Good morning, everyone.
Thank you for coming here [unclear] of the semester.
So, I'm going to start.
Access to the internet is greater than ever before
and as a consequence, it's becoming more multilingual.
However, there's evidence of segmentation of cyberspace
due to language and national borders.
This image serves to illustrate that.
This is the language communities of Twitter in Europe.
So, what you can see are tweets geolocated over a map of Europe
and the different colors represent the different languages.
You can even see regional languages like Catalan in the Catalan region of Spain
And this is going to be useful for an example I'm going to use later.
I'm interested in Twitter in particular,
because of the speed of information dissemination
and that most of this information is publicly accessible.
I'm going to illustrate this with a capture
of a dynamic visualization you can find on the Twitter blog
by Miguel Rios.
And what you can see here is the global flow of tweets
after the earthquake in Japan.
In pink, there are the tweets coming out of Japan
and, in green, the retweets all over the world.
This illustrates that in Twitter information is spreading across countries.
But how can this happen?
Expatriates, migrants, minorities. diaspora communities, language learners
all play an important role in building transnational networks
and cultural bridges between nations and communities.
They are the multilingual users on the internet.
The overarching research question is:
how are multilingual users of Twitter connecting different language groups?
In 2009, the Berkman Center of Internet and Society at Harvard University
mapped the Arabic blogosphere
and they described a key concept for my research.
They discovered an English bridge and a French bridge of bloggers
that were writing in their native Arabic language and in English or French.
And they were connecting the different national blogospheres
with the international one.
This might have played a role in the Arab popular uprisings in 2011
for reaching out to the world.
And this is connected with a concept that first appeared in 2008
of the bridge bloggers.
So, bridge bloggers are bloggers
that are trying to connect their local communities
to a wider global audience.
The image you can see here is actually the visualization they created
of mapping the Arabic blogosphere.
Each dot is a blogger, or a blog.
The size represents their popularity, so how many incoming links they have
and they grouped them--
the neighborhoods they created
in relation to the linking between them.
So, the ones that are grouped together are linking among each other.
The colors are a different question.
The colors represent "attentive clusters", that's how they call it.
And they look at their online resources and media outlets
these blogs were linking to.
So, blogs of the same colors are following the same media outlets
and online resources.
And they did human coding to label those groups.
And here is where we see the label English grids
the responses from Cuba in English
and up there, there's [unclear] France.
And so I think it's important to retain the concept of attentive clusters.
Now, let's go back to 2011 during the Arab popular uprisings.
And I'll show you a visualization of the influence network
of Twitter users in Egypt.
So, what you're seeing here
just imagine people down the street at Tahrir Square
tweeting in Arabic about what's going on on the ground.
And those are the people in red.
So, these red dots represent users that are tweeting in Arabic.
Then we have the international community
or even Americans, British and so on tweeting in English.
And they are in blue, those blue dots.
And then, interestingly, we have people in between them.
which are illustrated in different degrees of violet, or violet shades.
This represents the fact that they are tweeting in both Arabic and English.
So, what we're seeing is the bridge Twitters
because, like Ethan Zuckermann called them "bridge bloggers".
So, another context.
The same year, 2011, a lot of big protests were going on in Europe.
And in particular, in Spain.
They started on May 15th 2011 there were massive protests.
And of because of this context, this situation
new attentive clusters were emerging in the social media landscape of Spain.
Now, this is a visualization you can find in the Socialflow blog, research blog
on social networks.
And what it is, is it tracks the origin and the initial spread
of the hashtag #occupywallstreet in Twitter.
They detected that one of the first users of the hashtag #occupywallstreet
was on July 13th 2011, linking to a blog post of Adbusters.
So you have the Twitter account of Adbusters there, very big
because it's being retweeted a lot.
And mentioned a lot.
And they collected these mentions and the tweets that had these mentions
and these retweets with the hashtag during July 13th.
From July 13th to July 23rd.
So, from the first 10 days of the use of this hashtag
it was from the very beginning of the use of this hashtag on Twitter.
They just mapped the accounts
and the series of posts with the hashtag and mentions with the hashtag
and the users that were connecting
because of these mentions and retweets.
Now the interesting thing in this visualization
is that they
the Socialflow people particularly in [inaudible]
detected this Spanish brand of users
were forming an attentive cluster.
