DEIM Forum 2014 B6-1

Discovering NBA Game Stories from Twitter
Zhichao ZHANG† , Hisashi KOGA† , and Youhei OGYU†† Graduate School of Information Systems, University of Electro–Communications 1–5–1, Chofugaoka,
Chofu-shi, Tokyo, 182–8585 Japan
E-mail: †{zhang,koga,ogyu}@sd.is.uec.ac.jp
Abstract This paper proposes a system which generates the game summary for basketball games by quoting therepresentative tweets during the game period for the ﬁrst time, whereas the previous similar researches treatedsoccer and American football. Basketball is a more challenging subject than football or or soccer for making asummary. This paper introduces a unique idea to put the discussions at the break times between quarters and at
the end of the game into the game summary, since spectators state the game status at that moment and comments
upon the activities of the players and the teams then. Without specifying keywords, our heuristics attempt to suchdiscussions as the peaks of long duration in the tweet volume graph. Experimentally, our summary covers up to
87% of the items written in the NBA Oﬃcial Game Summary.
Key words Twitter Mining, Sport Event, Summary Generation

1. Introduction

than that of soccer game. The score of a basketball gamecan be 110-100, while the score of a soccer game can be

Twitter has become the most popular micro-blog which

only 1-1. Therefore, one shoot/goal is less important forhas more than 0.5 billion users. A lot of tweets are pub-basketball than for soccer. This paper introduces a uniquelished every day in Twitter, containing a myriad of infor-

idea to generate the game summary eﬀectively for basketballmation about what the users are doing and watching, whichgames, while paying attention to the increase of the tweet

can be seen as describing opinions about various events. Re-

volume in the same way as the previous researches. Aftercently, mining information about such events from the twit-

discovering the important moments from the tweet volume

ter stream has been a signiﬁcant research topic. This re-graph which records the number of tweets per time unit, oursearch approach is categorized into two kinds: the ﬁrst kind

method yields the game story by choosing the representative

attempts to discover the event occurrence without knowing

tweets for each important moment. Notably, our method

event types [2], while the second one intends to obtain the de-works in an almost unsupervised way without requiring thetailed descriptions about a speciﬁc event like an earthquake,

event keywords speciﬁc to basketball games such as dunk, 3-

a typhoon etc. [1] from the twitter stream and to summarize

pointer and so on. Only the two team names associated withthem.

the game have to be given in order to extract the relevant

In the line of the second kind, some previous litterateurs

set of tweets from the twitter stream.

dealt with sport games. Chakrabarti et al. [3] and Sport-

The rest of the paper is organized as follows: In Sect. 2,

Sense [4] studied American football, while [5] examined thewe brief the related works which focus on the event detectionsoccer’s world cup. All these methods identify the importantfrom the twitter stream. Section 3 analyzes the character-moments in sport games by picking up the time instances

istics of the tweets regarding to the NBA games. Section

when the tweet volume per time unit rapidly increases. For

4 describes our summary generation system for the NBA

the sport games, a remarkable event causes a sudden increase

games. Section 5 reports the experimental results. Finally,of the tweet volume, because many Twitter users comment

Section 6 concludes this paper and discusses the future work.

on it. Basically, important events in sport games can be discovered by checking if the tweet volume suddenly increases.

2. Related Works

Our research purposes to generate the summary for bas-

This section brieﬂy refers to the previous researches whichketball games from the tweets regarding to the NBA (Na-attempted to detect events for sport games from the twittertional Basketball Association) games. Basketball has diﬀer-

stream.

ent properties from American football and soccer. For ex-

Zhao et al. [4] developed a system named Sportsense which

ample, the goal frequency of basketball game is much higher

displays the major events rates fans’ excitement level in themiddle of the NFL American football games. They ﬁrst build
event templates by learning event examples which shows the

peak

tweet
volumechange of the tweet volume when the events happen. Oncethe event templates are complete, the same type of events
can be detected on-line for the ongoing game by matching to
the event templates. However, this event detection method
startrequires supervision, since the keywords related to events, for

end

example ”touchdown” or ”TD” for short, must be speciﬁed
so as to collect the event examples. The researchers musthave the domain knowledge about the NFL games in orderto predetermine the event keywords. Chakrabarti et al. [3]use Twitter to generate summaries of long running, structure rich events under the circumstances that multiple event
instances share the same underlying structure. Speciﬁcally,
they learned the structure and the vocabulary of events for
American football with a modiﬁed Hidden Markov Model

Figure 1 the Start, Peak, and End Times of a Spikeequals its word frequency. Finally, the top N sentences that
do not share any non-stop word stemmed tokens are output
as the game summary. For American football game, [5] sets
N to 3, which means that only a few sentences are enoughto cover the contents of an important moment.

