WEBVTT
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Good morning, everyone.
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Thank you for coming here
[unclear] of the semester.
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So, I'm going to start.
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Access to the internet
is greater than ever before
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and as a consequence,
it's becoming more multilingual.
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However, there's evidence of segmentation
of cyberspace
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due to language and national borders.
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This image serves to illustrate that.
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This is the language communities
of Twitter in Europe.
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So, what you can see are tweets
geolocated over a map of Europe
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and the different colors
represent the different languages.
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You can even see regional languages
like Catalan in the Catalan region of Spain
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And this is going to be useful
for an example I'm going to use later.
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I'm interested in Twitter in particular,
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because of the speed
of information dissemination
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and that most of this information
is publicly accessible.
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I'm going to illustrate this
with a capture
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of a dynamic visualization
you can find on the Twitter blog
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by Miguel Rios.
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And what you can see here
is the global flow of tweets
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after the earthquake in Japan.
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In pink, there are the tweets
coming out of Japan
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and, in green, the retweets
all over the world.
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This illustrates that in Twitter
information is spreading across countries.
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But how can this happen?
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Expatriates, migrants, minorities.
diaspora communities, language learners
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all play an important role
in building transnational networks
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and cultural bridges
between nations and communities.
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They are the multilingual users
on the internet.
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The overarching research question is:
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how are multilingual users of Twitter
connecting different language groups?
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In 2009, the Berkman Center of Internet
and Society at Harvard University
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mapped the Arabic blogosphere
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and they described a key concept
for my research.
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They discovered an English bridge
and a French bridge of bloggers
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that were writing in their native
Arabic language and in English or French.
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And they were connecting the different
national blogospheres
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with the international one.
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This might have played a role in the Arab
popular uprisings in 2011
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for reaching out to the world.
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And this is connected with a concept
that first appeared in 2008
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of the bridge bloggers.
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So, bridge bloggers are bloggers
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that are trying to connect
their local communities
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to a wider global audience.
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The image you can see here
is actually the visualization they created
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of mapping the Arabic blogosphere.
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Each dot is a blogger, or a blog.
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The size represents their popularity,
so how many incoming links they have
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and they grouped them--
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the neighborhoods they created
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in relation to the linking
between them.
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So, the ones that are grouped together
are linking among each other.
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The colors are a different question.
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The colors represent "attentive clusters",
that's how they call it.
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And they look at their online resources
and media outlets
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these blogs were linking to.
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So, blogs of the same colors
are following the same media outlets
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and online resources.
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And they did human coding
to label those groups.
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And here is where we see
the label English grids
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the responses from Cuba
in English
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and up there, there's [unclear] France.
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And so I think it's important to retain
the concept of attentive clusters.
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Now, let's go back to 2011
during the Arab popular uprisings.
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And I'll show you a visualization
of the influence network
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of Twitter users in Egypt.
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So, what you're seeing here
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just imagine people down the street
at Tahrir Square
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tweeting in Arabic about what's going on
on the ground.
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And those are the people in red.
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So, these red dots represent users
that are tweeting in Arabic.
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Then we have the international community
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or even Americans, British and so on
tweeting in English.
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And they are in blue,
those blue dots.
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And then, interestingly, we have
people in between them.
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which are illustrated in different
degrees of violet, or violet shades.
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This represents the fact that they
are tweeting in both Arabic and English.
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So, what we're seeing
is the bridge Twitters
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because, like Ethan Zuckermann called them
"bridge bloggers".
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So, another context.
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The same year, 2011, a lot
of big protests were going on in Europe.
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And in particular, in Spain.
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They started on May 15th 2011
there were massive protests.
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And of because of this context,
this situation
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new attentive clusters were emerging
in the social media landscape of Spain.
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Now, this is a visualization you can find
in the Socialflow blog, research blog
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on social networks.
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And what it is, is it tracks the origin
and the initial spread
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of the hashtag #occupywallstreet
in Twitter.
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They detected that one of the first users
of the hashtag #occupywallstreet
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was on July 13th 2011, linking to a blog
post of Adbusters.
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So you have the Twitter account
of Adbusters there, very big
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because it's being retweeted a lot.
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And mentioned a lot.
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And they collected these mentions
and the tweets that had these mentions
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and these retweets with the hashtag
during July 13th.
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From July 13th to July 23rd.
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So, from the first 10 days
of the use of this hashtag
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it was from the very beginning of the use
of this hashtag on Twitter.
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They just mapped the accounts
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and the series of posts with the hashtag
and mentions with the hashtag
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and the users that were connecting
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because of these mentions
and retweets.
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Now the interesting thing
in this visualization
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is that they
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the Socialflow people
particularly in [inaudible]
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detected this Spanish brand
of users
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were forming an attentive cluster.
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Mentioning and retweeting about it
in Spanish
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using the hashtag in their messages
in Spanish.
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And they point out in the blog
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that this Spanish contingent
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helped post and spread the word
about Occupy Wall Street
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even before most of the United States
was aware of it.
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So, I found that very interesting.
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And it was due to the context
in Spain at that moment
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with big protests and new clusters
forming in the social media landscape.
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Now I have shown you the importance
of these multilingual users
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in connecting language communities
and spreading information
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across countries, acting as mediators.
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But let's focus on another aspect
of connecting language groups
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which is language choice.
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So I'm going to devote a moment
to speak about languages
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and language choice.
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To understand languages in the world
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I'm going to use a telescope.
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So de Swaan...
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...proposed a theory called
the world language system
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back in the 1990s.
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to explain the languages in the world.
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And he used a very beautiful metaphor,
the constellation.
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So, in his theory there's about a dozen
languages in the world
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that are the hearts of the system,
or the suns.
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The suns of the system.
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For instance, English, French, Spanish,
Arabic and more.
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And then there are hundreds,
maybe more than 100, 200...
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national languages that are orbiting
around these suns like planets.
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And finally we have regional
and minority languages
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that are orbiting these planets
like satellites.
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And he used this metaphor
to explain the power relationships
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between languages.
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This is a theory of what he called
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"communication potential
and language competition"
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A key point he made
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is that the system holds together
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thanks to multilingual people
and interpreters.
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This is what's providing cohesion
to the system.
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He also made a controversial proposal
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about the communication potential
of a language.
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So, he proposed a formula,
a mathematical formula
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where he could estimate the communication
potential of a language
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and supposedly a person with tools
through learning and usage
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based on the communications of that.
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For example, a person might decide
to learn English and use English
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because not only does it provide
communication with English native speakers
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but also, adding to that, it provides
the possibility to communicate
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with all the second-language learners
of English
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from many different languages,
many different countries.
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So, supposedly, in history
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English provides
the greatest communication.
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And he received some criticism,
because of the central role of English
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in his theory
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He said it was the central hub
of all the system.
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There's also the language ecology paradigm
first proposed by Haugen in 1972
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and there's this idea of an ecosystem
of languages
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and, again, it's using another metaphor
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and because of this metaphor
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also appeared the idea
of endangered languages.
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I'm going to briefly just read
the definition.
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He defined the language ecology as:
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"the study of interactions between
any given language and its environment"
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and what I think is very important:
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"language exists only in the minds
of its users"
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which leads me to point at my research.
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In my research, I'm using a microscope
to see the cells
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and my cells in my study
are the Twitter users.
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Why is that?
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Because as Haugen explains,
there's a psychological dimension
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to language ecology
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where language interacts
with other languages
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in the minds of multilingual people.
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And there's a sociological dimension
to language ecology
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where we use language to communicate
and interact with other people.
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And this language ecology generates
because of the people
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that decide to use that language
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learning and interacting
with people using it.
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And this is the point
of language choice in languages.
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So, I focus on the connections of people
and the language choice.
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So, these are the four points
I'm going to be speaking about.
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But actually the main focus
is going to be the first point
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Social network analysis and the taxonomy
of intersections between language groups
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This is where I'm going to be spending
most of the time.
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And then very briefly,
just for compilation purposes
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I'm going to speak about another
small study that I did
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the factor analysis
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looking at the influence
of the social network
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in the language choices of the users.
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So, how the social network
influences language choice
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of our multilingual users.
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And then I'm going to briefly also talk
about the last study of my dissertation
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that is still ongoing.
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So, I still have new research
to talk about.
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And it's content analysis
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and in this case I'm focusing on
intrinsic factors
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intrinsic to the messages
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about the topic,
and the type of exchange.
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If it's a reply,
if it's a public post
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and how that influences
the language choice as well.