Mentioning and retweeting about it in Spanish
using the hashtag in their messages in Spanish.
And they point out in the blog
that this Spanish contingent
helped post and spread the word about Occupy Wall Street
even before most of the United States was aware of it.
So, I found that very interesting.
And it was due to the context in Spain at that moment
with big protests and new clusters forming in the social media landscape.
Now I have shown you the importance of these multilingual users
in connecting language communities and spreading information
across countries, acting as mediators.
But let's focus on another aspect of connecting language groups
which is language choice.
So I'm going to devote a moment to speak about languages
and language choice.
To understand languages in the world
I'm going to use a telescope.
So de Swaan...
...proposed a theory called the world language system
back in the 1990s.
to explain the languages in the world.
And he used a very beautiful metaphor, the constellation.
So, in his theory there's about a dozen languages in the world
that are the hearts of the system, or the suns.
The suns of the system.
For instance, English, French, Spanish, Arabic and more.
And then there are hundreds, maybe more than 100, 200...
national languages that are orbiting around these suns like planets.
And finally we have regional and minority languages
that are orbiting these planets like satellites.
And he used this metaphor to explain the power relationships
between languages.
This is a theory of what he called
"communication potential and language competition"
A key point he made
is that the system holds together
thanks to multilingual people and interpreters.
This is what's providing cohesion to the system.
He also made a controversial proposal
about the communication potential of a language.
So, he proposed a formula, a mathematical formula
where he could estimate the communication potential of a language
and supposedly a person with tools through learning and usage
based on the communications of that.
For example, a person might decide to learn English and use English
because not only does it provide communication with English native speakers
but also, adding to that, it provides the possibility to communicate
with all the second-language learners of English
from many different languages, many different countries.
So, supposedly, in history
English provides the greatest communication.
And he received some criticism, because of the central role of English
in his theory
He said it was the central hub of all the system.
There's also the language ecology paradigm first proposed by Haugen in 1972
and there's this idea of an ecosystem of languages
and, again, it's using another metaphor
and because of this metaphor
also appeared the idea of endangered languages.
I'm going to briefly just read the definition.
He defined the language ecology as:
"the study of interactions between any given language and its environment"
and what I think is very important:
"language exists only in the minds of its users"
which leads me to point at my research.
In my research, I'm using a microscope to see the cells
and my cells in my study are the Twitter users.
Why is that?
Because as Haugen explains, there's a psychological dimension
to language ecology
where language interacts with other languages
in the minds of multilingual people.
And there's a sociological dimension to language ecology
where we use language to communicate and interact with other people.
And this language ecology generates because of the people
that decide to use that language
learning and interacting with people using it.
And this is the point of language choice in languages.
So, I focus on the connections of people and the language choice.
So, these are the four points I'm going to be speaking about.
But actually the main focus is going to be the first point
Social network analysis and the taxonomy of intersections between language groups
This is where I'm going to be spending most of the time.
And then very briefly, just for compilation purposes
I'm going to speak about another small study that I did
the factor analysis
looking at the influence of the social network
in the language choices of the users.
So, how the social network influences language choice
of our multilingual users.
And then I'm going to briefly also talk about the last study of my dissertation
that is still ongoing.
So, I still have new research to talk about.
And it's content analysis
and in this case I'm focusing on intrinsic factors
intrinsic to the messages
about the topic, and the type of exchange.
If it's a reply, if it's a public post
and how that influences the language choice as well.
And finally I will...
I'm going to give you my reflections
so I can invite your thoughts and suggestions and discussions about it.
Briefly, I'm going to start with the sampling
so I can talk about the rest of the research.
So my focus is on multilingual users,
how did I identify multilingual users on Twitter?
It was giving me a headache.
Finally what we decided...
this research has been--
I have always had the help of Jennifer Golbeck,
she was my adviser.
And I did this with her help.
So what we did, was gather a list of what is called stopwords.
From different languages and you have a list over there.
And then the stopword lists you can find them on the internet.
They are created for computational linguistics
so they use it for filtering purposes.
And they are common words in a language.
Very common words in a language.
So, sometimes they're used precisely for eliminating them from texts
when they're in, for example, searches in Google
the eliminate the stopwords, the stopwords that you type
in the search.
