Emerging Topic Detection on Twitter (Cataldi et al., MDMKDD 2010)

Padmini Srinivasan

Computer Science Department Department of Management Sciences

http://cs.uiowa.edu/~psrinivapadmini-srinivasan@uiowa.edu

Twitter

• 2 to 3 new users/second!!• 175 million users as of today• 95 million tweets written each day• 300 employees and hiring!• “what are you doing” “what’s happening”• Daily chatter, URL sharing, news– Distributed/local reporting; no filters; no editing– Fastest, lowest level information service

• Not a social network but an information network

Aggregators & others

• Plenty: – Tweetmeme

• Separates the kinds of media linked to (video, image..)

– Twitter search– Twistori

• Aggregates emotions

– Tweetsentiments.com– Retail Twitter Aggregation– TweetTabs– Where, what, when

Finding ‘Emergent’ Topics

• What is it?– A topic popular in current time and not in the past

• 5 steps:– Represent tweets as vectors of terms (language

independent)– Graph active authors’ social relationships (PageRank)– Model each term’s life cycle: “novel aging theory”– Rank terms based on their “energy”, select top few– Create a navigable topic graph linking emerging terms

with co-occurring ones: Emerging Topics.

Cool points• Nice overview with enough details about text representation, processing etc.• Hypothesis: flow of ideas from geographical origin of event to outside

– So find the starting tweets and you find the locale– Always so? Global event? Disasters?

• See PageRank in action– Author network in particular

• SCC– Term networks

• Biological metaphor– Lots of different terms: nutrition, calories, energy…

• Reasonable case study• Language – humour

– heartquake– Not dumping factor

Some trends

Last Class ContinuationCrawler Evaluation

• What are good pages? • Web scale is daunting• User based crawls are short, but web agents?• Page importance assessed– Presence of query keywords– Similarity of page to query/description– Similarity to seed pages (held out sample)– Use a classifier – not the same as used in crawler– Link-based popularity (but within topic?)

Summarizing Performance

• Precision– Relevance is Boolean: yes/no• Harvest rate: # of good pages/total # pages

– Relevance is continuous• Average relevance over crawled set

– Recall• Target recall: held out seed pages (H)

– |H ∧ pages crawled|/|pages crawled|

• Robustness– Start same crawler on disjoint seed sets. Examine overlap of

fetched pages

Sample Performance Graph

Summary

• Crawler architecture• Crawler algorithms• Crawler evaluation• Assignment 1– Run two crawlers for 5000 pages.– Start with the same set of seed pages for a topic.– Look at overlap and report this over time

(robustness)