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And finally I will...
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I'm going to give you my reflections
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so I can invite your thoughts
and suggestions and discussions about it.
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Briefly, I'm going to start
with the sampling
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so I can talk about the rest
of the research.
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So my focus is on multilingual users,
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how did I identify multilingual users
on Twitter?
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It was giving me a headache.
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Finally what we decided...
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this research has been--
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I have always had the help
of Jennifer Golbeck,
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she was my adviser.
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And I did this with her help.
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So what we did, was gather a list
of what is called stopwords.
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From different languages
and you have a list over there.
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And then the stopword lists
you can find them on the internet.
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They are created
for computational linguistics
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so they use it for filtering purposes.
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And they are common words
in a language.
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Very common words in a language.
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So, sometimes they're used precisely
for eliminating them from texts
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when they're in, for example,
searches in Google
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the eliminate the stopwords,
the stopwords that you type
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in the search.
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But in this case I wanted
to find the stopwords
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that are very common in the language
to represent the language.
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And so we had to select words
that were not written the same
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as in another language.
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Sometimes, could be confusing
and ambiguous.
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Then I typed in Google...
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one word in one language
and one word in another language.
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Usually I was always using
one English word
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and one word in a different language.
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And I looked in the Twitter domain.
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So the search results from Google
will give me the profiles
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of people on Twitter that in theory
wrote messages in both languages.
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We had to do a lot of hand-combing
to actually see if it was in two languages
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or it was just that they were mentioning
an English song
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the title of an English song
but they had no English in the rest.
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So we had to ensure
that they were authoring tweets
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in two languages.
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So writing them, not just retweeting them
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they were not just automatic postings
from Facebook.
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So we had a long set of criteria
a lot of manual combing
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and then finally we selected
92 multilingual users
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and in total they used 19 languages,
2 or 3 languages per person.
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Now, I don't know if you want to ask
some questions about the sampling
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because there's a lot of details about it.
00:18:13.392 --> 00:18:14.602
No doubts?
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Or maybe they'll come later!
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Now, how do I do
the social networks analysis?
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Well, now I have my 92 multilingual users
technically they are called the ego
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of an egocentric network.
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This is the cell of my study.
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It started with the nucleus of the cell
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which is my multilingual user
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and then I go to Twitter
00:18:40.478 --> 00:18:43.439
and first of all I have instructed--
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so in this case my ego
is called the Painter
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and I have extracted the last 50 messages
that he posted on Twitter
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to see the languages
this person used-- is using.
00:18:57.156 --> 00:19:01.943
And I see that he is using English,
Spanish and Catalan.
00:19:02.945 --> 00:19:05.479
Catalan is a regional language in Spain
00:19:05.605 --> 00:19:07.736
and I have shown you on the map
the region before
00:19:07.737 --> 00:19:09.247
where the region was.
00:19:09.275 --> 00:19:12.474
And they speak both Catalan
and Spanish.
00:19:13.727 --> 00:19:16.825
So, this person is tweeting
in a minority language
00:19:16.826 --> 00:19:18.488
a national language
00:19:18.489 --> 00:19:20.779
and also international.
00:19:26.808 --> 00:19:31.754
So, I already found the Painter
and I know what languages this person speaks
00:19:31.754 --> 00:19:33.542
well, uses on Twitter,
00:19:33.543 --> 00:19:36.178
and then I extract
all the social networks.
00:19:36.179 --> 00:19:37.932
So, the followers on Twitter
00:19:37.933 --> 00:19:39.716
you know that on Twitter
you have followers
00:19:39.717 --> 00:19:41.160
and you follow people.
00:19:41.163 --> 00:19:42.513
I extracted both.
00:19:42.546 --> 00:19:48.323
The followers of the Painter
the people that are following him on Twitter
00:19:48.323 --> 00:19:52.118
and also how the friends
are connecting to each other.
00:19:52.289 --> 00:19:56.671
So, all of them, all of these dots
are the followers
00:19:56.672 --> 00:19:58.707
the people following the Painter
on Twitter
00:19:58.708 --> 00:20:02.788
and also I see how they connect
among each other, ok?
00:20:04.542 --> 00:20:08.837
So the Painter follows Eduard
in the center
00:20:10.153 --> 00:20:12.245
and it seems he's very popular.
00:20:13.567 --> 00:20:17.042
And then I extract the last 30 posts
of Eduard--
00:20:17.048 --> 00:20:18.509
there's a reason for that
00:20:18.510 --> 00:20:21.961
but vernacular
is mostly economy questions!
00:20:24.717 --> 00:20:25.717
I will tell you why!
00:20:25.718 --> 00:20:28.857
So I extracted the last 30 posts of Eduard
00:20:28.858 --> 00:20:31.966
and then I do
automatic language identification
00:20:31.967 --> 00:20:36.734
with the Google API
for language identification
00:20:38.548 --> 00:20:39.548
which costs money.
00:20:40.527 --> 00:20:43.282
So you have to really think
about how many posts you want to send
00:20:43.283 --> 00:20:45.580
to Google and how much money
you have available
00:20:45.581 --> 00:20:48.178
and what is the accuracy
you're going to have
00:20:48.179 --> 00:20:51.125
according to how many posts you send.
00:20:51.348 --> 00:20:53.268
There's a lot of testing going on there.
00:20:54.271 --> 00:20:58.482
I do the same with everybody
in the social network.
00:20:58.700 --> 00:20:59.893
I extract the last 30 posts
00:20:59.894 --> 00:21:02.340
use the Google identification
00:21:02.341 --> 00:21:08.086
build that algorithm that decides
based on the languages of these 30 posts
00:21:08.087 --> 00:21:11.929
is this person monolingual?
Is this person multilingual?
00:21:11.929 --> 00:21:13.221
Which languages?
00:21:13.222 --> 00:21:15.379
And then I laddered them, ok.
00:21:16.572 --> 00:21:18.746
This is just a visualization behind the--
00:21:20.280 --> 00:21:27.315
Perhaps person 1 is monolingual,
or bilingual of two languages.
00:21:31.985 --> 00:21:35.782
Now that I have all the friends
of the Painter
00:21:35.918 --> 00:21:37.392
how they connect,
00:21:37.392 --> 00:21:40.854
I color code them
depending on the languages they are using.
00:21:42.020 --> 00:21:44.669
And here, what you can see
is very interesting.
00:21:46.076 --> 00:21:48.735
I don't know if you can distinguish
the colors well
00:21:48.736 --> 00:21:53.949
because up here, this area,
that is like a triangle
00:21:53.950 --> 00:21:57.896
there's a group of users
writing in English.
00:21:58.743 --> 00:22:00.753
And it's pink.
Sort of pinkish.
00:22:00.753 --> 00:22:04.547
And then, down here
there's this Spanish group
00:22:04.548 --> 00:22:06.792
in light green.
00:22:07.544 --> 00:22:12.407
And, in the middle, the one
that perhaps doesn't distinguish as well
00:22:12.408 --> 00:22:15.464
from the English,
is the Catalan group.
00:22:15.935 --> 00:22:18.962
So the users writing in Catalan
in dark blue.
00:22:19.776 --> 00:22:21.870
And then there's a set of violets
in between
00:22:21.871 --> 00:22:26.319
and these violets represent
the bilingual users
00:22:26.319 --> 00:22:29.292
either English and Catalan
or English and Spanish.
00:22:29.963 --> 00:22:33.031
And then there's darker green
around here,
00:22:33.031 --> 00:22:36.498
they are using both Catalan and Spanish.
00:22:36.498 --> 00:22:38.252
So there's a lot of bilinguals
going on.
00:22:38.252 --> 00:22:39.736
And there's an interesting dynamics
00:22:39.737 --> 00:22:42.710
in that you have this English group
up there
00:22:42.711 --> 00:22:44.060
and the Spanish group up here
00:22:44.061 --> 00:22:46.200
and the Catalan group in the middle.
00:22:46.201 --> 00:22:49.147
And this Catalan group is very mixed up
with the Spanish group
00:22:49.744 --> 00:22:52.184
which makes sense,
because it's a bilingual community.
00:23:01.121 --> 00:23:06.529
So, this is how I built the egocentric
network of my 92 multilingual users.
00:23:08.601 --> 00:23:10.987
The Painter is just one of them.
I have 92.
00:23:10.988 --> 00:23:16.575
I have 92 cells or egocentric networks
that I studied with my microscope.