But in this case I wanted to find the stopwords
that are very common in the language to represent the language.
And so we had to select words that were not written the same
as in another language.
Sometimes, could be confusing and ambiguous.
Then I typed in Google...
one word in one language and one word in another language.
Usually I was always using one English word
and one word in a different language.
And I looked in the Twitter domain.
So the search results from Google will give me the profiles
of people on Twitter that in theory wrote messages in both languages.
We had to do a lot of hand-combing to actually see if it was in two languages
or it was just that they were mentioning an English song
the title of an English song but they had no English in the rest.
So we had to ensure that they were authoring tweets
in two languages.
So writing them, not just retweeting them
they were not just automatic postings from Facebook.
So we had a long set of criteria a lot of manual combing
and then finally we selected 92 multilingual users
and in total they used 19 languages, 2 or 3 languages per person.
Now, I don't know if you want to ask some questions about the sampling
because there's a lot of details about it.
No doubts?
Or maybe they'll come later!
Now, how do I do the social networks analysis?
Well, now I have my 92 multilingual users technically they are called the ego
of an egocentric network.
This is the cell of my study.
It started with the nucleus of the cell
which is my multilingual user
and then I go to Twitter
and first of all I have instructed--
so in this case my ego is called the Painter
and I have extracted the last 50 messages that he posted on Twitter
to see the languages this person used-- is using.
And I see that he is using English, Spanish and Catalan.
Catalan is a regional language in Spain
and I have shown you on the map the region before
where the region was.
And they speak both Catalan and Spanish.
So, this person is tweeting in a minority language
a national language
and also international.
So, I already found the Painter and I know what languages this person speaks
well, uses on Twitter,
and then I extract all the social networks.
So, the followers on Twitter
you know that on Twitter you have followers
and you follow people.
I extracted both.
The followers of the Painter the people that are following him on Twitter
and also how the friends are connecting to each other.
So, all of them, all of these dots are the followers
the people following the Painter on Twitter
and also I see how they connect among each other, ok?
So the Painter follows Eduard in the center
and it seems he's very popular.
And then I extract the last 30 posts of Eduard--
there's a reason for that
but vernacular is mostly economy questions!
I will tell you why!
So I extracted the last 30 posts of Eduard
and then I do automatic language identification
with the Google API for language identification
which costs money.
So you have to really think about how many posts you want to send
to Google and how much money you have available
and what is the accuracy you're going to have
according to how many posts you send.
There's a lot of testing going on there.
I do the same with everybody in the social network.
I extract the last 30 posts
use the Google identification
build that algorithm that decides based on the languages of these 30 posts
is this person monolingual? Is this person multilingual?
Which languages?
And then I laddered them, ok.
This is just a visualization behind the--
Perhaps person 1 is monolingual, or bilingual of two languages.
Now that I have all the friends of the Painter
how they connect,
I color code them depending on the languages they are using.
And here, what you can see is very interesting.
I don't know if you can distinguish the colors well
because up here, this area, that is like a triangle
there's a group of users writing in English.
And it's pink. Sort of pinkish.
And then, down here there's this Spanish group
in light green.
And, in the middle, the one that perhaps doesn't distinguish as well
from the English, is the Catalan group.
So the users writing in Catalan in dark blue.
And then there's a set of violets in between
and these violets represent the bilingual users
either English and Catalan or English and Spanish.
And then there's darker green around here,
they are using both Catalan and Spanish.
So there's a lot of bilinguals going on.
And there's an interesting dynamics
in that you have this English group up there
and the Spanish group up here
and the Catalan group in the middle.
And this Catalan group is very mixed up with the Spanish group
which makes sense, because it's a bilingual community.
So, this is how I built the egocentric network of my 92 multilingual users.
The Painter is just one of them. I have 92.
I have 92 cells or egocentric networks that I studied with my microscope.
Do you want to ask some questions about this process
or this visualization?
(person 1) Of the bilingual units, are they users or tweets?
They are users, yeah.
So, the dots represent people.
So, like Eduard here. They represent people.
Now each dot to determine the language and the color
I extracted 30 posts
So, it's an interesting question because the 30 posts
have different language levels assigned to them
especially if they were bilingual
and I had to decide which language level I was going to assign to the user.
