Lu Chen @ http://knoesis.wright.edu/

Understanding User’s Geographic Context in Twitter

What are people thinking and doing in a given time and place?

Sentiment

Event

NOW?

User’s Geographic Context in Twitter

User location in profile Where is the user?

Geo-tags attached with the tweets Where is the user now (when the tweet is created)?

Places mentioned in the tweets ?

How are they related to each other? How can they be helpful to disambiguate or even predict each other?

Identifying and Disambiguating the Place Mentioned in Tweets

How to identify? Linked Geo Data

How to disambiguate? Other place names mentioned in tweets

In the same tweet In the tweets sent by the same user

Geo-tag of the tweet User location

Any doubt?

It’s not as easy as I was thinking… In fact, it’s pretty hard…

Start from collecting data Key words? Geo tags? User s?

Wikipedia: Top 10 most common U.S. place namesTracked 5000 usersCollected 2,187,205 tweets in total

Some Facts about the Data7.12% (155,705) tweets mention place names (according to

LGD)1.97% (42,988) tweets have geo-tags57.36% (2,868) users provide location information (might

be invalid)

Some Facts about Linked Geo DataBased on the Open Street Map.org planet file from 6th

April 201166 million triples, 10GBVirtuoso SPARQL endpoint:

http://knoesis-twit.cs.wright.edu:8890/sparql

Location DisambiguationKnowledge from LGD

Label, type, is_in, population, latitude, longitude For each place with its disambiguator

Is_in relations Minimize the minimum bounding box Minimize the distances

Type and populationhttp://www.movable-type.co.uk/scripts/latlong.html

Next StepRefine the algorithmEvaluationVisualization

What I LearnedFrom reading papers in this areaFrom using Linked Geo DataFrom coding, trying different tools and packages…From all these difficultiesFrom thinking, including thinking about what I learned

When I am thinking hard…