00:23:17.868 --> 00:23:21.817
Do you want to ask some questions
about this process
00:23:21.818 --> 00:23:23.419
or this visualization?
00:23:25.051 --> 00:23:29.982
(person 1) Of the bilingual units,
are they users or tweets?
00:23:30.894 --> 00:23:32.056
They are users, yeah.
00:23:32.400 --> 00:23:35.560
So, the dots represent people.
00:23:35.561 --> 00:23:40.014
So, like Eduard here.
They represent people.
00:23:42.250 --> 00:23:45.317
Now each dot to determine the language
and the color
00:23:45.318 --> 00:23:47.931
I extracted 30 posts
00:23:48.434 --> 00:23:52.797
So, it's an interesting question
because the 30 posts
00:23:52.798 --> 00:23:55.958
have different language levels
assigned to them
00:23:56.096 --> 00:23:57.130
especially if they were bilingual
00:23:57.131 --> 00:24:01.643
and I had to decide which language level
I was going to assign to the user.
00:24:01.644 --> 00:24:05.383
So, I had to build an algorithm
with a set of rules
00:24:10.279 --> 00:24:11.346
basically saying--
00:24:11.347 --> 00:24:16.651
the Google identification system
would give me a language
00:24:16.652 --> 00:24:17.882
and a confidence level
00:24:17.882 --> 00:24:19.496
So if the confidence level was very low
00:24:19.497 --> 00:24:23.838
I would say "discard that"
because I had a series of pluristics
00:24:23.858 --> 00:24:30.113
based on both the number of tweets
using a particular language
00:24:30.113 --> 00:24:32.685
and also on the confidence level.
00:24:33.655 --> 00:24:38.267
And there are a lot
of technical challenges there as well.
00:24:39.973 --> 00:24:41.948
(woman) So, it's possible
that some of these posts
00:24:41.949 --> 00:24:45.824
many of these posts would be multilingual,
I'm sorry monolingual in one language or the other?
00:24:46.498 --> 00:24:51.988
So it's also possible that some
of these individual posts
00:24:51.989 --> 00:24:54.184
would mix languages?
00:24:54.623 --> 00:24:56.733
Yes, it is possible.
It's very possible!
00:24:57.063 --> 00:25:00.360
It's very challenging
for the automatic system!
00:25:01.915 --> 00:25:03.743
(woman) Right, ok.
I just wanted to be clear--
00:25:03.744 --> 00:25:05.185
Yes, exactly.
00:25:05.186 --> 00:25:11.303
So it's not as frequent as I expected,
having bilingual posts
00:25:11.304 --> 00:25:12.740
that I would call.
00:25:12.741 --> 00:25:14.431
But it's happening.
00:25:15.058 --> 00:25:20.539
And so, for a series of tests,
I had to do manual combing
00:25:20.540 --> 00:25:23.263
and I saw that sometimes
it was the case
00:25:23.264 --> 00:25:26.718
that they were doing some sort
of translation in the same tweet
00:25:26.719 --> 00:25:31.585
and sometimes it was just the case
that they were mentioning titles of things
00:25:31.586 --> 00:25:34.206
or places in a different language.
00:25:34.563 --> 00:25:39.470
So, there's a lot of issues
surrounding the automatic handling of this
00:25:39.471 --> 00:25:44.478
but you are dealing with 92 networks
00:25:44.479 --> 00:25:50.864
and they have between 30
and 5,000 nodes in them.
00:25:52.708 --> 00:25:55.841
So, I don't remember the numbers exactly,
00:25:55.867 --> 00:25:59.148
but I'm talking about
around 80,000 people.
00:26:01.132 --> 00:26:04.527
So detecting the language of 80,000 people
and this is small-scale.
00:26:04.913 --> 00:26:08.286
If you go to millions,
you need an automatic system.
00:26:08.287 --> 00:26:11.291
And one of the things I'm having
to write up in my dissertation
00:26:11.292 --> 00:26:13.832
is what are the challenges.
00:26:13.833 --> 00:26:17.984
You have to be prepared for them,
to solve those problems.
00:26:18.551 --> 00:26:21.851
And one of them is what do you do
with bilingual posts
00:26:21.852 --> 00:26:23.920
which language do you assign to that post?
00:26:23.921 --> 00:26:28.287
Automatic posts, spam...
there's a lot of problems.
00:26:29.862 --> 00:26:31.219
Challenges, I mean.
00:26:31.220 --> 00:26:34.766
That's what makes it interesting
because you cannot do manual combing
00:26:34.766 --> 00:26:36.046
on these scales.
00:26:39.073 --> 00:26:41.013
Do you have another question?
00:26:44.501 --> 00:26:48.025
So, now, what am I doing with this?
00:26:50.562 --> 00:26:56.178
I'm going to classify my social networks,
looking at the patterns
00:26:56.179 --> 00:26:59.094
of overlaps between the languages groups.
00:26:59.720 --> 00:27:01.953
And overlaps or intersections.
00:27:02.547 --> 00:27:07.878
I'm looking specifically at the networks
that have only two language groups
00:27:08.219 --> 00:27:11.860
I had five of these networks
that were trilingual
00:27:12.284 --> 00:27:16.020
so I put them aside to go simple
first with just two language groups
00:27:16.021 --> 00:27:18.361
to see how they interconnect.
00:27:19.369 --> 00:27:21.272
And then I classified them
00:27:21.936 --> 00:27:24.198
first following a qualitative analysis
00:27:24.198 --> 00:27:28.822
and then I used network statistics
that I developed with my adviser
00:27:28.823 --> 00:27:30.386
for this purpose.
00:27:31.338 --> 00:27:33.693
And I will talk later a little more
about it.
00:27:34.341 --> 00:27:37.980
So, tried to provide
more robust measures for that.
00:27:39.428 --> 00:27:44.074
I classified them and I came up
with some types.
00:27:45.922 --> 00:27:49.631
This is what I call the gatekeeper
language bridge type.
00:27:50.526 --> 00:27:52.995
And there's some variants of it,
obviously.
00:27:53.624 --> 00:27:55.990
What you can see here
is the network of a person
00:27:55.991 --> 00:28:00.092
and I'm going to assume this person
is in the United States
00:28:00.093 --> 00:28:02.350
and speaks both Spanish and English.
00:28:04.043 --> 00:28:05.684
Let's call her Maria.
00:28:05.927 --> 00:28:11.581
So she's Maria and she has two groups
of friends using Spanish on Twitter
00:28:12.531 --> 00:28:15.768
and then that big group of friends
using English.
00:28:17.320 --> 00:28:19.528
And, as you can see,
there's just a few nodes
00:28:19.529 --> 00:28:22.003
connecting the two language groups.
00:28:22.004 --> 00:28:27.869
You can see that the social structure
can be different from the language groups
00:28:29.391 --> 00:28:32.174
so you can have maybe a group of friends
and a group of coworkers
00:28:32.175 --> 00:28:36.424
inside the same language group,
so it can be more complex
00:28:36.425 --> 00:28:41.205
than just dividing the social network
by language groups.
00:28:41.206 --> 00:28:45.522
There can be more grouping
because of other social resources.
00:28:46.811 --> 00:28:50.572
But the interesting thing is that
there are only a few nodes
00:28:50.573 --> 00:28:53.455
where people are connecting
holding together these Twitters.
00:28:55.058 --> 00:29:00.675
I think this was friends
with English here.
00:29:00.676 --> 00:29:05.461
You can see, in this case, it seems
like the two groups
00:29:05.462 --> 00:29:08.089
are holding closely together
00:29:08.809 --> 00:29:13.833
because there are much more links
holding the two groups together.
00:29:14.663 --> 00:29:18.246
Of course, this is going to depend
on the size of the networks
00:29:18.247 --> 00:29:23.067
so I had to account for the size
when coming up with measures
00:29:23.068 --> 00:29:25.943
with network connections
00:29:25.944 --> 00:29:28.257
I had to provide ratios.
00:29:28.258 --> 00:29:32.340
Now, the ratio of [close] language linking
here and here
00:29:32.341 --> 00:29:34.312
and you have these types--
00:29:36.477 --> 00:29:40.266
These types are not just clear-cut.
00:29:40.346 --> 00:29:41.696
There's an evolution.
00:29:41.700 --> 00:29:43.337
There's people that have
very few connections
00:29:43.338 --> 00:29:44.653
with the language groups
00:29:44.654 --> 00:29:46.943
and then progressively there's people
with more and more.