So, I had to build an algorithm with a set of rules
basically saying--
the Google identification system would give me a language
and a confidence level
So if the confidence level was very low
I would say "discard that" because I had a series of pluristics
based on both the number of tweets using a particular language
and also on the confidence level.
And there are a lot of technical challenges there as well.
(woman) So, it's possible that some of these posts
many of these posts would be multilingual, I'm sorry monolingual in one language or the other?
So it's also possible that some of these individual posts
would mix languages?
Yes, it is possible. It's very possible!
It's very challenging for the automatic system!
(woman) Right, ok. I just wanted to be clear--
Yes, exactly.
So it's not as frequent as I expected, having bilingual posts
that I would call.
But it's happening.
And so, for a series of tests, I had to do manual combing
and I saw that sometimes it was the case
that they were doing some sort of translation in the same tweet
and sometimes it was just the case that they were mentioning titles of things
or places in a different language.
So, there's a lot of issues surrounding the automatic handling of this
but you are dealing with 92 networks
and they have between 30 and 5,000 nodes in them.
So, I don't remember the numbers exactly,
but I'm talking about around 80,000 people.
So detecting the language of 80,000 people and this is small-scale.
If you go to millions, you need an automatic system.
And one of the things I'm having to write up in my dissertation
is what are the challenges.
You have to be prepared for them, to solve those problems.
And one of them is what do you do with bilingual posts
which language do you assign to that post?
Automatic posts, spam... there's a lot of problems.
Challenges, I mean.
That's what makes it interesting because you cannot do manual combing
on these scales.
Do you have another question?
So, now, what am I doing with this?
I'm going to classify my social networks, looking at the patterns
of overlaps between the languages groups.
And overlaps or intersections.
I'm looking specifically at the networks that have only two language groups
I had five of these networks that were trilingual
so I put them aside to go simple first with just two language groups
to see how they interconnect.
And then I classified them
first following a qualitative analysis
and then I used network statistics that I developed with my adviser
for this purpose.
And I will talk later a little more about it.
So, tried to provide more robust measures for that.
I classified them and I came up with some types.
This is what I call the gatekeeper language bridge type.
And there's some variants of it, obviously.
What you can see here is the network of a person
and I'm going to assume this person is in the United States
and speaks both Spanish and English.
Let's call her Maria.
So she's Maria and she has two groups of friends using Spanish on Twitter
and then that big group of friends using English.
And, as you can see, there's just a few nodes
connecting the two language groups.
You can see that the social structure can be different from the language groups
so you can have maybe a group of friends and a group of coworkers
inside the same language group, so it can be more complex
than just dividing the social network by language groups.
There can be more grouping because of other social resources.
But the interesting thing is that there are only a few nodes
where people are connecting holding together these Twitters.
I think this was friends with English here.
You can see, in this case, it seems like the two groups
are holding closely together
because there are much more links holding the two groups together.
Of course, this is going to depend on the size of the networks
so I had to account for the size when coming up with measures
with network connections
I had to provide ratios.
Now, the ratio of [close] language linking here and here
and you have these types--
These types are not just clear-cut.
There's an evolution.
There's people that have very few connections
with the language groups
and then progressively there's people with more and more.
And this increases.
Which points to the fact, that my cells are there.
Which means I don't see the evolution over time, ok?
This is a limitation of my research.
I just see the social network of this person looked
at a particular point in time.
I don't know how it evolves over time.
So, for myself, it's just there.
It would be interesting to see these different patterns
that I have been observing.
Maybe over time these connections between languages maybe increasing.
Now we have the integration and union type
where in this case you have a person from an Arab country
and green represents the friends that are using Arabic
and the friends using English are in pink, but there's also violet
there are bilinguals.
That means there's a group of English users
and bilingual English - Arabic users inserted in the group of Arabic, inside.
That's the integration, so they're integrated.
And then I have a Greek guy, who uses Greek and English
and his Arabic friends.
And in this case, you can see it's sort of light blue
representing Greek, so the friends that tweet in Greek
Pink again represents people tweeting in English
and there's a lot of bilinguals.
So these kind of dark blues represent the bilinguals.
And these are two groups
that if you've seen before, the gatekeeper and the language bridge
progressively getting closer and closer
with more and more links across languages.
