during the broadcast on a
Samsung smart phone, resulted to the global
marketing effects for Samsung company.
As shown in these examples, accompanied with thegrowth of contents, information is propagated crosslanguages and regions. In addition, behind theseinformation diffusion, there exists social influencesuch as beneficence and commercial. Detecting andanalyzing these kind of cross-lingual information
diffusion will help to find world news and somesocial problems. And if we can make use of thesecross-lingual information diffusion, it may contributeto beneficence and global marketing. While bunch ofresearch focused on analysis and pre diction of thesecascade growth, little research is about cross-region
and cross-lingual cascades. The goal of this work is
to understand and analyze the languages of users,

tweets, retweets and mentions. We analyze thefactors to influence the cascade grow and crosslanguages.
The rest of this paper is organized as follows.
Section 2 introduces related work and section 3
describes our data crawling and language detection
methodology. We conduct basic statistical analysis of
information cascades and study the factors behindcascade growth in Section 4. In section 5 we definecross-lingual information cascades and analyze thefactors behind them. Finally, Section 6 concludes thiswork and future work.

2. Related Work
The widespread adoption of online social network
services opens a new problem of large -scale
information diffusion [11]. Many papers haveanalyzed and cataloged properties of informationcascades, while others have considered predicting the
speed, size and structure of cascade growth [11].
Many studies consider the cascade prediction task asa regression problem or a binary classification
problem. However, they never considered about thelanguage change during information diffusion.
With the globalization and multilingualism ofsocial network services, recent research has studied
language distribution and multilingualism in global
social network services [1, 3, 9]. Multiple languages
are used in global social network services and on
Twitter, only half of the tweets were in English [9].
Hale [1] found 11% of users is multilingual users,
who use more than two languages in social network.
Social network services although international inscope, is not as multilingual as it might be. It is clearthat languages serve as barriers in information
diffusion [8]. However, we can observe thecross-lingual information diffusion as well.
Some papers analyzed the role of multilingual
users [1, 2] and languages [1, 7, 8] in language
communities. The social network analysis ofmultilingual users indicate us that mu ltilingual
individuals could help diminish the segmentation of
information spheres online by connecting different
language communities [2]. When users do crosslanguages, [1, 8, 14] suggested these users will
engage in larger languages, particularly English.
However, previous research did some static analysisof language distribution and language communities,
little research is about dynamic language analysis of
information diffusion which will be introduced inthis paper.

3. Data Collection and Language Detecti on
3.1. Data Collection
Twitter is one of most global and multilingualsocial network services and data is publicly available
through API. We collected 20.5 million tweets perday, representing 6-7% of all public messages overthan 8 years. In the beginning, we collected andbroadened the users and tweets from the retweets andmentions of 30 famous Japanese users and their
tweets. In this work, we analyzed 615,327,985 tweets

and 1,442,263 users from Twitter over one month
period in June, 2014. According to previous research,
Japanese users and tweets seldom share with people
or information in other languages [1, 3]. In our work,
we want to testify this assumption as well.

3.2. Language Detection
Tweet Language Detection
We identified the language of each tweet using
Language Detection API [16]. Because l anguage

identification is difficult on such short text [4],Urls, hashtags, and mentions were temporarily
removed from the text of tweets for language
detection following the recommendations of Grah am,
et al. [17]. Also we removed the text containing lessthan 20 characters and it only cut down 0.8% tweets.
We identified 54 languages from the 61 0 million
tweets.
User Language Detection
We detected the languages for each users bystatistic of their language usage of tweets. Main
language of users were defined by the mostfrequently used language in their tweets. Table 1
shows the languages of tweets and main language ofusers, ordered by decreasing number of tweets.
Language
EnglishJapanese
ArabicSpanishFrenchIndonesian
Thai
Portuguese
KoreanOther
Unknown#Tweets
203662130
169298415
47981804
30706241
24374187
17212893
2097036510889569
890474575821484
5506125