00:29:47.704 --> 00:29:49.037
And this increases.
00:29:49.037 --> 00:29:52.048
Which points to the fact,
that my cells are there.
00:29:52.735 --> 00:29:57.001
Which means I don't see the evolution
over time, ok?
00:29:57.819 --> 00:29:59.724
This is a limitation of my research.
00:29:59.725 --> 00:30:04.594
I just see the social network
of this person looked
00:30:04.594 --> 00:30:07.491
at a particular point in time.
00:30:07.925 --> 00:30:10.057
I don't know how it evolves over time.
00:30:10.058 --> 00:30:13.130
So, for myself, it's just there.
00:30:13.508 --> 00:30:18.702
It would be interesting
to see these different patterns
00:30:18.702 --> 00:30:20.771
that I have been observing.
00:30:20.771 --> 00:30:26.632
Maybe over time these connections
between languages maybe increasing.
00:30:28.862 --> 00:30:32.131
Now we have the integration
and union type
00:30:32.693 --> 00:30:37.128
where in this case you have a person
from an Arab country
00:30:37.129 --> 00:30:40.778
and green represents the friends
that are using Arabic
00:30:40.779 --> 00:30:45.155
and the friends using English are in pink,
but there's also violet
00:30:45.156 --> 00:30:46.837
there are bilinguals.
00:30:47.196 --> 00:30:51.534
That means there's a group
of English users
00:30:51.535 --> 00:30:57.187
and bilingual English - Arabic users
inserted in the group of Arabic, inside.
00:30:59.530 --> 00:31:01.289
That's the integration,
so they're integrated.
00:31:02.419 --> 00:31:07.726
And then I have a Greek guy,
who uses Greek and English
00:31:07.726 --> 00:31:09.446
and his Arabic friends.
00:31:09.446 --> 00:31:11.935
And in this case, you can see
it's sort of light blue
00:31:11.936 --> 00:31:16.788
representing Greek, so the friends
that tweet in Greek
00:31:16.789 --> 00:31:20.729
Pink again represents people tweeting
in English
00:31:21.353 --> 00:31:23.426
and there's a lot of bilinguals.
00:31:23.449 --> 00:31:26.994
So these kind of dark blues
represent the bilinguals.
00:31:26.995 --> 00:31:28.604
And these are two groups
00:31:28.605 --> 00:31:32.741
that if you've seen before,
the gatekeeper and the language bridge
00:31:32.742 --> 00:31:35.281
progressively getting closer and closer
00:31:35.282 --> 00:31:40.990
with more and more links
across languages.
00:31:41.184 --> 00:31:42.815
In this case, this is like the extreme.
00:31:42.816 --> 00:31:46.016
The links between the two languages
are so dense
00:31:46.017 --> 00:31:51.021
that you cannot almost distinguish
where the border is
00:31:51.021 --> 00:31:53.128
between the two language groups.
00:31:53.164 --> 00:31:58.534
And, interestingly, the border might be
even only noticeable
00:31:58.534 --> 00:32:01.406
because there's a lot of bilinguals
around it.
00:32:02.091 --> 00:32:04.924
And this is the union type
where they unite.
00:32:07.201 --> 00:32:09.806
And finally, the peripheral language type.
00:32:09.807 --> 00:32:13.690
This is a Brazilian guy,
the network of a Brazilian guy
00:32:15.324 --> 00:32:16.892
where you have--
00:32:16.893 --> 00:32:18.885
probably he lives in the United States
or something like that--
00:32:18.886 --> 00:32:23.192
because this guy has mostly
all this big group of friends
00:32:23.226 --> 00:32:24.850
tweeting in English.
00:32:26.532 --> 00:32:31.978
And then there's the side tentacle
running outside, using Portuguese.
00:32:34.702 --> 00:32:36.399
And this is like a periphery landscape.
00:32:36.400 --> 00:32:39.137
So, in the periphery there's a small group
of Portuguese language.
00:32:39.893 --> 00:32:45.233
Now, I forgot to mention that there's dots
that are light yellow or white.
00:32:45.286 --> 00:32:48.100
Those are the ones that have no data.
00:32:49.074 --> 00:32:51.270
So, I don't know
the language they're using
00:32:51.271 --> 00:32:53.382
because either their accounts are closed
00:32:53.383 --> 00:32:57.803
or for some reason, in between the collection
of data they closed the account.
00:32:59.307 --> 00:33:03.059
Mostly, the reason
is that they're private accounts
00:33:03.570 --> 00:33:05.640
where you cannot get the data from.
00:33:06.442 --> 00:33:08.755
I think somewhere I read
it was about 5 percent.
00:33:08.756 --> 00:33:10.216
I'm not sure.
00:33:10.216 --> 00:33:14.010
But for one reason or another,
I don't have that information.
00:33:16.563 --> 00:33:20.976
Now, why am I classifying them?
These networks?
00:33:22.785 --> 00:33:26.088
Well, the reason is that--
00:33:26.089 --> 00:33:28.793
well, there are some studies
that demonstrate that the social structure
00:33:28.794 --> 00:33:33.539
the structure of the social networks
influences the spread of information.
00:33:34.096 --> 00:33:36.457
How information disseminates
in the network.
00:33:38.553 --> 00:33:42.909
So, I'm just assuming
that these different structures
00:33:42.910 --> 00:33:46.382
are going to influence the spread
of information.
00:33:47.292 --> 00:33:49.750
But this is a study that has to be done.
00:33:49.929 --> 00:33:52.944
I cannot demonstrate that one
of these types
00:33:52.945 --> 00:33:55.681
facilitates the spread of information.
00:33:55.682 --> 00:34:02.330
I can only say that I am assuming,
so that potential study
00:34:04.200 --> 00:34:09.400
could just look at, for example,
if gatekeeper and language bridges
00:34:10.551 --> 00:34:16.231
are not as good for spreading information
as union and integration types.
00:34:20.178 --> 00:34:25.022
Right, we can just assume
because of the cross-language links
00:34:28.295 --> 00:34:33.380
so, how many links there are
or the ratio of discourse language
00:34:33.380 --> 00:34:38.331
may potentially facilitate information
diffusion in these cases.
00:34:39.944 --> 00:34:42.557
So, that study needs to be done.
00:34:42.607 --> 00:34:44.732
I cannot say what's going to happen!
00:34:44.732 --> 00:34:47.123
I just assume it's going to be like that.
00:34:49.178 --> 00:34:52.009
So that is the reason why I classify them.
00:34:52.498 --> 00:34:54.599
I have some network statistics.
00:34:55.969 --> 00:35:00.753
We've made about an 80 percent accuracy
guess, which is quite good,
00:35:00.753 --> 00:35:02.453
but the sample is small.
00:35:08.014 --> 00:35:10.961
So now, do you have any more questions
before I move past to the next study?
00:35:13.726 --> 00:35:15.444
man) I was curious as to how many--
00:35:15.444 --> 00:35:19.144
what was the selection process like
to find the 92 users?
00:35:20.324 --> 00:35:22.891
Well, this is what I've been spending
the beginning
00:35:22.892 --> 00:35:26.690
about just using two stopwords
from two different languages
00:35:26.691 --> 00:35:31.482
typing that in the search box in Google
and searching Twitter
00:35:31.482 --> 00:35:32.875
and then once--
00:35:32.876 --> 00:35:36.192
Basically you just go through
the list of results
00:35:36.193 --> 00:35:41.540
and start opening the profile,
counting the tweets.
00:35:42.327 --> 00:35:44.536
How many in this language,
how many in the other.
00:35:44.601 --> 00:35:46.640
And we put a threshold of 10 percent
00:35:46.640 --> 00:35:53.026
they had to have written 10 percent
of the tweets in a second language
00:35:53.228 --> 00:35:56.742
and you couldn't count retweets
or automatic posting.
00:35:57.937 --> 00:36:00.296
We also had to manually discard
these spammers.
00:36:01.535 --> 00:36:03.733
So, that was the process.
00:36:06.151 --> 00:36:09.536
(woman) And that's a paid search
through Google?
00:36:10.131 --> 00:36:12.601
No, that we did manually
00:36:12.717 --> 00:36:14.087
and then once--
00:36:14.088 --> 00:36:20.392
So the other thing you can say is you can
use these core multilingual users
00:36:20.938 --> 00:36:23.929
and then do what I did for behavior
in these social networks
00:36:23.929 --> 00:36:29.363
which is once you extract the friends
and extract the messages of the friends
00:36:30.669 --> 00:36:33.559
and automatically find the language
00:36:34.035 --> 00:36:36.522
then you can say "Oh, this person
is multilingual" automatically.
