Hi.
This is Mingyang Li from the University of Pennsylvania.
I'm going to present our paper on "Studying Politeness Across
Cultures using English Twitter and Mandarin Weibo."
Politeness manifests differently in different cultures.
That is to say, different cultures
have different understandings and expressions
of politeness strategies.
If we assume that other cultures make the same choices,
we may misunderstand each other.
For this paper, we focus on politeness
across the languages.
Especially, we consider the American English and Mandarin
Chinese that is used in mainland China.
Here are some examples of misunderstanding.
In Mandarin Chinese, it is common to say, come and eat
with us.
Or I'll get mad at you.
And this may sound actually rude in English
but is actually an expression of hospitality
in Mandarin speakers.
On the English side, many people would say, great job,
or keep it up when they want to encourage their friends.
Well, this may sound really condescending
to a Chinese speaker.
This is because in some parts of the Chinese culture,
it is the privilege of the people with a higher status
to comment on or to judge other people's performance.
Now let's talk about the methods.
So first we want to gather up all the data
sets that we are going to use.
We take Weibo posts posted by users
claiming to be from mainland China as our Chinese corpus.
We also take Twitter tweets that are geotagged within the United
States as our English corpus.
Next we had native speakers annotate our corpus.
We obtained two labels for each of the posts in each data set.
And we had 5,300 posts annotated on each side
of Weibeo and Twitter.
Next, we counted the words by category
according to three cycle linguistical dictionaries.
The first we used is called Linguistic Inquiry and a Word
Count which maps words into topics, functional words,
and other features.
Another dictionary we used is the EmoLex
which maps words into emotions such as happy and disgust.
Lastly, we also developed our own lexicon
that we named PoliteLex.
It maps words to politeness or impoliteness strategies.
Here is a preview of what each Twitter
post would look like if counted according to LIWC.
Here is our politeness lexicon.
It is designed to work on both English and Chinese.
Here are some cool findings.
Almost all usages of pronouns correlate with impoliteness
in Mandarin Chinese.
This agrees with the fact that the Chinese language
is pro-drop.
That is to say, pronouns are usually
dropped in spoken Chinese.
If someone started using pronouns out of the blue,
they risk of sounding a bit out of place.
Second, it is polite to blend in.
For example, some posts may say, hey, we're on the same boat.
That is a show of affiliation.
This is more true on the Chinese side than on the American side.
This agrees with the fact that China
has a collectivist culture.
So Chinese focus more on the future.
This finding agrees with the Hoftstede's model
of cultural dimensions.
Finally, looping back to one of our examples
of misunderstanding, praises can be rude in the Chinese culture.
The PoliteLex feature of praise clearly shows this tendency.
After the correlational studies, we also ran predictive studies.
We trained predictors.
We support vector machines on two data sets.
This done for the politeness corpus and the Twitter label
politeness corpus we introduced a moment ago.
Here's the F1 scores tested on the Stanford politeness corpus.
We can see that with the combination of LIWC
and the PoliteLex, we are achieving
a 3% enhancement from the Stanford politeness API.
The Stanford potiteness API is an estimator
that uses both [INAUDIBLE] based features and the [INAUDIBLE]
based features.
We are happy to see that using only lexical based features,
we are able to outperform the Stanford politeness API.
Next, we have the F1 scores for predicting the politeness
on our Microblog Corpora.
Here we also see that the combination of LIWC, EmoLex,
and the PoliteLex achieves the best performance overall
on both sides.
In particular, on Twitter we achieved
almost an 80% enhancement from the Stanford politeness API.
We still have future work to be done.
Mainly detecting and removing biases in the data
sets collected and making the features in PoliteLex
to be more equivalent and comparable across cultures.
That is all.
Thank you.
And our code can be found at this link.