Event
Summarization
using Tweets
Deepayan Chakrabarti and
KunalPunera
Yahoo!Research
Abstract
 For

some highly structured and recurring
events, such as sports, it is better to use more
sophisticated techni...
Introduction
 one-shot

events
 Have “structure” or are long-running
 (a)the most recent tweets could
be repeating the ...
Introduction
 Our

goal:to extract a few tweets that
best describe the chain of interesting
occurrences in that event

A...
Introduction
 challenges

:
 Events are typically “bursty”
 Separate sub-events may not be temporally
far apart
 Previ...
Characteristics of Sports Coverage
in Tweets
Characteristics of Sports
Coverage in Tweets
Characteristics of Sports
Coverage in Tweets
 Some
1.
2.

issues of this data:
sub-events are marked by increased
frequen...
Algorithms
 Baseline:

SUMMALLTEXT
 associate with each tweet a vector of the
TF-logIDF of its constituent words
 Cosin...
Algorithms
Algorithms
 Several
1.
2.

defects:
O ( |Z|2) computations
heavily biased towards the most popular
sub-event
Algorithms
 Baseline:
1.
2.

SUMMTIMEINT
Split up the duration into equal-sized
time intervals
Select the key tweets from...
Algorithms
Algorithms
 Defects:

Burstiness of tweet volume:
 Multiple sub-events in the same burst:
 “Cold Start” :

Algorithms
 Our

Approach: SUMMHMM
 BACKGROUND ON HMMS:
 N states labeled S1 ,…, SN ,
 A set of observation symbols v1...
Algorithms
 Each

state: one class of sub-events
 The symbols: the words used in tweets
 The variation in symbol probab...
Algorithms
 Our

Modifications
 OUTPUTS PER TIME – STEP: a multiset of
symbols
 DETECTING BURSTS IN TWEET VOLUME:
 COMB...
Algorithms
 three

sets of symbol probabilities:
 (1)θ( s ) , which is specific to each state but
is the same for all eve...
Algorithms
 Algorithm

Summary
 Input: multiple events of the same type
 Learns the model parameters that bestfit
the da...
Algorithms
 standard

Viterbi algorithm:
Algorithms
Experiments
 Experimental

Setup
 professional American Football
 Sep 12th, 2010 to Jan 24th, 2011
 over 440K tweets o...
Experiments
 MANUAL

GROUND TRUTH CONSTRUCTION .
 Each output tweet was matched with the
happenings in the game and labe...
Experiments
 Play-by-Play

Performance
 RECALL
 PRECISION

Summary Construction
 EVALUATION

AT OPERATING POINT .
conclusion
 We

proposed an approach based on
learning an underlying hidden state
representation of an event .
Towards Twitter
Context
Summarization
with User
Influence
Models
ABSTRACT
 Traditional

summarization techniques only
consider text information.
 We study how user influence
models, whic...
INTRODUCTION
A

Twitter context tree is defined as a tree
structure of tweets which are connected
with reply relationship,...
TWITTER CONTEXT TREE ANALYSIS
 The

temporal growth of the Twitter
context tree
TWITTER CONTEXT TREE ANALYSIS
 Whether

the tree structure can help
the summarization task
USER INFLUENCE MODELS
 Granger

Causality Influence Model
 A time series data x is to Granger cause
another time series d...
USER INFLUENCE MODELS


Lasso-Granger method



Lag ( X,T )to denote the lagged version of
data X ;
FullyConnectedFeatur...
USER INFLUENCE MODELS
 Pagerank

Influence Model
 For each user u , it has a directed edge to
each user v if u has a repl...
SUMMARIZATION METHOD
 Text-based
 TFIDF

Signals
SUMMARIZATION METHOD
 Popularity

Signals
 Number of replies, number of retweets,
and number of followers for a given
tw...
SUMMARIZATION METHOD
 Temporal
1.
2.