%
33.1
27.5
7.8
4.99
3.96
2.8
3.41
1.77
1.45
12.3
0.89

#Users553793413907151929
73088
63848
55296
30719
14243
17691
64573
3176

%
38.4
28.7
10.5
5.07
4.43
3.83
2.13
0.99
1.23
4.48
0.22

Table 1 Number of tweets in different languages andnumber of users with different main languages inTwitter
Owing to the different method to collect the data,
the frequency distribution of each language is a little
different from previous research [1, 2]. However, thetop 10 languages are in line with previous research [1,2]. In another word, our dataset is quite global andmultilingual despite the higher frequency ofJapanese.
On Twitter, users can write in many languages andmultilingual users are defined as the users who use
more than two languages. Given the difficulties withshorter text it is useful to establish a threshold unde rwhich the detection of a language is more likely
classifier error than authentic use of the language.
For this study, a user was considered as amonolingual user when the proportion of usage ofmain language in all tweets of this user is at least
80%. U sers who use two or more languages in their

tweets and usage rate of main language is less than
80%, was classified as a multilingual user. Users
with less than four tweets were excluded entirely to
avoid having any users in the sample withinsufficient data to determine if they are monolingual
or multilingual in their Twitter usage. We conducteda human-coding study of a random sample of 100
tweets, and found a substantial agreement between
human judges and the language detection algorithm.
Figure 1 shows cumulative distribution function ofusers' usage rate of main language. The mean valueof usage rate of main language is 0.908. Among allu sers, 17% users meeting our requirement and are

In our study, if user i retweeted or mentioned thetweet of user j, user i is called as resharer and user j
is called root user. Similarly, the retweet or mention
is called reshare and tweet of a root user is call ed
root tweet. A set of root tweet and reshares is
considered as an information cascade and the numberof posts in an information cascade is the cascade size.
We sampled the information cascades with cascadesize. Figure 2 shows that cascade size follows aheavy-tailed distribution. Large scale informationcascades are really rare. 94% of cascades are
consisted of less than 5 reshares. In other study,
cascades with low cascade size are meaningless andwe sample cascades with minimum cascade size 10.

considered to multilingual users. When apply thesame criteria of multilingual users with Hale's
work, the proportion is similar to [1].
Monolingual Users

Multilingual Users

Figure 1 Cumulative distribution function of users'
usage rate of main language.

Figure 2 frequency of Cascade with different cascadesize
Speed of cascade growth
Information cascades do not grow forever nor stop
growing anymore. Found in previous research[11],half of retweeting occurs within an hour, and 75%
under a day. However, about 10% of retweets takeplace a month later. In our work, we observe durationof each reshares and investigate the speed of cascade
growth during one month. In figure 3, we can find
94% of reshares occur within 1 day and 98% of thecascades grows within 1 week.

4. Analysis of Cascade Growth
4.1. Information Reshares and Cascades
Twitter allows kinds of convenient conventions.
Retweeting is typically used to spread information
received from followees to follower s [15]. A common
form of retweeting is “RT @username message”,
where “message” is a tweet created by “username”.
Mentions in the form of @username, allow Twitterusers to refer to a specific user. A reply, a specificform of mention with @username appearing at the
beginning of the tweet, is a tweet responding to aprevious message.

Figure 3 Time duration of reshares and temporal
growth of a cascade
Final size of cascade growth
According to previous results, informationcascades grows 98% within one week. So in our work,

final cascade size f(k)we define one week as information growth durationand final cascade size within one week as final size
f(k). Similar to the previous work[19], we observe
the first k reshares of a cascade and observed thefinal size f(k) with one week. Figure 4 shows the
distribution of f(k) with different k reshares. We canfind k and f(k) follows linear relations. The mediansize of final size f(k) is about 1.5 times of k andmean size of f(k) is 2.5 times of k.
300
250
200

Median
Mean

150
100
50
0
10 20 30 40 50 60 70 80 90 100#observed reshares (k)Figure 4 distribution of final cascade size f(k) of
observed reshares k

4.2. Factors of Cascade Growth
From the speed and final size observation ofcascade growth, we can find most of cascades growquickly in the first day and tend to stable after one
week. Also, half of cascades will reach 1.5 times
after first k reshares. Here we try to find what factors
will influence the cascades growth.
Previous work attempted on several features ofroot node and first k nodes, containing content
features, root and resharer features, structuralfeatures and temporal features [19]. In this study, we
testify the basic features of root node and resharers.
Here we define activity and influence feature foreach user. Activity is defined by the number of prior
tweets. Influence is defined by the number of priorreshared tweets. And we define the average number
and max number of root user and first k users'
activity and influence as cascades' features. Also we
add the features of root tweet, containing urls,
hashtags, mentions and text length. From correlation
coefficient analysis, we find the average activity(0.1113135), average influence (0.106363019) andtext length (0.1075459) features have positive
relation with cascade growth. However, the value isvery low. More feature selection and analysis will be
the future work.

