This document describes research on using hidden Markov models to summarize Twitter events. It discusses segmenting event timelines into key sub-events and selecting tweets to describe each segment. The researchers trained HMMs on words from tweets about American football games to learn the sub-event structure. They evaluated the method by comparing selected tweets to the actual game play-by-play. The results demonstrated the HMM approach could accurately summarize sports events based on tweets.

1. Event
Summarization
using Tweets
Deepayan Chakrabarti and
KunalPunera
Yahoo!Research

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. 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. 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. 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. Characteristics of Sports Coverage
in Tweets

7. Characteristics of Sports
Coverage in Tweets

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. 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. Algorithms

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

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. Algorithms

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

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. 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. Algorithms
 Our
Modifications
 OUTPUTS PER TIME – STEP: a multiset of
symbols
 DETECTING BURSTS IN TWEET VOLUME:
 COMBINING INFORMATION FROM
MULTIPLE EVENTS

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. 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. Algorithms
 standard
Viterbi algorithm：

21. Algorithms

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. 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. Experiments
 Play-by-Play
Performance
 RECALL
 PRECISION
Summary Construction

25.  EVALUATION
AT OPERATING POINT .

29. conclusion
 We
proposed an approach based on
learning an underlying hidden state
representation of an event .

30. Towards Twitter
Context
Summarization
with User
Influence
Models

31. 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.

32. 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

33. TWITTER CONTEXT TREE ANALYSIS
 The
temporal growth of the Twitter
context tree

34. TWITTER CONTEXT TREE ANALYSIS
 Whether
the tree structure can help
the summarization task

35. 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

36. 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.



38. 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 .

39. SUMMARIZATION METHOD
 Text-based
 TFIDF
Signals

40. SUMMARIZATION METHOD
 Popularity
Signals
 Number of replies, number of retweets,
and number of followers for a given
tweet’s author.

41. 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.

42. Supervised Learning Framework
 Gradient
algorithm
Boosted Decision Tree(GBDT)

43. 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

44. EDITORIAL DATA SET

46. EXPERIMENTS
 Evaluation
Metrics

47. Methods for Comparison










Centroid:
SimToRoot:
Linear:
Mead:
LexRank
SVD:
ContentOnly
ContentAttribute:
AllNoGranger:
All:

48. Experimental Results
 Overall
Comparison

54. 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.