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Mining Twitter for real-time trend and informationdiscovery
Yahoo! Research Barcelona
Arkaitz Zubiaga
NLP & IR Group @ UNED
December 19th, 2011
Motivation
Index
1 Motivation
2 Our Work (I): Classification of Trending Topics
3 Our Work (II): Real-Time Summarization of Events
4 Outlook
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Motivation
Twitter is a microblogging service with over 200 million users.
Users share short messages of up to 140 characters (tweets).
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Motivation
Twitter: following users
Different from Facebook, following is not reciprocal.
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Motivation
Twitter: retweeting
Retweet: users can help spread tweets by others.
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Motivation
Retweeting enables fast spread of messages.
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Motivation
Increase of activity on Twitter
As of October 2011, Twitter received 250 million tweets per day.
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Motivation
Variety of Twitter accounts
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Motivation
Usefulness of Twitter
Twitter provides...1 ...large amounts of data in real-time,
2 from a wide variety of sources,
3 with the ability to spread rapidly.
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Motivation
Twitter’s popularity
Twitter has gained widespread popularity as a tool for...
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Motivation
Using Twitter for... following events
(1) Live-tweeting about and following events.
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Motivation
Using Twitter for... helping others
(2) Helping others, as in natural disasters.
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Motivation
Using Twitter for... finding out about news
and (3) Finding out about breaking news.
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Motivation
Twitter on the media
Lots of researchers are analyzing tweets.
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Motivation
Trends on Twitter
The news about the Japan earthquake broke on Twitter.
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Motivation
Video: Japan earthquake on Twitter
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Motivation
Research on Twitter
Most of the research on Twitter focus on the analysis of streams afterthey happened.
Very little research deals with the real-time analysis of streams.
Our goal: How can we mine Twitter streams to acquire real-timeknowledge about events and trends?
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Our Work (I): Classification of Trending Topics
Index
1 Motivation
2 Our Work (I): Classification of Trending Topics
3 Our Work (II): Real-Time Summarization of Events
4 Outlook
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Our Work (I): Classification of Trending Topics
Trending Topics on Twitter
Trending topics reflect the top conversations being discussed onTwitter more than usually.
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Our Work (I): Classification of Trending Topics
What produces trending topics?
What kinds of events leverage those trending topics?
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Our Work (I): Classification of Trending Topics
Typology of Trending Topics
News: Japan earthquake.
Current events: a soccer game.
Memes: funny and viral ideas.
Commemoratives: World AIDS Day.
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Our Work (I): Classification of Trending Topics
Goal
Find out the type of a trending topic as soon as it emerges.
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Our Work (I): Classification of Trending Topics
Dataset
1,036 unique trending topics, with up to 1,500 associatedtweets as soon as they trended.
Manual classification of trending topics:
616 current events.251 memes.142 news.27 commemoratives.
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Our Work (I): Classification of Trending Topics
Experiment Settings
Support Vector Machines (one-against-all)
500 trends for the training set.
10 runs.
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Our Work (I): Classification of Trending Topics
Representation of Trending Topics
2 different representation approaches:
Twitter features: 15 straightforward language-independentfeatures that rely on the social spread of trends.
Bag-of-words: Text of tweets (TF).
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Our Work (I): Classification of Trending Topics
Results
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Our Work (I): Classification of Trending Topics
Results
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Our Work (I): Classification of Trending Topics
Main findings
Trending topics can accurately (78.4%) be categorized using socialfeatures:
Outperforming use of textual content.
Without making use of external data.
In real-time as the trending topic emerges.
Arkaitz Zubiaga, Damiano Spina, Vıctor Fresno, and Raquel Martınez.2011. Classifying trending topics: a typology of conversation triggers onTwitter. CIKM 2011.
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Our Work (II): Real-Time Summarization of Events
Index
1 Motivation
2 Our Work (I): Classification of Trending Topics
3 Our Work (II): Real-Time Summarization of Events
4 Outlook
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Our Work (II): Real-Time Summarization of Events
Events on Twitter
When users live-tweet about events:
They produce vast amounts of tweets about events.