In this case, this is like the extreme.
The links between the two languages are so dense
that you cannot almost distinguish where the border is
between the two language groups.
And, interestingly, the border might be even only noticeable
because there's a lot of bilinguals around it.
And this is the union type where they unite.
And finally, the peripheral language type.
This is a Brazilian guy, the network of a Brazilian guy
where you have--
probably he lives in the United States or something like that--
because this guy has mostly all this big group of friends
tweeting in English.
And then there's the side tentacle running outside, using Portuguese.
And this is like a periphery landscape.
So, in the periphery there's a small group of Portuguese language.
Now, I forgot to mention that there's dots that are light yellow or white.
Those are the ones that have no data.
So, I don't know the language they're using
because either their accounts are closed
or for some reason, in between the collection of data they closed the account.
Mostly, the reason is that they're private accounts
where you cannot get the data from.
I think somewhere I read it was about 5 percent.
I'm not sure.
But for one reason or another, I don't have that information.
Now, why am I classifying them? These networks?
Well, the reason is that--
well, there are some studies that demonstrate that the social structure
the structure of the social networks influences the spread of information.
How information disseminates in the network.
So, I'm just assuming that these different structures
are going to influence the spread of information.
But this is a study that has to be done.
I cannot demonstrate that one of these types
facilitates the spread of information.
I can only say that I am assuming, so that potential study
could just look at, for example, if gatekeeper and language bridges
are not as good for spreading information as union and integration types.
Right, we can just assume because of the cross-language links
so, how many links there are or the ratio of discourse language
may potentially facilitate information diffusion in these cases.
So, that study needs to be done.
I cannot say what's going to happen!
I just assume it's going to be like that.
So that is the reason why I classify them.
I have some network statistics.
We've made about an 80 percent accuracy guess, which is quite good,
but the sample is small.
So now, do you have any more questions before I move past to the next study?
man) I was curious as to how many--
what was the selection process like to find the 92 users?
Well, this is what I've been spending the beginning
about just using two stopwords from two different languages
typing that in the search box in Google and searching Twitter
and then once--
Basically you just go through the list of results
and start opening the profile, counting the tweets.
How many in this language, how many in the other.
And we put a threshold of 10 percent
they had to have written 10 percent of the tweets in a second language
and you couldn't count retweets or automatic posting.
We also had to manually discard these spammers.
So, that was the process.
(woman) And that's a paid search through Google?
No, that we did manually
and then once--
So the other thing you can say is you can use these core multilingual users
and then do what I did for behavior in these social networks
which is once you extract the friends and extract the messages of the friends
and automatically find the language
then you can say "Oh, this person is multilingual" automatically.
You just process it and you can detect a lot more multilingual people
through that process.
The paid process was sending these posts
to the Google language identification tool.
So, what I did was clean each message automatically.
Basically, eliminating the hashtags
and the mentions that had an @ in front,
symbols, URLs, all those things I would automatically eliminate them
and then with the rest of the message, I'd send that to the Google API
for language identification
and the Google API would give me a language level and a confidence binary.
And that for each message.
And then I built the algorithm with the help of Jen Golbeck
to decide, well I have 30 messages, 500 English
10 million Spanish and then one in Swahili which is unlikely
and you had to decide the confidence value--
So I used rules, defined rules
but it could be done statistically I think.
And write some statistical method to decide
"well this person actually is bilingual" or whatever.
That's the process.
It's long!
Yes.
(woman) Hi, I understand that you did it manually
but currently in existing research field is there any software
that we can use to capture,
to have access to all these different tweets?
And to capture the different categories? [inaudible]
Ok, so you mean the extraction?
(woman) Yeah.
No, I didn't do it manually.
(woman) And the other, I think the other part
of your data presentation is visualizations coming out
like this graph.
Can you show us what kind of research do we have for social scientists
to present the data in a visual form?
This is a tool I would recommend.
[inaudible]
So, the first question.
All the extraction from Twitter, it was automatic.
I didn't copy the tweets, it was automatic.
I used the Twitter API.
They have a process for registered developers
and I extracted it automatically.
Now, the tools, and I forgot to put that in this slide
but in the beginning, when I showed you the first visualization
I put the name of the tool in--
I don't know if I translate well, but I think it's G-E--
You can see here, G-E-P-H-I, I don't know how to pronounce it!