Signals
fit the age of tweets in a context tree
into an exponential distribution.
fo...
Supervised Learning Framework
 Gradient

algorithm

Boosted Decision Tree(GBDT)
EDITORIAL DATA SET
 10

Twitter context trees from March 7th
to March 20th,2011
 4 are initiated by Lady Gaga
 6 are in...
EDITORIAL DATA SET
EXPERIMENTS
 Evaluation

Metrics
Methods for Comparison













Centroid:
SimToRoot:
Linear:
Mead:
LexRank
SVD:
ContentOnly
ContentAttribute...
Experimental Results
 Overall

Comparison
CONCLUSION
 User

influence information is very helpful to
generate a high quality summary for each
Twitter context tree.
...
Event summarization using tweets
Event summarization using tweets
Event summarization using tweets
Event summarization using tweets
Event summarization using tweets
Event summarization using tweets
Event summarization using tweets
Event summarization using tweets
Event summarization using tweets
Event summarization using tweets
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Event summarization using tweets

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Deepayan Chakrabarti and KunalPunera
Yahoo!Research

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Event summarization using tweets

  1. 1. Event Summarization using Tweets Deepayan Chakrabarti and KunalPunera Yahoo!Research
  2. 2. Abstract  For some highly structured and recurring events, such as sports, it is better to use more sophisticated techniques to summarize the relevant tweets.  A solution based on learning the underlying hidden state representation of the event via Hidden Markov Models.
  3. 3. Introduction  one-shot events  Have “structure” or are long-running  (a)the most recent tweets could be repeating the same information about the event  (b)most users would be interested in a summary of the occurrences in the game so far.
  4. 4. Introduction  Our goal:to extract a few tweets that best describe the chain of interesting occurrences in that event A 1. 2. two-step process: Segment the event time-line pick key tweets to describe each segment
  5. 5. Introduction  challenges :  Events are typically “bursty”  Separate sub-events may not be temporally far apart  Previous instances of similar events are available.  Tweets are noisy  Strong empirical results.
  6. 6. Characteristics of Sports Coverage in Tweets
  7. 7. Characteristics of Sports Coverage in Tweets
  8. 8. Characteristics of Sports Coverage in Tweets  Some 1. 2. issues of this data: sub-events are marked by increased frequency of tweets. Boundaries of sub-events also result in a change in vocabulary of tweets.
  9. 9. Algorithms  Baseline: SUMMALLTEXT  associate with each tweet a vector of the TF-logIDF of its constituent words  Cosine distance  Select those tweets which are closest to all other tweets from theevent.
  10. 10. Algorithms
  11. 11. Algorithms  Several 1. 2. defects: O ( |Z|2) computations heavily biased towards the most popular sub-event
  12. 12. Algorithms  Baseline: 1. 2. SUMMTIMEINT Split up the duration into equal-sized time intervals Select the key tweets from each interval  Two 1. 2. extra parameters: a segmentation TS of the duration of the event into equal-time windows the minimum activity threshold l
  13. 13. Algorithms
  14. 14. Algorithms  Defects: Burstiness of tweet volume:  Multiple sub-events in the same burst:  “Cold Start” : 
  15. 15. Algorithms  Our Approach: SUMMHMM  BACKGROUND ON HMMS:  N states labeled S1 ,…, SN ,  A set of observation symbols v1 ,…, vM  bi(k)  a ij πi
  16. 16. Algorithms  Each state: one class of sub-events  The symbols: the words used in tweets  The variation in symbol probabilities across different states: the different “language models” used by the Twitter users  The transitions between states models the chain of sub-events over time
  17. 17. Algorithms  Our Modifications  OUTPUTS PER TIME – STEP: a multiset of symbols  DETECTING BURSTS IN TWEET VOLUME:  COMBINING INFORMATION FROM MULTIPLE EVENTS