00:36:36.522 --> 00:36:41.099
You just process it and you can detect
a lot more multilingual people
00:36:41.183 --> 00:36:42.756
through that process.
00:36:42.757 --> 00:36:46.101
The paid process was sending these posts
00:36:46.101 --> 00:36:49.075
to the Google language
identification tool.
00:36:49.885 --> 00:36:55.010
So, what I did was clean each message
automatically.
00:36:55.544 --> 00:37:00.387
Basically, eliminating the hashtags
00:37:01.437 --> 00:37:05.230
and the mentions
that had an @ in front,
00:37:05.230 --> 00:37:10.074
symbols, URLs, all those things
I would automatically eliminate them
00:37:10.392 --> 00:37:13.777
and then with the rest of the message,
I'd send that to the Google API
00:37:14.125 --> 00:37:15.849
for language identification
00:37:16.009 --> 00:37:21.726
and the Google API would give me
a language level and a confidence binary.
00:37:21.726 --> 00:37:23.476
And that for each message.
00:37:23.485 --> 00:37:26.371
And then I built the algorithm
with the help of Jen Golbeck
00:37:26.372 --> 00:37:30.688
to decide, well I have 30 messages,
500 English
00:37:30.714 --> 00:37:35.420
10 million Spanish and then one in Swahili
which is unlikely
00:37:36.728 --> 00:37:39.954
and you had to decide
the confidence value--
00:37:39.955 --> 00:37:42.935
So I used rules, defined rules
00:37:42.936 --> 00:37:45.559
but it could be done
statistically I think.
00:37:46.097 --> 00:37:48.388
And write some statistical method
to decide
00:37:48.389 --> 00:37:51.869
"well this person actually is bilingual"
or whatever.
00:37:52.779 --> 00:37:54.429
That's the process.
00:37:54.477 --> 00:37:55.597
It's long!
00:37:55.788 --> 00:37:56.788
Yes.
00:37:58.026 --> 00:38:00.487
(woman) Hi, I understand
that you did it manually
00:38:00.488 --> 00:38:05.265
but currently in existing research field
is there any software
00:38:05.265 --> 00:38:08.489
that we can use to capture,
00:38:08.489 --> 00:38:11.935
to have access to all
these different tweets?
00:38:11.983 --> 00:38:15.400
And to capture the different categories?
[inaudible]
00:38:15.400 --> 00:38:18.472
Ok, so you mean the extraction?
00:38:18.912 --> 00:38:19.983
(woman) Yeah.
00:38:19.983 --> 00:38:21.226
No, I didn't do it manually.
00:38:21.227 --> 00:38:22.705
(woman) And the other,
I think the other part
00:38:22.706 --> 00:38:25.570
of your data presentation
is visualizations coming out
00:38:25.571 --> 00:38:27.132
like this graph.
00:38:27.132 --> 00:38:32.610
Can you show us what kind of research
do we have for social scientists
00:38:33.250 --> 00:38:35.478
to present the data in a visual form?
00:38:35.479 --> 00:38:37.461
This is a tool I would recommend.
00:38:37.461 --> 00:38:39.123
[inaudible]
00:38:39.123 --> 00:38:41.427
So, the first question.
00:38:42.572 --> 00:38:45.748
All the extraction from Twitter,
it was automatic.
00:38:46.265 --> 00:38:48.638
I didn't copy the tweets,
it was automatic.
00:38:48.855 --> 00:38:50.707
I used the Twitter API.
00:38:51.286 --> 00:38:54.849
They have a process
for registered developers
00:38:54.850 --> 00:38:57.205
and I extracted it automatically.
00:39:01.925 --> 00:39:05.777
Now, the tools, and I forgot
to put that in this slide
00:39:05.847 --> 00:39:09.444
but in the beginning,
when I showed you the first visualization
00:39:09.445 --> 00:39:11.605
I put the name of the tool in--
00:39:12.703 --> 00:39:17.644
I don't know if I translate well,
but I think it's G-E--
00:39:17.644 --> 00:39:23.785
You can see here, G-E-P-H-I,
I don't know how to pronounce it!
00:39:23.785 --> 00:39:26.997
["Jefy" I think...]
00:39:28.201 --> 00:39:32.216
So, this is the one I've used
for the visualizations
00:39:33.709 --> 00:39:36.871
and it's good because you can use it
on any platform.
00:39:36.872 --> 00:39:41.911
So both on a Mac or a PC or Linux.
00:39:44.829 --> 00:39:46.696
Now, it has limitations for...
00:39:47.209 --> 00:39:50.778
mostly for network statistics
in my opinion.
00:39:54.237 --> 00:39:57.061
The other one, that is very popular
is Node XL.
00:39:57.062 --> 00:40:00.548
And in fact it was developed
here in the ATI lab.
00:40:01.773 --> 00:40:04.092
In the lab where I work.
00:40:05.190 --> 00:40:06.937
So, they collaborated with Microsoft.
00:40:06.938 --> 00:40:09.867
It's a template for Excel
00:40:11.076 --> 00:40:12.552
and it allows--
00:40:12.553 --> 00:40:17.849
In fact they are still adding new features
and there's two people working on it
00:40:18.235 --> 00:40:19.665
in the lab.
00:40:19.739 --> 00:40:23.984
But the reason I haven't used it here,
is because I have a Mac
00:40:24.264 --> 00:40:29.166
and also there's another reason
I like this positioning algorithm
00:40:31.302 --> 00:40:32.807
and this is...
00:40:32.808 --> 00:40:37.014
this is another issue
I haven't talked about
00:40:37.124 --> 00:40:40.476
is how you actually place the dots.
00:40:40.476 --> 00:40:47.182
And actually these algorithms for layout
use force-directed schemes
00:40:48.820 --> 00:40:50.507
like in physics science.
00:40:50.584 --> 00:40:53.598
So if a node has a lot of links
with another node
00:40:53.599 --> 00:40:56.980
they put it closer,
so it's like there's forces
00:40:56.981 --> 00:41:00.276
or strings attaching the nodes.
00:41:00.858 --> 00:41:04.293
And depending on how many strings
there are, they're closer or farther.
00:41:04.605 --> 00:41:07.933
There's physics science rules
for placing them.
00:41:07.959 --> 00:41:09.508
But there's different algorithms
00:41:09.509 --> 00:41:14.981
but the other reason I chose Gephi
is that it has an algorithm
00:41:15.336 --> 00:41:20.899
specifically in this tool
that places my language groups separately
00:41:20.943 --> 00:41:24.338
more than any other algorithm
that I could use in Node XL.
00:41:24.339 --> 00:41:29.142
And it was more useful
to see the groups separated.
00:41:30.407 --> 00:41:33.186
But you can use both
depending on what you want to do.
00:41:33.187 --> 00:41:35.905
They both have weaknesses and strengths,
00:41:35.931 --> 00:41:38.847
different depending
on what you have to do.
00:41:40.592 --> 00:41:46.628
Node XL has more features
for processing many networks
00:41:48.068 --> 00:41:51.147
and extracting network statistics
for many networks at the same time.
00:41:52.217 --> 00:41:57.372
And it has a lot of interesting features,
maybe this is more manual.
00:41:58.528 --> 00:41:59.998
I don't know.
00:42:00.215 --> 00:42:04.670
Somebody called it
"the Photoshop of visualization".
00:42:09.125 --> 00:42:13.580
So I'm going to briefly comment
on the factor analysis.
00:42:13.892 --> 00:42:18.627
The point here, what I want to see
is multilingual users of Twitter
00:42:20.784 --> 00:42:23.663
are aware of their audience in a way.
00:42:24.848 --> 00:42:29.480
And they somehow perceive
how many followers
00:42:29.480 --> 00:42:32.205
of this language or the other they have.
00:42:32.761 --> 00:42:35.501
Maybe not very consciously,
00:42:37.641 --> 00:42:39.763
but they perceive something.
00:42:39.932 --> 00:42:42.468
So, I went to see how this social network
00:42:42.469 --> 00:42:46.691
the fact that there's many languages
or just one in the social network
00:42:47.628 --> 00:42:52.814
can affect the choice of language in this person,
the ego person.
