Mining Twitter for Real-Time Trend and Information Discovery

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

Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 2 / 43

Motivation

Twitter

Twitter is a microblogging service with over 200 million users.

Users share short messages of up to 140 characters (tweets).


Motivation

Twitter: following users

Different from Facebook, following is not reciprocal.


Motivation

Twitter: retweeting

Retweet: users can help spread tweets by others.


Motivation

Twitter

Retweeting enables fast spread of messages.


Motivation

Increase of activity on Twitter

As of October 2011, Twitter received 250 million tweets per day.


Motivation

Variety of Twitter accounts


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.


Motivation

Twitter’s popularity

Twitter has gained widespread popularity as a tool for...


Motivation

Using Twitter for... following events

(1) Live-tweeting about and following events.


Motivation

Using Twitter for... helping others

(2) Helping others, as in natural disasters.


Motivation

Using Twitter for... finding out about news

and (3) Finding out about breaking news.


Motivation

Twitter on the media

Lots of researchers are analyzing tweets.


Motivation

Trends on Twitter

The news about the Japan earthquake broke on Twitter.


Motivation

Video: Japan earthquake on Twitter


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?


Our Work (I): Classification of Trending Topics

Index

1 Motivation



4 Outlook



Trending Topics on Twitter

Trending topics reflect the top conversations being discussed onTwitter more than usually.



What produces trending topics?

What kinds of events leverage those trending topics?



Typology of Trending Topics

News: Japan earthquake.

Current events: a soccer game.

Memes: funny and viral ideas.

Commemoratives: World AIDS Day.



Goal

Find out the type of a trending topic as soon as it emerges.



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.



Experiment Settings

Support Vector Machines (one-against-all)

500 trends for the training set.

10 runs.



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).



Results



Results



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.


Our Work (II): Real-Time Summarization of Events

Index

1 Motivation



4 Outlook



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.



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?



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.



Gold Standard

Live reports gathered from Yahoo! Sports.

Yahoo! journalists provide annotations for:

Goals.Penalties.Red Cards.Disallowed Goals.Game Starts, Ends, Stops & Resumptions.



Histogram of a Soccer Game

time elapsed

twee

t rat

e

500

1000

1500

2000

2500

1310854000 1310856000 1310858000 1310860000 1310862000 1310864000



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



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).



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.



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.



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



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.


Outlook

Index

1 Motivation



4 Outlook


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.


Outlook

Any Questions?


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Mining Twitter for Real-Time Trend and Information Discovery

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