3. Tweets for Basketball Games(HMM). Here the tweets for many games need to be prepared to compose the learning data. Moreover, the learning
process for the HMM is time-consuming.
On the other hand, Nichols et al. [5] detected events andgenerated a journalistic summary from the tweets at a World
Cup soccer game.

They neither count on predetermined

event keywords nor learn from the tweets for multiple games.
Namely, their method works in an unsupervised way. We explain their method in details below, since we will extend it
for basketball games in this paper.
After obtaining the tweets regarding to a given game using basic keyword ﬁltering via the twitter API, they ﬁrst
draw a tweet volume graph whose x-axis presents the timein minute and the y-axis denotes the number of tweets per
minute. From this graph, they extract spikes each of which isdeﬁned by the triple <Start Time, Peak Time, End Time>as shown in Fig. 1. The tweet volume starts increasing at
the start time, reaches the peak at the peak time and stops
decreasing at the end time. Among the derived spikes, only
those whose slope between the start time and the peak timegoes beyond a threshold are memorized as the importantmoments M1 , M2 , · · · , Mm , where m denotes the number ofimportant moments. In [5], the threshold is empirically set
to 3 times as large as the median of all the slopes for American football. Similar approaches are utilized by some otherprevious researches [6] [7] [8].
After the important moments in the game are identiﬁed,
the game summary is constructed by selecting the N representative sentences from the set of tweets posted at each im<j=< m). In this process, ﬁrst, theportant moment Mj (1 =This section ﬁrst explains how to collect the tweet dataset
regarding to the NBA games and then describe their features.
3. 1 Collection of Tweets
We rely on the Twitter Streaming API (https://dev.twitter.com/docs/streaming-apis) to gather the tweets on the NBA
games. This service allows developers to pull tweets in realtime which contain speciﬁed keywords. In our case, we collect the tweets for a certain NBA game between two teams
by setting their team names as the keywords. For example,
we can collect the tweets for the game between the Miami
Heat and San Antonio Spurs, by specifying the set of hashtags: ”#Heat”，”#Spurs”, ”#MIA” and ”#SAS” as the
keywords. Here ”MIA” and ”SAS” are abbreviation of thetwo team names. We get the team names and their abbreviations from the section of ”teams” from the NBA oﬃcial
website (www.nba.com). In order to get the tweets at thegame period, we keep only the tweets with the timestamp
between the begin time and the end time of the game, which
we also learn from the oﬃcial website. Our dataset consistsof tweets for 30 games from the regular season, playoﬀs andthe ﬁnals of NBA 2012-2013 season. We show two examplesof the tweets below.
@TrappedlnThe225 - Down by 6 still in the game. #Heatnull Sat May 25 10:01:36 JST 2013
@dudeimspacely - #Pacers 28#Heat 22 End of lst Quarter. #NBAPlayoﬀs null Sat May 25 10:01:36 JST 2013
3. 2 Feature of the Tweets for NBA Games
Figure 2 illustrates the tweet volume graph which records
the number of tweets per minute for the game between Lak-longest sentence of each tweet included in Mj is abstracted.

ers and Spurs in Apr. 27th, 2013. After examining several

Let the set of such longest sentences for Mj be Lj . Then,

tweet volume graphs for diﬀerent NBA games manually, we

the score of a sentence in Lj is computed by summing up thehave noticed the next primary features of the tweet dataset

scores of its word tokens. Here, the score of a word token

for the NBA games.

information is missed.
To contain such game status information, this paper
uniquely pays attention to the discussions at the break times
between quarters and at the end of the game, which always
contains important information such as the game status andthe comments on the previous quarter. For instance, during
the halftime of the NBA games, spectators give their impression of the ﬁrst half and, therefore, we should not neglect
the tweets published then so as to obtain useful informaFigure 2 Tweet Volume Graph for a Basketball Gametion. Note that the tweets in the break times are ignoredor slighted in the previous researches for soccer and football.

