Design and Multilingual Users on Twitter and Wikipedia

Design and Multilingual Users
on Twitter and Wikipedia
Scott A. Hale
scott.hale@oii.ox.ac.uk
http://www.scotthale.net/
Oxford Internet Institute
University of Oxford
17 June 2014
Scott A. Hale Design and Multilingual Users

Importance of design

Content is diverse across languages
“multilingualism...[is] the norm for most of the world’s societies” (Birner,
2005), with over half of Europe and over a ﬁfth of the US multilingual
(Erard, 2012); yet, many platforms are designed only with monolingual users
in mind.
In a Uzbekistan survey, Internet users reported accessing content in
foreign languages even while simultaneously reporting poor foreign
language skills (Wei & Kolko, 2005)

Content is diverse across languages
“multilingualism...[is] the norm for most of the world’s societies” (Birner,
2005), with over half of Europe and over a ﬁfth of the US multilingual
(Erard, 2012); yet, many platforms are designed only with monolingual users
in mind.
In a Uzbekistan survey, Internet users reported accessing content in
foreign languages even while simultaneously reporting poor foreign
language skills (Wei & Kolko, 2005)
Users often contribute local content/knowledge (Hecht & Gergle,
2010a)
Large diversity in information between languages (Hecht & Gergle,
2010b)
Can lead to self-focus bias (Hecht & Gergle, 2009)

Motivations
Language clustering vs. small-worlds
Users thought to cluster by language in most online platforms (Barnett
& Choi, 1995; Hale, 2012a, 2012b; Herring et al., 2007; Nordenstreng
& Varis, 1974; Takhteyev, Gruzd, & Wellman, 2011; Wilkinson &
Thelwall, 2012)
Many online platforms thought to exhibit the ‘small-world’ phenomenon
of small path lengths between users (despite high clustering)

Motivations
Language clustering vs. small-worlds
Users thought to cluster by language in most online platforms (Barnett
& Choi, 1995; Hale, 2012a, 2012b; Herring et al., 2007; Nordenstreng
& Varis, 1974; Takhteyev et al., 2011; Wilkinson & Thelwall, 2012)
Many online platforms thought to exhibit the ‘small-world’ phenomenon
of small path lengths between users (despite high clustering)
Role of multilingual users
⇒ If users cluster by language and platforms are small-worlds, there must
be brokers bridging diﬀerent language groups (spanning structural
holes)
Multilingual users are possible bridge users. Only one study
investigating this: Ego-net level study on Twitter following–follower
network structure (Eleta & Golbeck, 2012).
No study multiplatform study, no study at large-scale level

Outline
What are the roles of multilinguals and platform design in shaping the
spread of information in social media?
Twitter and Wikipedia at a global level
1 Language will have strong role in structuring the platform
2 Users engaging with content in multiple languages (multilingual users)
serve as bridges between diﬀerent clusters/editions
3 Users primarily writing in less-represented languages will be more likely
to cross-language boundaries than users writing in highly-represented
languages
4 When users cross languages they will cross to larger languages (e.g.
English) and thus at a language level English will form more bridges
than other other languages

Data
Twitter
Twitter mentions, retweet
network
18 days of ‘spritzer’ 1% sample
stream from June 2011
7,341,271 nodes. 8,545,693
directed, weighted edges
Wikipedia
Edits from top 46 language
editions
8 July to 9 August 2013
3.5 million non-minor edits by
55,568 registered users
Global Connectivity and Multilinguals in the Twitter Network (2014).
http://www.scotthale.net/pubs/?chi2014
Multilinguals and Wikipedia Editing (2014).
http://www.scotthale.net/pubs/?websci2014

Twitter: Data cleaning
Language classiﬁcation
Clean text of tweets for language
detection (remove urls,
usernames, emoticons)
Use Chromium Compact
Language Detection kit for
language detection (Graham,
Hale, & Gaﬀney, 2013)

language detection (Graham et
al., 2013)
Remove users with less than 2
tweets or 20% of the user’s
tweets in one language
Remove users with less than four
tweets total

al., 2013)
tweets total
Bots and spam users
Remove users with no mentions
(indegree=0)
Select only the largest
weakly-connected component
(88% of nodes)

al., 2013)
tweets total
Bots and spam users
Remove users with no mentions
(indegree=0)
Select only the largest
weakly-connected component
(88% of nodes)
End result
916,836 nodes (users) and 2,652,618
directed edges (mentions/retweets)
Each user assigned most used
language and frequency [0-1] that
the most used language is used

Wikipedia: Data cleaning
Non-minor edits by registered, human users to articles
Only edits to main (article) namespace
Removed articles ﬂagged as being created by ‘bots’
Removed anonymous users
Removed undeclared bots and users with only one edit session in the
month
Require at least four edits and at least 2 edits to one edition
Matching users and articles across languages
Look for common usernames across language editions
Check usernames are indeed linked global accounts
WikiData dump to match articles across languages
55,568 users (excluding Simple English edition) with a total of 3,518,955
edits.