00:42:54.638 --> 00:42:57.734
So, I actually did a lot of testing,
different variables,
00:42:57.735 --> 00:43:01.434
but I'm just going to focus
on the essence,
00:43:01.434 --> 00:43:05.729
which is I have my dependent variable
which is the proportion of English
00:43:05.730 --> 00:43:11.064
used by the ego has 50 posts,
maybe 60 percent of them are in English
00:43:11.883 --> 00:43:14.409
and 40 percent in Spanish,
I don't know.
00:43:14.693 --> 00:43:18.630
And then they have the factor
of how many users in the network
00:43:18.631 --> 00:43:21.381
are in English
and how many are using other languages.
00:43:21.597 --> 00:43:24.274
And then the multilingual index
of the network
00:43:24.275 --> 00:43:26.153
- and this is my favorite part -
00:43:26.153 --> 00:43:29.674
because it's basically saying
00:43:29.774 --> 00:43:35.900
"is multilingualism encouraging English
as a lingua franca?"
00:43:37.026 --> 00:43:41.693
especially on Twitter, where we have these
public posts that anybody can read.
00:43:43.339 --> 00:43:47.418
So anyway... I'm not going to go
into the technical details
00:43:47.940 --> 00:43:50.516
of bi-nodal statistical interpretation.
00:43:50.517 --> 00:43:55.415
What I wanted to do is
that in these combined effects
00:43:56.046 --> 00:44:00.500
of the factors,
which one was more important?
00:44:00.998 --> 00:44:03.208
Was heavier than the others?
00:44:03.289 --> 00:44:07.340
Had more weight in defining these
proportional [inaudible] used by the ego.
00:44:08.750 --> 00:44:11.242
I tried other factors,
00:44:11.243 --> 00:44:14.237
I also looked at the use
of non-English language
00:44:15.370 --> 00:44:18.137
In the end... there are certain,
00:44:19.620 --> 00:44:21.423
I mean, they're obvious somehow.
00:44:21.424 --> 00:44:23.602
I think it's more interesting the process
of what I've learned
00:44:23.603 --> 00:44:25.908
than the results themselves.
00:44:27.166 --> 00:44:30.031
Because basically what I've learned
is that, yeah,
00:44:31.040 --> 00:44:32.931
the English use of the network
00:44:32.931 --> 00:44:36.338
is encouraged by the use
of English by the ego
00:44:36.338 --> 00:44:40.756
and in a certain way it's so important
that any other factor
00:44:40.757 --> 00:44:44.029
is really not that important.
00:44:45.231 --> 00:44:48.980
And even the second most important,
the multilingual index
00:44:49.770 --> 00:44:54.830
was so light compared with
the heavy impact of English
00:44:55.575 --> 00:44:57.107
used in the network.
00:44:57.608 --> 00:45:00.294
But what I thought was really interesting
00:45:00.295 --> 00:45:03.329
was how do you define
the multlinguality of a network?
00:45:03.968 --> 00:45:07.295
And with this I got help
from Jordan Boyd-Graber
00:45:07.296 --> 00:45:09.336
who is also in the iSchool
00:45:09.337 --> 00:45:14.331
and in the lab for computational lab,
the information processing lab
00:45:14.332 --> 00:45:15.332
here in Maryland.
00:45:15.333 --> 00:45:17.556
He helped me
with all these technical aspects.
00:45:18.183 --> 00:45:20.590
And he was the one suggesting
"Well, why don't you look--"
00:45:20.590 --> 00:45:24.620
"instead of just looking at the number
of languages in the network...
00:45:24.620 --> 00:45:28.694
"because sometimes you get
wrongly detected languages...
00:45:28.695 --> 00:45:30.231
like Swahili. Well, no one was really
speaking Swahihi in this network.
00:45:33.201 --> 00:45:37.029
There were technical challenges,
like I explained to you.
00:45:38.122 --> 00:45:42.248
So maybe there's a high number
of languages in the network
00:45:42.249 --> 00:45:44.189
but the network is mostly monolingual.
00:45:44.190 --> 00:45:49.064
Mostly everybody uses English
and just a few people maybe use others
00:45:49.633 --> 00:45:52.337
or maybe just it got wrongly detected.
00:45:52.338 --> 00:45:54.810
And maybe you're just saying
00:45:54.811 --> 00:45:57.047
"Oh yeah, there's ten languages
in the network!"
00:45:57.048 --> 00:45:59.548
and actually it's not
a very multilingual network at all.
00:45:59.549 --> 00:46:02.650
So, we came up with this, the entropy.
00:46:03.390 --> 00:46:06.495
And this is a physics concept
that measures the disorder
00:46:06.496 --> 00:46:07.866
in a system.
00:46:07.866 --> 00:46:11.452
And in this case, the entropy
would be my multilingual index
00:46:11.453 --> 00:46:17.104
and what it's doing is providing a value
between 0 and 1
00:46:17.364 --> 00:46:23.105
So, with 0 it's a very homogeneous system
everyone speaks the same language
00:46:23.549 --> 00:46:26.900
and if it's closer to 1,
it's really a heterogeneous
00:46:26.972 --> 00:46:28.911
and it places an importance
00:46:28.912 --> 00:46:31.823
in how many people
are using its language.
00:46:32.235 --> 00:46:36.480
So, this is the equation,
just to show you it.
00:46:38.009 --> 00:46:40.641
And it takes into account the number
of languages in the network
00:46:40.642 --> 00:46:45.427
and then one of the variables
is how many nodes in that language
00:46:45.498 --> 00:46:48.337
that there are divided by the total number
00:46:48.338 --> 00:46:50.971
and this is what gives the proportion
for example.
00:46:52.889 --> 00:46:56.556
So just to let you know
that there's interesting lessons
00:46:56.557 --> 00:46:57.977
from this study.
00:46:57.982 --> 00:47:00.479
Despite the research not being exciting!
00:47:00.549 --> 00:47:02.881
And this is what I'm doing right now.
00:47:04.816 --> 00:47:08.002
So, the intrinsic characteristic
of the message
00:47:08.484 --> 00:47:11.038
how that influences the language choice.
00:47:11.062 --> 00:47:16.370
First, I'm wondering,
because I just saw it in the content
00:47:19.070 --> 00:47:22.495
are replies encouraging people
to use their native language?
00:47:22.992 --> 00:47:27.150
And public posts encouraging people
to use English as a lingua franca?
00:47:27.759 --> 00:47:30.251
This is one that showed up the same.
00:47:30.252 --> 00:47:34.151
And I changed the handle,
for privacy reasons...
00:47:34.549 --> 00:47:37.709
So this is the reply to somebody
and it's in Arabic.
00:47:38.443 --> 00:47:41.001
And this is a public posting
and it's in English.
00:47:42.414 --> 00:47:45.501
Now, the thing I'm looking at
is public analysis
00:47:45.502 --> 00:47:50.314
and I'm considering with Jordan
to do some automatic topic analysis
00:47:50.706 --> 00:47:54.215
because there's many languages,
so I cannot decode it all
00:47:54.782 --> 00:47:56.503
in many of them.
00:47:56.507 --> 00:47:58.459
Only in three, maybe four...
00:47:59.910 --> 00:48:01.406
So, I'm wondering,
00:48:01.407 --> 00:48:04.213
are technology topics favoring
the use of English?
00:48:04.600 --> 00:48:10.072
And other topics,
international news maybe?
00:48:11.308 --> 00:48:16.147
Whereas other topics
like national news or songs
00:48:16.148 --> 00:48:19.407
they might be encouraging the use
of native languages.
00:48:20.566 --> 00:48:22.904
And then I'm looking
if there's translations
00:48:22.904 --> 00:48:26.845
or if there's cross-cultural words
that you can detect.
00:48:27.324 --> 00:48:29.111
For instance, this person
is writing in English
00:48:29.112 --> 00:48:33.313
but it recommending a visit to a museum
in the city of Lille in France.
00:48:33.767 --> 00:48:38.830
So this person knows the city in France,
knows that to visit the museum
00:48:38.987 --> 00:48:40.556
you go there.
00:48:40.559 --> 00:48:43.089
And this is what I call
cross-cultural words.
00:48:44.239 --> 00:48:49.095
[What I kind of found] is that surprisingly
there's not many translation behaviors
00:48:49.096 --> 00:48:52.589
going on, despite these people
being multilingual.
00:48:53.001 --> 00:48:56.264
And this is what is going to trigger
some reflections.
00:49:00.289 --> 00:49:02.085
How am I doing on time?
00:49:04.172 --> 00:49:05.646
(woman) 1:22.