（ 1 ） The graph ﬂuctuates more often in the NBA games

than in the games of NFL and soccer, which implies that it
contains more spikes. This is probably because the basketball games are accompanied by more continuous actions than
American football and soccer. We also noticed that spikes

We show an example of the tweet issued at a break time below. This tweet was posted at the break time after the 3rdquarter of a game between the Indiana Pacers and the Miami
Heat. It surely exhibits the game status at that moment andis signiﬁcant.

with sharp slopes tend to be related to real-time actions suchas splendid slum dunks and turnovers.

At the end of the 3rd, the Pacers are ahead of the（ 2 ） The contents of the tweets contained in one spikeHeat by 13. Hibbert, George lead with 22 points

are very diverse for the NBA games, since multiple events

each.

such as slum dunk, assist, three pointer, game winner etc.
can happen in just one minute.
Our system in the next section exploits the two above features to generate an attractive game story for an NBA game.

4. Our Event Detection System

Unfortunately, the discussions at the break times cause
only gentle excitement and result in the peaks with gentle slopes in the tweet volume graph. Then, how can weﬁnd such discussions from the graph without knowing speciﬁc keywords? In this paper, we propose a simple eﬃcientheuristic approach to regard the spikes having long duration

The purpose of this system is discovering useful information as much as possible, and displaying them to the NBA
fans as the games story. So the input of our system is thestream of tweets regarding to a certain NBA game, and theoutput is the summary, i.e. the story of this game. We develop this system by tailoring the method of Nichols et al. [5]for the basketball games. It operates the next three steps inorder so as to output the ﬁnal games story:（ 1 ） Our system ﬁrst determines the important moments

by choosing some of the spikes in the tweet volume graph.
（ 2 ） Next, the tweets that can describe the contents ofas the discussions between the break times. The rationaleof this idea is as follows. At the beginning of a break time,
people start talking about the previous quarter and a uphillslope is formed. Then, since they gradually leave from theirPCs or smart phones, a downhill slope is formed. Interestingly, this downhill slope becomes a very long tail, as it isnever interrupted until the next quarter starts.
We take both the slope and the duration of a spike into
consideration. Particularly, we evaluate the value of a spike
P according to Eq. (1). A spike is evaluated higher, as thisformula becomes larger for the spike.

the important moments are chosen from the set of tweets
published at the important moments.
（ 3 ） The ﬁnal game summary is derived by excluding thesimilar tweets from the tweets chosen at the previous step.
We explain the three steps from now on.
4. 1 Decision of Important Moments
Like [5], the important moments are derived by searchingpeculiar spikes from the tweet volume graph. Although [5]seek spikes whose slopes are steeper than a certain thresholdas the important moments for soccer, considering spikes withsharp slopes is insuﬃcient for basketball, because they are
related to the real-time actions such as beautiful slum dunks
as pointed out in Sect. 3.2, Thus, the general game status

Score(P ) = fs ×

Slope(P )Area(P )+ fa ×.
MaxSlope
MaxArea(1)

fs and fa are weighting parameters to control the contribution of slope and area. We currently set both fs and fa to
0.5. MaxSlope denotes the biggest slope and MaxArea is thebiggest area size of the peaks over the whole tweet volume
graph. Though the area size of a spike appears in Eq.(1)instead of the spike duration, be aware that the area size ofthe spike is roughly proportional to its duration. In the same
way as [5], we choose the peaks with the score higher than athreshold θ as the important moments. In the experiments
at Sect. 5, θ is set to 0.1.