["Jefy" I think...]
So, this is the one I've used for the visualizations
and it's good because you can use it on any platform.
So both on a Mac or a PC or Linux.
Now, it has limitations for...
mostly for network statistics in my opinion.
The other one, that is very popular is Node XL.
And in fact it was developed here in the ATI lab.
In the lab where I work.
So, they collaborated with Microsoft.
It's a template for Excel
and it allows--
In fact they are still adding new features and there's two people working on it
in the lab.
But the reason I haven't used it here, is because I have a Mac
and also there's another reason I like this positioning algorithm
and this is...
this is another issue I haven't talked about
is how you actually place the dots.
And actually these algorithms for layout use force-directed schemes
like in physics science.
So if a node has a lot of links with another node
they put it closer, so it's like there's forces
or strings attaching the nodes.
And depending on how many strings there are, they're closer or farther.
There's physics science rules for placing them.
But there's different algorithms
but the other reason I chose Gephi is that it has an algorithm
specifically in this tool that places my language groups separately
more than any other algorithm that I could use in Node XL.
And it was more useful to see the groups separated.
But you can use both depending on what you want to do.
They both have weaknesses and strengths,
different depending on what you have to do.
Node XL has more features for processing many networks
and extracting network statistics for many networks at the same time.
And it has a lot of interesting features, maybe this is more manual.
I don't know.
Somebody called it "the Photoshop of visualization".
So I'm going to briefly comment on the factor analysis.
The point here, what I want to see is multilingual users of Twitter
are aware of their audience in a way.
And they somehow perceive how many followers
of this language or the other they have.
Maybe not very consciously,
but they perceive something.
So, I went to see how this social network
the fact that there's many languages or just one in the social network
can affect the choice of language in this person, the ego person.
So, I actually did a lot of testing, different variables,
but I'm just going to focus on the essence,
which is I have my dependent variable which is the proportion of English
used by the ego has 50 posts, maybe 60 percent of them are in English
and 40 percent in Spanish, I don't know.
And then they have the factor of how many users in the network
are in English and how many are using other languages.
And then the multilingual index of the network
- and this is my favorite part -
because it's basically saying
"is multilingualism encouraging English as a lingua franca?"
especially on Twitter, where we have these public posts that anybody can read.
So anyway... I'm not going to go into the technical details
of bi-nodal statistical interpretation.
What I wanted to do is that in these combined effects
of the factors, which one was more important?
Was heavier than the others?
Had more weight in defining these proportional [inaudible] used by the ego.
I tried other factors,
I also looked at the use of non-English language
In the end... there are certain,
I mean, they're obvious somehow.
I think it's more interesting the process of what I've learned
than the results themselves.
Because basically what I've learned is that, yeah,
the English use of the network
is encouraged by the use of English by the ego
and in a certain way it's so important that any other factor
is really not that important.
And even the second most important, the multilingual index
was so light compared with the heavy impact of English
used in the network.
But what I thought was really interesting
was how do you define the multlinguality of a network?
And with this I got help from Jordan Boyd-Graber
who is also in the iSchool
and in the lab for computational lab, the information processing lab
here in Maryland.
He helped me with all these technical aspects.
And he was the one suggesting "Well, why don't you look--"
"instead of just looking at the number of languages in the network...
"because sometimes you get wrongly detected languages...
like Swahili. Well, no one was really speaking Swahihi in this network.
There were technical challenges, like I explained to you.
So maybe there's a high number of languages in the network
but the network is mostly monolingual.
Mostly everybody uses English and just a few people maybe use others
or maybe just it got wrongly detected.
And maybe you're just saying
"Oh yeah, there's ten languages in the network!"
and actually it's not a very multilingual network at all.
So, we came up with this, the entropy.
And this is a physics concept that measures the disorder
in a system.
And in this case, the entropy would be my multilingual index
and what it's doing is providing a value between 0 and 1
So, with 0 it's a very homogeneous system everyone speaks the same language
and if it's closer to 1, it's really a heterogeneous
and it places an importance
in how many people are using its language.
So, this is the equation, just to show you it.
And it takes into account the number of languages in the network
and then one of the variables is how many nodes in that language
that there are divided by the total number
and this is what gives the proportion for example.