User counts
Twitter
Language User Count
English (en) 375,474
Japanese (ja) 137,263
Portuguese (pt) 133,501
Malay/Indonesian (ms) 106,223
Spanish (es) 70,246
Dutch (nl) 31,035
Korean (ko) 16,123
Thai (th) 8,629
Arabic (ar) 7,679
French (fr) 5,769
Filipino/Tagalog (ﬁl) 5,393
Wikipedia
Language User Count
English 22,412
German 4,920
French 3,430
Russian 3,330
Spanish 3,299
Japanese 3,164
Italian 2,202
Chinese 1,975
Portuguese 1,220
Polish 1,011
Dutch 1,007

Twitter: Multilinguals vs Monolinguals
On Twitter, 11% of users (˜103,000) were observed to use more than one
language and designated as multilingual users.
Multilingual vs. monolingual users: Comparison of tweet count, out-degree, and
in-degree.

Wikipedia: Multilinguals vs Monolinguals
On Wikipedia, 15.4% of users
(8,544) edited more than one
language edition and were
designated as multilingual users.
Density plot compares the
number of edits made by
monolingual and multilingual
Wikipedia users. Size of edits
does not diﬀer signiﬁcantly.

Wikipedia: Multilinguals vs Monolinguals
On Wikipedia, 15.4% of users
(8,544) edited more than one
language edition and were
designated as multilingual users.
Density plot compares the
number of edits made by
monolingual and multilingual
Wikipedia users. Size of edits
does not diﬀer signiﬁcantly.
Only 2.6% of edits are from users
writing in their non-primary
languages on Wikipedia.

Twitter: Language and structure
Label propagation algorithm (Raghavan, Albert, & Kumara, 2007) found
20,253 communities.
Histograms of the size of communities (left) and the number of languages within
each community (right). Modularity score of 0.81 for this community structure.

Twitter: Language and structure
Scatter plot of community size and
the percentage of users in the
community most often using the most
prevalent language.

Language and structure
Most-used
language
% users
in most-used
language
Number of
languages
Number of
nodes
Malay (ms) 78.3 41 123,616
English (en) 99.3 39 114,826
Portuguese (pt) 94.3 40 101,987
Japanese (ja) 99.6 19 83,785
English (en) 75.7 44 80,387
English (en) 55.1 42 37,688
Dutch (nl) 90.6 23 20,634
Table Clusters with over 10,000 nodes found through the label propagation
algorithm. Collectively 61% of all users are in one of these clusters.

Twitter: Do multilinguals bridge clusters?
Size of the largest, weakly-connected component (left), total number of components
(center), and average size of the components (right) created by removing all
multilingual users, an equivalent number of monolingual users randomly, an
equivalent number of all users randomly, and removing all multilingual users from a
network with the same degree distribution but with edges randomly shuﬄed. Box
plots show values from 100 realizations. Mean values are indicated with +.

Wikipedia: Do multilinguals bridge editions?
Do multilinguals edit similar articles across languages?
A large number of users did not edit any of the same articles in their primary
languages, but a large number of users also always edited the same articles in their
primary languages.

Variations by language
Twitter Wikipedia
Number of users in each language compared to the percentage of these users
classiﬁed as multilingual.

Twitter: Cross-language connections
ar
de
en
es
fil
fr
gl
it
ja
koms
nl
pt
th
Mentions and retweets across
languages
Nodes represent most-used
language
Directed, weighted edges show
the log of the number of users
primarily using one language who
mention / retweet users in
another language
Only edges with weights over
1.96 standard deviations above
the mean are shown
Colors indicate communities
found by the infomap community
detection algorithm
N.B. This diﬀers from the published paper where edges were normalized by the expected number of connections between language
pairs if tweets were directed at users randomly without regard to language.

Wikipedia: Language crossings
ar
bg
ca
cs
da
de
en
es
fa
fifr
he
hu
id
it
ja
ko
nl
no
pl
pt
ro
ru
sv
tr
uk
zh
Co-editing network graph
Nodes represent language
editions
primarily editing one language
edition who edited another
edition
the mean are shown
detection algorithm

Wikipedia: Language crossings (English removed)
ca
cs
de
es
fr
it
ja
nl
pl
pt
ru
sv
uk zh
Co-editing network graph
Nodes represent language
editions
primarily editing one language
edition who edited another
edition
the mean are shown
detection algorithm

Summary and Implications
Multilingualism correlated with
activity on both platforms
Design for multilingual users
Allow users to have multiple
preferred languages when
personalizing search results,
friend recommendations, etc.

Structured by language
Language has a strong role
structuring both platforms
Multilingual users in position to
bridge clusters/editions, but
mixed evidence on actual role
Multilingual user percentage ∝
1/self-focus bias

1/self-focus bias
Important per language variations
Users in less-represented languages
more likely to cross-language
boundaries on Wikipedia, but no
correlation on Twitter.
Platform diﬀerences?
Consistent ﬁndings of English
and Japanese as outliers

1/self-focus bias
Important per language variations
Users in less-represented languages
more likely to cross-language
boundaries on Wikipedia, but no
correlation on Twitter.
Platform diﬀerences?
Consistent ﬁndings of English
and Japanese as outliers
Larger languages form bridges
Especially English, but
Other geolinguistic patterns
evident
Global connectivity results
through the combination of
multilinguals across many
language pairs

Design and Multilingual Users
on Twitter and Wikipedia
Scott A. Hale
scott.hale@oii.ox.ac.uk
http://www.scotthale.net/
Oxford Internet Institute
University of Oxford
17 June 2014
I would like to thank Eric T. Meyer, Taha Yasseri, Jonathan Bright, and Mike Thelwall
who provided helpful comments on various aspects of this research.

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