4. 2 Selection of Tweets for Important Moments(4) Harden with the airball with 53 seconds left. Did I say

Next, for an important moment, we select the set of tweets

soft? I meant ﬂaccid. #Rockets

which can describe its contents. In this process, a tweet is(5) James Harden picks up his 5th foul, he will sub out

ranked according to the relevance of words that the tweet

This example shows that the diversity of the top 10 key-

contains. Here, the relevance of a word is determined by its

words is high. The top 10 keywords are divided into several

frequency in the group of tweets that belong to the impor-types: (1) players’ names, (2) event names and (3) generaltant moment. In counting the word frequency, we exclude

information words. Despite only the 10 words are considered

the English stop words by utilizing English stop-word dic-in selecting tweets for this important moment, the contents

tionaries opened to the public on the web. In addition, theof the chosen tweets have high diversity. We observe thetwo team names which compete in the concerned NBA game

similar tendency for other important moment examples. We

are also discarded. Because the team names are used as theguess the reason of this phenomenon as follows.

ﬁltering keywords for the Twitter Streaming API, they are（ 1 ） On condition that one event is associated with one

contained in almost all the tweets. Thus the team names

player, the top 10 keywords for one important moment in-cannot describe the contents of the important moments well.

cludes several player’s names for the most cases. Thus, mul-After calculating the frequency of all the words over thetiple events are discovered with the top 10 keywords.

tweets belonging to the important moment, we get the top（ 2 ） One word out of the top 10 keywords may corre-

K words which have the highest word frequency at the im-spond to multiple events. For example, a player’s name canportant moment. In our current implementation, K = 10.

be related to several diﬀerent events.

Next, the score of a tweet is computed. We regard a tweet（ 3 ） Even for the identical event, multiple tweets describ-

is more important as it contains more highly-ranked key-ing it can supply diﬀerent information, while they contain thewords. The score of a tweet t is denoted by V (t) in Eq.

common keywords.
4. 3 Removal of Similar Tweets(2).
V (t) =n

The similar tweets are excluded from the tweets chosen atscore(i)(2)

i=1

the previous step. Removal of the similar tweets is necessary here, since a lot of spectators issue very similar tweets

Here, n is the number of the top K words in t, and score(i)on the same event and displaying such similar tweets annoys

presents the value of the top i-th word wi . For K = 10,

the NBA fans.

score(i) is set to 20 − i. Hence, the word with higher fre-This step ﬁrst uses clustering to classifying the set of tweets

quency has a higher score. In particular, the top keyword

into several clusters of similar tweets and then outputs one

is assigned about twice as large score as the 10th keyword.

representative tweet per cluster. As a clustering algorithm,

Finally, the set of tweets t for which V (t) becomes greater

we use average linkage method, one of the well-known ag-

than some threshold τ are passed to subsequent processing

glomerative hierarchical clustering algorithms. The agglom-

discussed in Sect 4.3.

erative hierarchical clustering begins with one-point clusters

To conﬁrm if we can successfully get the relevant tweets us-

and recursively merges the most similar pair of clusters, until

ing the top 10 keywords, and if the diversity of these tweets

the number of clusters ﬁnally reduces to one. In the agglom-

is high, we preliminarily apply our method to several im-

eration step, the clustering algorithm searches the closest

portant moments. Here we report one case example for one

pair of clusters and merges them into a new single cluster.

important moment of the game between Houston Rockets

The hierarchical clustering algorithm is advantageous in thatand Oklahoma City Thunder on Apr. 30th, 2013. For thisthe number of clusters do not have to be speciﬁed a priori.

case, the top 10 keywords are as follows:This nice feature is suitable for our case, since a single impor-durant, kevin, left, dunk, seconds, pointer, lead, driving,harden, cuts.
The ﬁve tweets with the highest score for this importantmoment is shown below.

tant moment contains multiple events for basketball games
as stated in Sect. 3.2, so that it is impossible to grasp theproper number of tweet clusters beforehand.
In the average linkage method, the distance between two(1) @okcthunder: Kevin Durant with a 3-pointer and aclusters is deﬁned as the average distance between any mem-

driving dunk in 29 seconds. #Thunder cuts #Rockets leadber (tweet in our case) of one cluster to any member of theto 2. 105-103. 1:13 left in Gam

other cluster. Here, the distance D(ti , tj ) between a pair of(2) harden missed 3-pointer clutch shots in a row. watch

tweets ti and tj is deﬁned as the Jaccard distance in Eq. (3).

durant pull up and win it with a 3 at the buzzer. #Thunder(3) James harden blowing the games for #ROCKETS