00:49:05.646 --> 00:49:10.050
(man) Umm, it's usually an hour long...
00:49:10.450 --> 00:49:14.358
So, I will go on with my reflections.
00:49:14.358 --> 00:49:18.266
to encourage some thoughts.
00:49:18.266 --> 00:49:22.027
So the greatest connecting power
is the will of users who want
00:49:22.027 --> 00:49:23.317
to be connected.
00:49:23.317 --> 00:49:28.201
This is a really nice quality,
because the communities of interest
00:49:28.290 --> 00:49:32.012
in social media, in Twitter
is what is bringing people
00:49:32.013 --> 00:49:33.701
from different countries, together.
00:49:34.794 --> 00:49:41.151
And also experiences,
like the Voluntweeters,
00:49:42.095 --> 00:49:45.815
so after the earthquake in Haiti,
there were these spontaneous
00:49:45.816 --> 00:49:48.972
self-organizations of Twitter users
for translating tweets
00:49:50.213 --> 00:49:53.755
and they called themselves Voluntweeters,
there's a paper about that--
00:49:53.826 --> 00:49:59.151
So this is the triggering
of social connections
00:50:00.820 --> 00:50:04.486
across countries, across borders
and across languages.
00:50:06.759 --> 00:50:10.300
But even when the social structure
could potentially facilitate
00:50:10.301 --> 00:50:13.375
information diffusion
and cross-language linking
00:50:14.558 --> 00:50:16.731
this condition is not sufficient.
00:50:16.732 --> 00:50:19.720
There are other factors
like the design of the interfaces
00:50:19.721 --> 00:50:22.479
and the design of systems
that can influence...
00:50:23.145 --> 00:50:27.438
can promote, or not translation behaviors
and cross-cultural awareness.
00:50:28.293 --> 00:50:31.503
And the Wikipedia
of cross-language linking
00:50:31.504 --> 00:50:35.113
you have links for many languages
for every article.
00:50:37.257 --> 00:50:41.061
We also still acknowledge the dynamic
language preferences of multilingual users
00:50:41.790 --> 00:50:44.145
so they could address their messages
to the appropriate audience.
00:50:44.146 --> 00:50:47.187
I like the solution of Google+
with their circles
00:50:47.880 --> 00:50:51.890
where I can put my friends and family
in Spain in a circle
00:50:51.891 --> 00:50:54.559
and write them in Spanish.
00:50:54.739 --> 00:51:00.633
And then the recommendation of people
based on language profile
00:51:01.437 --> 00:51:04.134
would be useful for this spontaneous
self-organization.
00:51:05.708 --> 00:51:08.057
So, these are some of the things.
00:51:08.143 --> 00:51:10.455
The impact of mediation.
00:51:10.782 --> 00:51:13.206
Global Voices is
an international community of bloggers
00:51:13.207 --> 00:51:18.303
that connect bloggers and citizens
from around the world
00:51:18.814 --> 00:51:20.504
in different languages.
00:51:21.171 --> 00:51:22.580
And Scott Hale
00:51:22.581 --> 00:51:27.353
a student from Oxford University
led a very interesting study
00:51:27.354 --> 00:51:33.960
after the earthquake in Haiti about blogs
in Spanish, Japanese and English
00:51:35.561 --> 00:51:38.542
and he looked
at the cross-language linking
00:51:38.543 --> 00:51:41.388
and focusing on this topic
over time.
00:51:41.488 --> 00:51:45.495
And he discovered that 50 percent
of the cross-language linking
00:51:45.496 --> 00:51:48.304
was happening through this platform,
Global Voices.
00:51:49.062 --> 00:51:51.941
So, it had a very big impact
in the language links.
00:51:54.170 --> 00:51:57.857
And finally, social media,
big media outlets,
00:51:57.858 --> 00:52:01.592
people are interconnected
in these complex networks
00:52:04.693 --> 00:52:08.945
and underlying is this language ecosystem.
00:52:09.058 --> 00:52:12.786
So we have the language ecosystem,
and on top of that
00:52:12.787 --> 00:52:15.296
we have the social media ecosystem.
00:52:15.305 --> 00:52:20.200
People would share a video from YouTube
on Twitter, or news on Facebook.
00:52:21.302 --> 00:52:26.011
What happened if we integrate
in this ecosystem
00:52:26.517 --> 00:52:30.518
these platforms, like Global Voices,
like Universal Subtitles
00:52:30.519 --> 00:52:34.327
which is a platform
for crowdsourcing subtitling of videos
00:52:34.328 --> 00:52:37.108
and translation of subtitles
for videos.
00:52:38.050 --> 00:52:42.222
If you integrate that and this
starts connecting, starts building paths
00:52:42.223 --> 00:52:45.743
between languages,
that didn't exist before.
00:52:45.744 --> 00:52:50.955
So I think we should make it easy
for multilingual people to translate
00:52:50.955 --> 00:52:55.187
and subtitle all the content they like,
their favorite content
00:52:56.003 --> 00:53:00.326
and share it with the appropriate audience
so they can start connecting
00:53:00.327 --> 00:53:03.114
the language islands of the internet.
00:53:03.145 --> 00:53:06.219
And that way stories will travel
all over the world.
00:53:09.204 --> 00:53:11.950
Particularly I would like to thank
Jen Golbeck, my adviser
00:53:11.951 --> 00:53:14.337
and Fulbright for supporting
this research.
00:53:14.477 --> 00:53:19.206
And then I open the space
for questions and your ideas
00:53:19.488 --> 00:53:21.780
if this has triggered some thoughts.
00:53:24.140 --> 00:53:25.972
(woman) I have a question
about how this relates
00:53:25.973 --> 00:53:28.112
to your Yahoo award.
00:53:29.468 --> 00:53:35.076
Well, they have the Internet Experiences
lab in California.
00:53:35.078 --> 00:53:36.428
And they--
00:53:36.460 --> 00:53:40.213
So, we tend to think
maybe it's a super tiny place
00:53:40.213 --> 00:53:42.630
but actually there are fields
00:53:42.631 --> 00:53:44.818
and I applied for the social systems.
00:53:45.121 --> 00:53:48.967
The social systems are a category.
00:53:49.068 --> 00:53:54.686
And I think that was embedded
in the Internet Experience lab
00:53:56.739 --> 00:53:58.452
and yeah, they liked it.
00:53:58.516 --> 00:54:01.530
(man) But is it this
work that they are interested in?
00:54:01.813 --> 00:54:02.883
Yes.
00:54:02.884 --> 00:54:04.022
- The languages?
- Yes.
00:54:04.022 --> 00:54:07.726
Well, now I have results,
because I wrote up reports
00:54:09.496 --> 00:54:11.548
about what my work was about.
00:54:16.758 --> 00:54:17.968
Great.
00:54:22.055 --> 00:54:22.879
Yes?
00:54:22.879 --> 00:54:25.682
(woman) I was thinking about
if you analyzed the place...
00:54:25.682 --> 00:54:30.689
like if there's any relationship
between tweeters and tweets
00:54:31.056 --> 00:54:33.624
and the place that the people are.
00:54:35.883 --> 00:54:39.760
I mean, because it's not the same
being a Brazilian in Brazil
00:54:39.761 --> 00:54:43.197
and tweeting in Portuguese
or being Brazilian in the US
00:54:43.198 --> 00:54:45.330
and tweeting in Portuguese--
00:54:45.950 --> 00:54:49.249
There's many, many factors
that I haven't looked at.
00:54:50.126 --> 00:54:51.971
It's not part of your study?
00:54:52.300 --> 00:54:54.447
But because I had to scope it somehow.
00:54:54.448 --> 00:54:56.108
There's so many factors.
00:54:56.710 --> 00:54:59.993
Geography was one that I was originally
intending to look at
00:55:00.097 --> 00:55:04.458
but I found there were so many problems
to actually get the right geography
00:55:04.459 --> 00:55:06.652
the right geolocation.
00:55:08.154 --> 00:55:12.136
The problem is that I didn't originally
collect the geolocation.
00:55:12.137 --> 00:55:15.898
I think only a small percentage
of messages have...
00:55:16.457 --> 00:55:18.297
geolocated information.
00:55:18.902 --> 00:55:20.795
I'm not sure about the percentage there.
00:55:20.796 --> 00:55:24.690
So there's only a small percentage
of messages that have geolocation.
00:55:25.173 --> 00:55:27.604
There's issues with the accuracy...