  18. 18. Algorithms  three sets of symbol probabilities:  (1)θ( s ) , which is specific to each state but is the same for all events,  (2) θ( sg ) , which is specific to a particular state for a particular game  (3) θ( bg ) , which is a background distribution of symbols over all states and games.
  19. 19. Algorithms  Algorithm Summary  Input: multiple events of the same type  Learns the model parameters that bestfit the data. (EM algorithm)  the optimal segmentation (standard V iterbi algorithm)
  20. 20. Algorithms  standard Viterbi algorithm:
  21. 21. Algorithms
  22. 22. Experiments  Experimental Setup  professional American Football  Sep 12th, 2010 to Jan 24th, 2011  over 440K tweets over 150 games for an average of around 1760 tweets per game.
  23. 23. Experiments  MANUAL GROUND TRUTH CONSTRUCTION .  Each output tweet was matched with the happenings in the game and labeled as Comment-Play , Comment-Game , or Comment-General .
  24. 24. Experiments  Play-by-Play Performance  RECALL  PRECISION Summary Construction
  25. 25.  EVALUATION AT OPERATING POINT .
  26. 26. conclusion  We proposed an approach based on learning an underlying hidden state representation of an event .
  27. 27. Towards Twitter Context Summarization with User Influence Models
  28. 28. ABSTRACT  Traditional summarization techniques only consider text information.  We study how user influence models, which project user interaction information onto a Twitter context tree, can help Twitter context summarization within a supervised learning framework.
  29. 29. INTRODUCTION A Twitter context tree is defined as a tree structure of tweets which are connected with reply relationship, and the root of a context tree is its original tweet.  two types of user influence models, called pair-wise user influence model and global user influence model.  Granger Causality influence model  PageRank algorithm
  30. 30. TWITTER CONTEXT TREE ANALYSIS  The temporal growth of the Twitter context tree
  31. 31. TWITTER CONTEXT TREE ANALYSIS  Whether the tree structure can help the summarization task
  32. 32. USER INFLUENCE MODELS  Granger Causality Influence Model  A time series data x is to Granger cause another time series data y ,If and only if regressing for y in terms of both past values of y and x is statistically significantly more accurate than regressing for y in terms of past values of y only. Let
  33. 33. USER INFLUENCE MODELS  Lasso-Granger method  Lag ( X,T )to denote the lagged version of data X ; FullyConnectedFeatureGraph ( X ) denotes the fully connected graph defined over the features; Lasso ( y, Xlag )denotes the set of temporal variables receiving a non-zero co-efficient by the Lasso algorithm.  
  34. 34. USER INFLUENCE MODELS  Pagerank Influence Model  For each user u , it has a directed edge to each user v if u has a reply or a retweet to v ’s tweet and we can have a global user graph G .
  35. 35. SUMMARIZATION METHOD  Text-based  TFIDF Signals
  36. 36. SUMMARIZATION METHOD  Popularity Signals  Number of replies, number of retweets, and number of followers for a given tweet’s author.
  37. 37. SUMMARIZATION METHOD  Temporal 1. 2. Signals fit the age of tweets in a context tree into an exponential distribution. for each tweet, we compute its temporal signal as the likelihood of sampling its age from the fitted exponential distribution.
  38. 38. Supervised Learning Framework  Gradient algorithm Boosted Decision Tree(GBDT)
  39. 39. EDITORIAL DATA SET  10 Twitter context trees from March 7th to March 20th,2011  4 are initiated by Lady Gaga  6 are initiated by Justin Bieber 1. read the root tweet 2. Scans through all candidate tweets 3. Selects 5 to 10 tweets
  40. 40. EDITORIAL DATA SET
  41. 41. EXPERIMENTS  Evaluation Metrics
  42. 42. Methods for Comparison           Centroid: SimToRoot: Linear: Mead: LexRank SVD: ContentOnly ContentAttribute: AllNoGranger: All:
  43. 43. Experimental Results  Overall Comparison
  44. 44. CONCLUSION  User influence information is very helpful to generate a high quality summary for each Twitter context tree.  All signals are converted into features, and we cast Twitter context summarization into a supervised learning problem.
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