D(ti , tj ) = 1 −|Si ∩ Sj |,|Si ∪ Sj |(3)

where Si and Sj symbolize the set of the words in ti and tj

the Indiana Pacers on June 2nd, 2013. First, the groundrespectively. The Jaccard distance is derived by subtracting

truth important moments discovered manually are shown on

the Jaccard coeﬃcient from 1. The Jaccard coeﬃcient be-

the tweet volume graph for this game in Fig. 3. There, theand measures thetween two sets A and B is deﬁned as A∩B
A∪B

spikes surrounded by the green rectangle correspond to theextent of the overlap between them.

ground truth important moments. To see what kind of infor-

If we stop merging clusters before the cluster number de-mation the ground truth important moments include, Table

creases to 1, multiple clusters are extracted. Our implemen-

1 lists a typical tweet example chosen by us for the 14 im-tation ceases merging clusters, when the distance between

portant moments. Among them, IM1, IM5, IM9 and IM14

the two clusters to be merged exceeds a threshold value

correspond to the discussions at the break times between

D = 0.925. Since we determine this value of D only empiri-

quarters and at the end of the game.

cally, the algorithm to derive an optimal value of D remains
to be developed in future.
After having multiple clusters in the above way, we determine one representative tweet for every cluster with morethan 3 members. Namely, small clusters are not adopted,
since they are not admitted by many spectators. Consider acluster C consisting of m tweets (m > 3). A tweet in C whichis the most similar to other tweets in the same cluster is appointed to the representative of C. Here, the similarity of atweet ti in C to other tweets is measured by

Figure 3 Ground Truth Detected Manuallym|Si ∩ Sj |j=1,j =i||Si ∪ Sj |which sums up the Jaccard coeﬃcients between ti and all theother tweets tj in C.

Table 1 Examples of Tweets for each Important Moment
IM1

Paul George’s dunk on bosh #nasty #heat vs pacers

IM2

At the end of the ﬁrst quarter, the #Pacers trail the

Finally, the representative tweets of all the clusters for all
the important moments constitute our game story.

Heat 23-21
IM3

are shooting lightsout at 85% (6-7) and #Pacers are

5. Experiments
With the dataset in Sect. 3, we experimentally evaluate
the performance of our system.
5. 1 Accuracy of Detected Important Moments

shooting 50% (1-2). #Heat lead 25-24
IM4

2 missed dunks #pacers ouch

IM5

D-Wade steal, ends up with a #LeBron JAMI #Heatlead at the half 40-39 0ver the #pacers

IM6

One of the novel ideas in our method is that it takes both

itgoing
IM7

nals#heat #game6

the slope only. This policy is abbreviated as SL hereafter.
IM8#Pacers are dominating the boards. They’ve got 43
rebs (13 0ﬀ 30 def) vs 28 rebs (10 0ﬀ 18 def) for #Heat.

The accuracy of an algorithm is measured by how many percentages of the real important moments, i.e., the spikes with

The pacers are playing so good defensively. Let’s go
Heatl!! Let’s go Heat!!! Letis go Heat!! #eastern ﬁ-

by comparing it with another policy which only considers
SL is derived by setting fs = 1.0 and fa = 0.0 in Eq. (1)#pacers taking an 11 point lead against the #heats on
the 3rd quarter #awesome #enjoyingthegame #keep-

the slope and the area of the spikes into account to detect theimportant moments. Therefore, we evaluate our algorithm

Ridiculous 3 point FGyo early in this game. #Heat

Lance Stephenson leads w/ 11
IM9

useful information are identiﬁed. We manually derive theAt the end of the 3rd, the #Pacers are ahead of theHeat by 13. Hibbert, George lead with 22 points eachreal important moments for all the 30 games from their tweet

IM10

Three pointer! lets go miller #heat

volume graphs as the ground truth.

IM11

Intense #heat and #pacers!! win or go home game
Let’s go #Heat#NBA #HEATNATIONAs the result, 14.86 genuine important moments are discovered manually per game on average. On average, ourIM12tantrum and likely closing the door on a comeback.

algorithm ﬁnds 13.23 important moments contained in the
ground truth, while SL ﬁnds 10.56. Thus, the recall of our#heat #pacers
IM13

algorithm reaches 89% whereas that of the SL is 71%. Thus,

To be more comprehensive, we give an explanation withrespect to one game instance between the Miami Heat and#Pacers go on a 9-0 to restore order and now lead 81-6
w/ 3:55 left. Roy Hibbert 24pts, 9rebs, Paul George

our algorithm can detect important moments more accurately than SL.