So just to let you know that there's interesting lessons
from this study.
Despite the research not being exciting!
And this is what I'm doing right now.
So, the intrinsic characteristic of the message
how that influences the language choice.
First, I'm wondering, because I just saw it in the content
are replies encouraging people to use their native language?
And public posts encouraging people to use English as a lingua franca?
This is one that showed up the same.
And I changed the handle, for privacy reasons...
So this is the reply to somebody and it's in Arabic.
And this is a public posting and it's in English.
Now, the thing I'm looking at is public analysis
and I'm considering with Jordan to do some automatic topic analysis
because there's many languages, so I cannot decode it all
in many of them.
Only in three, maybe four...
So, I'm wondering,
are technology topics favoring the use of English?
And other topics, international news maybe?
Whereas other topics like national news or songs
they might be encouraging the use of native languages.
And then I'm looking if there's translations
or if there's cross-cultural words that you can detect.
For instance, this person is writing in English
but it recommending a visit to a museum in the city of Lille in France.
So this person knows the city in France, knows that to visit the museum
you go there.
And this is what I call cross-cultural words.
[What I kind of found] is that surprisingly there's not many translation behaviors
going on, despite these people being multilingual.
And this is what is going to trigger some reflections.
How am I doing on time?
(woman) 1:22.
(man) Umm, it's usually an hour long...
So, I will go on with my reflections.
to encourage some thoughts.
So the greatest connecting power is the will of users who want
to be connected.
This is a really nice quality, because the communities of interest
in social media, in Twitter is what is bringing people
from different countries, together.
And also experiences, like the Voluntweeters,
so after the earthquake in Haiti, there were these spontaneous
self-organizations of Twitter users for translating tweets
and they called themselves Voluntweeters, there's a paper about that--
So this is the triggering of social connections
across countries, across borders and across languages.
But even when the social structure could potentially facilitate
information diffusion and cross-language linking
this condition is not sufficient.
There are other factors like the design of the interfaces
and the design of systems that can influence...
can promote, or not translation behaviors and cross-cultural awareness.
And the Wikipedia of cross-language linking
you have links for many languages for every article.
We also still acknowledge the dynamic language preferences of multilingual users
so they could address their messages to the appropriate audience.
I like the solution of Google+ with their circles
where I can put my friends and family in Spain in a circle
and write them in Spanish.
And then the recommendation of people based on language profile
would be useful for this spontaneous self-organization.
So, these are some of the things.
The impact of mediation.
Global Voices is an international community of bloggers
that connect bloggers and citizens from around the world
in different languages.
And Scott Hale
a student from Oxford University led a very interesting study
after the earthquake in Haiti about blogs in Spanish, Japanese and English
and he looked at the cross-language linking
and focusing on this topic over time.
And he discovered that 50 percent of the cross-language linking
was happening through this platform, Global Voices.
So, it had a very big impact in the language links.
And finally, social media, big media outlets,
people are interconnected in these complex networks
and underlying is this language ecosystem.
So we have the language ecosystem, and on top of that
we have the social media ecosystem.
People would share a video from YouTube on Twitter, or news on Facebook.
What happened if we integrate in this ecosystem
these platforms, like Global Voices, like Universal Subtitles
which is a platform for crowdsourcing subtitling of videos
and translation of subtitles for videos.
If you integrate that and this starts connecting, starts building paths
between languages, that didn't exist before.
So I think we should make it easy for multilingual people to translate
and subtitle all the content they like, their favorite content
and share it with the appropriate audience so they can start connecting
the language islands of the internet.
And that way stories will travel all over the world.
Particularly I would like to thank Jen Golbeck, my adviser
and Fulbright for supporting this research.
And then I open the space for questions and your ideas
if this has triggered some thoughts.
(woman) I have a question about how this relates
to your Yahoo award.
Well, they have the Internet Experiences lab in California.
And they--
So, we tend to think maybe it's a super tiny place
but actually there are fields
and I applied for the social systems.
The social systems are a category.
And I think that was embedded in the Internet Experience lab
and yeah, they liked it.
(man) But is it this work that they are interested in?
Yes.
- The languages? - Yes.
Well, now I have results, because I wrote up reports
about what my work was about.
Great.
Yes?
