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Flux of MEME - Description of Work

results achieved during the 1st semester of research - keywords: semantic web, twitter, clustering, geo, topic extraction
by

thomas alisi

on 7 March 2011

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Transcript of Flux of MEME - Description of Work

it is said that geo tagging is growing
still it represents around 1% of published content from twitter blog, dec 2010:
"Twitter users now send more than 95 million Tweets a day, on just about every topic imaginable." first big problem: fetch geo-localized data and create clusters of concepts in time/space axis
status: solved step 1: fetch data from twitter attempt n.2: access a continuous flow of data through twitter streaming API
the client does not need to perform a specific query
all tweets are fed through the client to our database
the stream can be tweaked to filter specific keywords/locations
keyword filtering can still be applied with "wikiminer" expansion

main problems:
twitter gives limited access to content (account "spritzer" has access to approx. 1% of total tweets) step 2: improve quality of results problem:
too much heterogeneous data carrying too little information

solution:
filter geo-localized content
enrich data with related links
filter related links to meaningful content only step 3: store content locally the database needs to store all the strucure needed for fetched data + cluster structure
a flexible architecture of DAOs allowed subsequent interventions on the database for refinements of the structure step 4: clusters! (at last) create time slices read all the posts + links in the timeline create geo-clusters using
hierarchical agglomeration create semantic-clusters using Latent Dirichlet Allocation step 5: web prototype what's next? Flux of MEME, the idea behind:
analyze clusters of concepts and understand how they move in space and time attempt n.1: access data through twitter search API
the client performs a specific query
a specific query implies the use of a limited amount of text/concepts
hence concepts must be expanded using clever algorithms
expansion of concepts was implemented using "wikiminer" library

main problems:
the client needs to wait results from twitter live search, wasting most of the time on hold
the twitter API gives access to a limited time frame, running 1 week in the past at most step 1: fetch data from twitter 1. fetch data 2. create geo-clusters 3. extract topics 4. analyze stats
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