LeBron James just got a technical for throwing a

25pts 8 rebs. #heatvpacers
IM14

It’s going to game 7 #Pacers

Our algorithm detects 13 ground truth important mo-

Table 2 Sentences in the Oﬃcial Game Story and the Matched
Tweets in our Summary

ments. It misses one true important moment, that is, IM10

Oﬃcial Game Story

that describes an important three pointer by the Miami
Heat’s player Mike Miller. Note that all the discussions in

1

Miami Heat 91-77 in Game 77

the break times are found by our algorithm. On the other
hand, SL discovers 10 important moments of which 9 match

62

to the ground truth. It misses IM1, IM8, IM9 IM10 and

The Pacers limited Miami
to 36-percent shooting andIM12. Importantly, IM9 which describes the summary of
the third quarter and corresponds to the break time is not

Our Summary

The pacers defeated the #Pacers win # Heat by 91-

dominated things inside
3

The Pacers won the re- Rebound 53-33, score inrecognized by SL considering the slope only. In this way, SL

bounding battle 53-33 and the paint 44-22, this theleaks more meaningful information than our algorithm.

outscoring the Heat 44-22 reason why the #Pacers

5. 2 Quality of Our Summary
The design purpose of our system is to describe the NBA

in the paint4

with 7 rebounds and 6 as- and 6 assists in game 6

game story by exploiting the useful information contained in
the representative tweets. To examine if our system fulﬁlls

won this game

LeBron James scored 29 LBJ got 29 pts, 7 rebounds
sists

5

In quarter 3, the Pacers At the end of the 3rd, thethis goal, we evaluate the representative tweets outputted

outscored Miami 29-15, in- #Pacers are ahead of theby our system. In particular, we compare our summary thatcluding a 12-0 burst early Heat by 13.IND 68, MIA

consists of the chosen tweets with the NBA oﬃcial Gamein the quarter , took a 55
68-55 lead into the fourth

Story and examine how our summary agrees with it. The
NBA oﬃcial Game Story is a detailed game summary writ-quarter.
6

ten by the editors of the NBA oﬃcial web site which contains

shots, both 3-pointers, for changed #heat

the meaningful information about the games, such as gamestatus, representative events, good move, bad move, quota-

Miller made his only two Mike miller is a gamethe Heat.

7

Had a putback dunk and LeBron James just got ations from player interviews telling the state of the game andthen Miami unraveled com- technical for throwing aso on.

pletely. Called for an oﬀen- tantrum
sive foul

As an example for one game, Table 2 shows the 13 items
described in the oﬃcial Game story and the matched tweets

8

ana’s lead to 81-68 with up 81-68.

in our summary side by side. This game was played between
the Miami Heat and the Indiana Pacers on June 2nd. Empty
entries in the table mean that our summary misses the item
corresponding to the row. Since there exist two empty entries, our summary covers the 11 items. Therefore the cov= 84.6%. Whereas ourerage rate of our summary is 1113
summary cannot cover all the items described in the NBA
oﬃcial Game Story, it succeeds in acquiring useful information not written there. Table 3 displays the examples of thetweets with useful information that is not stated in the NBA

George Hil pushing Indi- 4 mins left and #pacers are
3:55 remaining.

9

Dwayne Wade and Chris D.Wade and C.Bosh toBosh combined for 15 points tally got 15 points
on 4-for-16 shooting.

10 West missed his ﬁrst seven Wake up, Mr. West!!!
shots and ﬁnished 5-for-14
for Indiana.
11 Joel Anthony came backinto the rotation
12 Paul George (28 points) George 28 pts, 8 reboundand Roy Hibbert (24)Oﬃcial Game Story, but contained in our summary. Theseand 5 assists, Roy Hibbert
20 pts, 11 rebounds.

tweets are either more detailed descriptions about the game13 Miami was without for- This game just shows how

than the NBA oﬃcial Game Story or comments on the teams

ward Chris ”Birdman” An- Miami would have lost theand the players.

dersenlast game if Birdman got
ejected.