00:55:28.041 --> 00:55:31.147
What I have collected is the information
in their profile
00:55:31.931 --> 00:55:35.462
they can put the information
about the place,
00:55:35.493 --> 00:55:39.572
but sometimes it's more
or less trustworthy,
00:55:39.573 --> 00:55:42.828
sometimes there's nothing,
and sometimes there's just crazy stuff.
00:55:43.210 --> 00:55:44.710
(audience laughs)
00:55:46.545 --> 00:55:49.735
So, something absolutely has to be there.
00:55:50.419 --> 00:55:55.249
If I wanted to expand this,
geography would be a nice place to go!
00:55:55.279 --> 00:55:56.609
(woman) Ok.
00:55:59.863 --> 00:56:00.631
Yes?
00:56:00.631 --> 00:56:01.710
(man) Could you say a little bit more
00:56:01.710 --> 00:56:04.946
I think you said about the visualization
choices you made?
00:56:04.964 --> 00:56:06.224
Oh yes, well...
00:56:08.033 --> 00:56:11.117
I tried this tool, the Node XL,
00:56:11.118 --> 00:56:13.284
I used both Node XL and Gephi.
00:56:13.522 --> 00:56:14.522
There's more...
00:56:16.109 --> 00:56:20.202
I think there's, I don't remember the name
there's one that was developed
00:56:20.202 --> 00:56:21.854
here in Maryland
00:56:21.854 --> 00:56:24.163
but it's not as user-friendly.
00:56:26.108 --> 00:56:29.563
But I've forgotten the name,
I will have to look it up.
00:56:29.895 --> 00:56:33.872
And there's a lot of tools
that are for really technical people
00:56:34.696 --> 00:56:37.156
that are handling millions of nodes.
00:56:37.528 --> 00:56:40.615
Because with these tools,
for social scientists or humanists
00:56:40.615 --> 00:56:42.295
maybe they are not.
00:56:42.316 --> 00:56:48.685
Some tools can have maybe 300-400 nodes
and still be understandable.
00:56:51.115 --> 00:56:55.622
But if you go beyond that,
actually visualizations get crazy
00:56:56.058 --> 00:57:02.088
and even for more technical tools
for more technical people
00:57:02.563 --> 00:57:07.061
there are hundreds or millions,
they cannot do visualizations
00:57:08.349 --> 00:57:11.870
at some point they just give you
statistical measures.
00:57:13.729 --> 00:57:15.156
I have to leave it out.
00:57:15.156 --> 00:57:17.051
I have a list of tools and that
00:57:17.051 --> 00:57:20.598
but if I need the names,
I need to go through everything.
00:57:22.596 --> 00:57:25.479
(woman) But yours was Mac-accessible?
00:57:25.479 --> 00:57:31.585
Yes, this Gephi tool is Mac-accessible,
you can use it with Microsoft
00:57:31.792 --> 00:57:34.446
with Mac and with Linux.
00:57:35.905 --> 00:57:37.979
And I forgot to say,
it's open source.
00:57:43.480 --> 00:57:48.839
(woman) Did you find
studying languages and internet
00:57:48.840 --> 00:57:52.681
was like a place, unexplored?
00:57:52.948 --> 00:57:55.208
Like here in the United States?
00:57:55.378 --> 00:58:00.001
Like when you began studying
or analyzing this
00:58:00.002 --> 00:58:04.303
you felt that a lot of people
are doing this
00:58:04.303 --> 00:58:06.200
or nobody is doing this
00:58:06.200 --> 00:58:08.352
and I'm the first one trying to--
00:58:08.435 --> 00:58:13.114
I'm not the first one,
but it's a very new area
00:58:13.114 --> 00:58:14.971
to be exploring.
00:58:15.033 --> 00:58:16.983
So, it's very exciting
because of that.
00:58:17.012 --> 00:58:18.797
Because there's so many
unanswered questions
00:58:18.798 --> 00:58:23.785
and I find that surprisingly enough
the United States is not paying so much attention
00:58:23.786 --> 00:58:26.053
about multilinguality issues
00:58:26.053 --> 00:58:31.002
And I think that language policies
are very monolingual-oriented
00:58:31.003 --> 00:58:32.948
but it's terrible
00:58:33.043 --> 00:58:37.182
because there's a whole lot
of multilinguality in this country.
00:58:37.183 --> 00:58:41.270
There's so many people
speaking different languages
00:58:42.548 --> 00:58:45.290
that I'm so amazed
about that contradiction.
00:58:45.780 --> 00:58:48.727
Because in Europe,
it's an obvious challenge for us
00:58:49.388 --> 00:58:51.907
because we need to understand each other
between all these countries
00:58:51.907 --> 00:58:53.567
of the European Union.
00:58:53.567 --> 00:58:58.499
And there's a lot of money invested
in research that relates to multilinguality
00:58:58.691 --> 00:59:00.738
and communication in languages
00:59:00.738 --> 00:59:04.557
and technology in particular,
cross-language systems
00:59:04.558 --> 00:59:09.030
and in libraries there's a lot of work
going on.
00:59:09.400 --> 00:59:13.942
There's investment in the research.
00:59:14.565 --> 00:59:18.405
So yeah, maybe in terms of investment
00:59:18.405 --> 00:59:22.115
the European Union is
not a bad place to be.
00:59:22.322 --> 00:59:24.109
Better than the United States!
00:59:24.110 --> 00:59:27.445
But at the same time,
what I find interesting
00:59:27.446 --> 00:59:33.323
is that here when I talk about it
people are really interested
00:59:35.313 --> 00:59:38.376
and interested in the subject
and excited about it.
00:59:38.458 --> 00:59:41.294
Maybe in Europe it looks more
like old news.
00:59:41.294 --> 00:59:43.796
Like "yeah, we already know that."
00:59:44.135 --> 00:59:45.665
(audience laughs)
00:59:45.674 --> 00:59:49.580
So I find that it's exciting
to be seeing the audience
00:59:49.629 --> 00:59:52.226
like "Oh yeah!"
It's so new.
00:59:52.666 --> 00:59:54.026
*(woman) Yes.
00:59:58.653 --> 01:00:03.146
(woman) As the emerging view
of research in the United States
01:00:03.146 --> 01:00:09.892
can you show me which institutions
or which area of academic institutions
01:00:11.798 --> 01:00:14.748
actually have more invested
in this topic in the US?
01:00:16.262 --> 01:00:18.916
I'm not sure about the institutions.
01:00:20.572 --> 01:00:25.978
What I know, particularly,
in Indiana there's work
01:00:26.510 --> 01:00:29.107
because Susan Herring
is a researcher there.
01:00:30.797 --> 01:00:32.891
She has inspired my work.
01:00:32.891 --> 01:00:35.607
She published a book
The Multilingual Internet
01:00:35.687 --> 01:00:40.953
and she has done research on blogs,
also communities
01:00:41.891 --> 01:00:45.251
of different languages connecting blogs
in the blogosphere.
01:00:45.251 --> 01:00:51.058
So she has been one of the ones,
one of the first tackling these issues
01:00:51.144 --> 01:00:54.720
and she's still going
and she's doing something.
01:00:54.896 --> 01:00:59.399
So, it's the University of Indiana,
I think.
01:01:00.914 --> 01:01:03.348
Yeah, Susan Herring.
Look for her!
01:01:06.095 --> 01:01:09.181
And also at the same university
there's Paolillo.
01:01:10.156 --> 01:01:12.793
He's also doing research
in this area
01:01:12.826 --> 01:01:18.869
and he actually published for UNESCO
for research on language diversity
01:01:18.945 --> 01:01:20.275
on the internet.
01:01:21.785 --> 01:01:23.479
So Susan Herring and Paolillo,
01:01:23.480 --> 01:01:25.444
they are at the same university.
01:01:26.736 --> 01:01:30.058
Those are my inspiring ones.
01:01:33.682 --> 01:01:37.270
Well, at Harvard at the Berkman Center
of Internet and Society also did
01:01:37.270 --> 01:01:38.639
this mapping of the blogs.
01:01:38.640 --> 01:01:40.649
But they don't focus on languages.
01:01:41.700 --> 01:01:45.279
But there's tangential thing
around there.
01:01:49.387 --> 01:01:51.428
(man) One more question?
01:01:53.560 --> 01:01:54.748
Well, thank you very much!
01:01:54.749 --> 01:01:55.749
Thanks!
01:01:55.759 --> 01:01:57.661
(audience applauds)