Users want to follow what others say.
Users cannot follow the overwhelming amounts of tweets.
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Our Work (II): Real-Time Summarization of Events
Stream summarization
Can we summarize streams of tweets in such a way that:
Users receive a reduced stream that they can follow?
Users do not miss any key sub-event occurred during the event?
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Our Work (II): Real-Time Summarization of Events
Study of soccer games
Copa America 2011 (July 1-26, 2011):
26 soccer games.
11k-70k tweets per game.
Tweets are written in 30 languages.
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Our Work (II): Real-Time Summarization of Events
Gold Standard
Live reports gathered from Yahoo! Sports.
Yahoo! journalists provide annotations for:
Goals.Penalties.Red Cards.Disallowed Goals.Game Starts, Ends, Stops & Resumptions.
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Our Work (II): Real-Time Summarization of Events
Histogram of a Soccer Game
time elapsed
twee
t rat
e
500
1000
1500
2000
2500
1310854000 1310856000 1310858000 1310860000 1310862000 1310864000
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Our Work (II): Real-Time Summarization of Events
Summarization of soccer games
2-step summarization:1 Sub-event detection.
2 Tweet selection.
summary
Sub-event Detection
Tweet Selection
real-time
tweets stream
tweet
tweet
tweet
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Our Work (II): Real-Time Summarization of Events
1st Step: Sub-event Detection
Increase [Zhao et al., 2011]: a sub-event occurred when a suddenincrease is given in the tweeting rate (1.7 as much as the previousrate).
Outliers: learns from audience. High tweeting rates ascompared to rates seen so far will be considered sub-events (90%percentile).
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Our Work (II): Real-Time Summarization of Events
1st Step: Results
P R F1 #
Increase 0.29 0.81 0.41 45.4
Outliers 0.51 0.84 0.63 25.6
Increase-based approach provides more sub-events, with many FPs(recall-based).
Outlier-based approach (rather based on outstanding tweeting rates)improves in P and R.
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Our Work (II): Real-Time Summarization of Events
2nd Step: Tweet Selection
Each term appearing in tweets in a given timeframe is given a weightaccording to:
Frequency (TF).
Language Models (KLD).
These weightings enable to choose a representative tweet, as the tweetwith higher value adding up weights of its terms.
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Our Work (II): Real-Time Summarization of Events
2nd Step: Results
es en pt
Goals (54)TF 0.98 0.98 0.98KLD 1.00 1.00 1.00
Penalties (2)TF 1.00 0.50 1.00KLD 1.00 0.50 1.00
Red cards (12)TF 0.75 0.75 0.83KLD 0.92 0.92 1.00
Disallowed goals (10)TF 0.40 0.50 0.40KLD 0.40 0.50 0.30
Game starts (26)TF 0.73 0.74 0.79KLD 0.84 0.79 0.83
Game ends (26)TF 1.00 1.00 1.00KLD 1.00 1.00 1.00
Game stops TF 0.62 0.60 0.57& resumptions (63) KLD 0.68 0.60 0.59
OverallTF 0.79 0.74 0.78KLD 0.84 0.77 0.82
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Our Work (II): Real-Time Summarization of Events
Main findings
Use of state-of-the-art text analysis methods generates accuratesummaries:
With precision and recall values above 80% (100% for keysub-events).
In real-time as the game is being played.
In 3 different languages (es, en, pt).
Without need of external data.
Damiano Spina, Arkaitz Zubiaga, Enrique Amigo, Julio Gonzalo. TowardsReal-Time Summarization of Events from Twitter Streams. To Appear.
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Outlook
Index
1 Motivation
2 Our Work (I): Classification of Trending Topics
3 Our Work (II): Real-Time Summarization of Events
4 Outlook
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Outlook
Outlook
Work 1:Further dig into each type of trending topic, in order to look forsubtypes of trends.
Work 2:Evaluate the performance of the summarizer on other kinds ofscheduled events (award ceremonies, keynote talks,...)Evaluate novelty of information garnered from tweets.
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Outlook
Any Questions?
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