(woman) I was thinking about if you analyzed the place...
like if there's any relationship between tweeters and tweets
and the place that the people are.
I mean, because it's not the same being a Brazilian in Brazil
and tweeting in Portuguese or being Brazilian in the US
and tweeting in Portuguese--
There's many, many factors that I haven't looked at.
It's not part of your study?
But because I had to scope it somehow.
There's so many factors.
Geography was one that I was originally intending to look at
but I found there were so many problems to actually get the right geography
the right geolocation.
The problem is that I didn't originally collect the geolocation.
I think only a small percentage of messages have...
geolocated information.
I'm not sure about the percentage there.
So there's only a small percentage of messages that have geolocation.
There's issues with the accuracy...
What I have collected is the information in their profile
they can put the information about the place,
but sometimes it's more or less trustworthy,
sometimes there's nothing, and sometimes there's just crazy stuff.
(audience laughs)
So, something absolutely has to be there.
If I wanted to expand this, geography would be a nice place to go!
(woman) Ok.
Yes?
(man) Could you say a little bit more
I think you said about the visualization choices you made?
Oh yes, well...
I tried this tool, the Node XL,
I used both Node XL and Gephi.
There's more...
I think there's, I don't remember the name there's one that was developed
here in Maryland
but it's not as user-friendly.
But I've forgotten the name, I will have to look it up.
And there's a lot of tools that are for really technical people
that are handling millions of nodes.
Because with these tools, for social scientists or humanists
maybe they are not.
Some tools can have maybe 300-400 nodes and still be understandable.
But if you go beyond that, actually visualizations get crazy
and even for more technical tools for more technical people
there are hundreds or millions, they cannot do visualizations
at some point they just give you statistical measures.
I have to leave it out.
I have a list of tools and that
but if I need the names, I need to go through everything.
(woman) But yours was Mac-accessible?
Yes, this Gephi tool is Mac-accessible, you can use it with Microsoft
with Mac and with Linux.
And I forgot to say, it's open source.
(woman) Did you find studying languages and internet
was like a place, unexplored?
Like here in the United States?
Like when you began studying or analyzing this
you felt that a lot of people are doing this
or nobody is doing this
and I'm the first one trying to--
I'm not the first one, but it's a very new area
to be exploring.
So, it's very exciting because of that.
Because there's so many unanswered questions
and I find that surprisingly enough the United States is not paying so much attention
about multilinguality issues
And I think that language policies are very monolingual-oriented
but it's terrible
because there's a whole lot of multilinguality in this country.
There's so many people speaking different languages
that I'm so amazed about that contradiction.
Because in Europe, it's an obvious challenge for us
because we need to understand each other between all these countries
of the European Union.
And there's a lot of money invested in research that relates to multilinguality
and communication in languages
and technology in particular, cross-language systems
and in libraries there's a lot of work going on.
There's investment in the research.
So yeah, maybe in terms of investment
the European Union is not a bad place to be.
Better than the United States!
But at the same time, what I find interesting
is that here when I talk about it people are really interested
and interested in the subject and excited about it.
Maybe in Europe it looks more like old news.
Like "yeah, we already know that."
(audience laughs)
So I find that it's exciting to be seeing the audience
like "Oh yeah!" It's so new.
*(woman) Yes.
(woman) As the emerging view of research in the United States
can you show me which institutions or which area of academic institutions
actually have more invested in this topic in the US?
I'm not sure about the institutions.
What I know, particularly, in Indiana there's work
because Susan Herring is a researcher there.
She has inspired my work.
She published a book The Multilingual Internet
and she has done research on blogs, also communities
of different languages connecting blogs in the blogosphere.
So she has been one of the ones, one of the first tackling these issues
and she's still going and she's doing something.
So, it's the University of Indiana, I think.
Yeah, Susan Herring. Look for her!
And also at the same university there's Paolillo.
He's also doing research in this area
and he actually published for UNESCO for research on language diversity
on the internet.
So Susan Herring and Paolillo,
they are at the same university.
Those are my inspiring ones.
Well, at Harvard at the Berkman Center of Internet and Society also did
this mapping of the blogs.
But they don't focus on languages.
But there's tangential thing around there.
(man) One more question?
Well, thank you very much!
Thanks!
(audience applauds)