5. Analysis of Cross-lingual Cascades
5.1. Cross-lingual Reshares and Cascades
In an information reshare, if the main language ofthe resharer is different to the language of the root
user, it is considered as a user-wise cross-lingual
reshare. And if the language of the reshare differsfrom the root tweet, it is defined as a tweet-wisecross-lingual reshare. Otherwise, it is called as amonolingual reshare. Table 2 shows the number ofreshares between language pairs.
From the table 2, we can find there is no doubt thatthe monolingual reshares are more than cross -lingual
reshares. However, between different language pairs,
the frequency of reshares are different. It shows thedifferent correlation between language pairs. For
instance, Spanish has a tighter relationship withEnglish than others. On the country, Arabic has lessrelations with Asian languages.
Cross-lingual information cascades are thecascadescontaining
tweet-wisecross-lingual
reshares or user-wise cross-lingual reshares. In other
word, In this information cascade, there is languageof at least one reshare differs from the root tweet's
language or the main language of the resharer differsfrom root user's. Monolingual information cascades
are defined as the cascades which do not contain any
tweet-wise cross-lingual reshares or user-wise crosslingual reshares. Cross-lingual ratio is the proportionof cross-lingual reshares in all reshares for one
cascade. By observing user-wise and tweet-wisecross-lingual cascades, we find the mean size ofcross-lingual ratio is only about 8% and most ofcascades are monolingual. What kind of cascades
will be cross-lingual cascades?

5.2. Factors of Cross-lingual Cascades
According to some previous research, multilingual
users and some larger languages can serve as thebridge between language communities. This section
studies on the factors such as languages of root users
and root tweets of cascade size large than 100 which
may result to the cross-lingual and user-wisecross-lingual information diffusion.
In order to find out the correlation between
language of root tweets and multilingual cascades,
we analyzed the cumulative distribution ofcross-lingual cascades for different language of root
users and root tweets.

EnglishJapanese

ArabicSpanishFrenchThai

Indonesian

KoreanEnglish18185747

33114

28325

66486

15582911229

117037

3317

Japanese

20714

6918259

489

284

444

733

540

1614

Arabic23368

136

3707047

606

1588

6

2889

10

Spanish

57542

439

763

2347730

38886

85

8405

57

French114400

623

1481

35705

1800400

195

12128

45

Thai

4472

366

15

37

119

1193952

127

233

Indonesian

82907

1005

1891

5738

8743

358

71407096

Korean

10001

4680

509

81

242

6038

356

357923

Table 2 Number of reshares between language pairs
Figure 5 shows the different distribution ofEnglish, Japanese, French and Korean root users'
cascades. We can find Japanese users' tweets are
more likely to be monolingual but Korean users'
tweets seem more cross-lingual in cascades withlager size. The cascades of French users' tweets arealmost consist of French as well. By manually
analyzing the topics of Korean users' tweets, we findthe topics of the cross-lingual cascades of Koreanroot users are closely related to K -pop andpropagated in Thai. The reason for this kind ofcross-lingual cascades resulted from the popularityof K-pop in Thailand. Here, we can find there is no
direct correlation between the size of languages andcross- lingual or monolingual information diffusion.
However, the topics of tweets may be the main
factors result to the cross-lingual information
diffusion.

Figure 5 CDF of user-wise cross-lingual ratio foreach language of root user.

6. Conclusion
In our work, we studied the language usage on
Twitter, especially related to Japanese users. We
observed information cascade growth and languagechange and analyzed the factors behind cascade
growth and cross-lingual cascades. Finally, we got
the following summarization.
（ 1） Based on a different dataset which collectedfrom Japanese users, the frequency of languages
differed from the previous research. However, the top
10 languages in our dataset is in line with previous
work. It indicates our dataset is global andmultilingual and suitable for cross-lingual cascade
analysis.
（ 2） On average, about 90% of tweets for a user
were posted in a single dominant language.
Multilingual users in social networks were less thanwe expected. It may result from the high thread holdof our definition or the insufficient dataset or justbecause of their behaviors using the social media.
（ 3 ） By analyzing the information reshares andcascades, we find that large cascades are rare and
98% of the reshares grows within one week. In
addition, after having observed cascades for one
week, half of the cascades grow 1.5 times. We
calculated the correlation coefficient between of thefactors and cascade size and found users' activity andinfluence has positive relation with cascade growth .
（ 4） Most of the information reshares and cascades
are monolingual and the mean size of cross-lingual
ratio is about 8%. We analyzed the relation between
root language, topic and cross -lingual ratio, but thefactors to influence the cross-lingual informationdiffusion are still unclear.
In our future work, we will aim to analyze morefactors behind growing cross-lingual diffusion andset up a prediction model for cross-lingual cascade
growth.

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