Figure 4 summarizes the coverage rate for multiple games,
i.e., the 7 games of the NBA eastern semi-ﬁnal between the

Finally, we show that our approach to compile the rep-

Heat and the Pacers. Here, the x-axis denotes the game ID

resentative tweets for the clusters produced by the hierar-

and the y-axis shows the coverage rate against the NBA Of-

chical clustering algorithm is eﬀective to make the summaryﬁcial Games Story. The mean coverage rate reaches up to

diverse. The tweets with respect to the NBA games are clas-

87%. Since our summary mines a lot of useful information

siﬁed into the following 6 types:

not contained in the NBA Oﬃcial Game Story, we consider（ 1 ） General status: showing the score of the game. For

that this coverage rate is acceptable.

example, the scores of the two teams at half time and at theTable 3 Tweets with Useful Information that is not Stated in the
NBA Oﬃcial Game Story
1

That dunk by Paul George over bosh!!! #pacers

2

Led by @PaulGeorge24’s 9pts/4rebs, the #Pacers trail

3

4

types. Table 4 exhibits the tweet examples in our summaryfor each information type. On the other hand, when we chose

IND 21

randomly the same number of tweets from the whole set of#Pacers only down 2 thru the 1st. Despite #Heat go-

tweets associated with IM5, only 2 information types wereing 6-7 from downtown. #GoPacers #HEATvsPAC-

covered, because too personal tweets were put into the sum-

ERS #NBAPlayoﬀsmary. The above fact provides some support for the claim

At least 15 points left on the board due to missed
8-1 free throw and 10-3 foul advantage for the #pacers
in the ﬁrst half. Where are all the people claiming thenba wants the #Heat to win

6

at the halftime, our summary covered all the 6 information

the Heat by 2 at the end of the 1st quarter.?MIA 23,

dunks layups #Pacers
5

As for IM5 in Table 1 which corresponds to the discussion

Hibbert with an easy layup, #pacers lead the #Heat

that the agglomerative hierarchical clustering helps augment
the diversity of our summary.
Table 4 Types of the Representative Tweets for IM5
Type of information
General status

51-42 midway thru the 3rd quarter7

A tale of two 3rd quarters in game 5 Miami won the

Detailed information

9

Paul George Wt 3 t put #pacers 7 up

10 Bosh is so bad its 3 min left in the game and dude

1 8-1 free throw and 10-3 foul advantage for the pacers in the ﬁrst half

29-15 #ECF #heat #pacers #games6
Omg the heat coming back 2 fast. C’mon #pacers

Score of half time: 40-39. Pacers downby 1 point

third quarter 30-13 in game 6 Pacers won the quarter
8

Instance of detected information

2 Wade has only 1 point and Boshwith 3General events

is sitting on the bench. How is this guy getting paid

1 D-Wade steal, L-James dunk

15m??? #Heat #NBAPlayoﬀs

2 Sam Young hits the Pacers’ ﬁrst 3pointer of the night

11 This is a good #nbaplayoﬀs game #heatvspacers!
#Pacers came to play.
12 This game is really intense.#Heat #Pacers

Special events

broke the record for the number ofmissed dunks in a halfComments on teams

1 Bad sign for Pacers. Down by 1 at
the half. Should be up by at least 8.
2 Pacers end yet another quarter terribly
3 Both teams are lucky as hell.

Comments on players

1 Wake up Mr West
2 NOTHING from DavidWest

Figure 4 Coverage Rate of our Summary against the NBA Oﬃ-

6. Conclusion

cial Games Story

This paper proposes a system which generates the gameend of the game.
（ 2 ） Detailed information: showing the detailed informa-tion of the game, e.g., the number of rebounds, assists, andthe points that a speciﬁc player achieves.
（ 3 ） General events: These are common events which areobserved in many games, e.g., brilliant block, slam dunk,
three pointer and game winner.
（ 4 ） Special events: These are rare events that may nothappen in every game, e.g., breaking the record of three point
and long consecutive win in the NBA history.
（ 5 ） Comments on the performance of the teams.
（ 6 ） Comments on the performance of players.

summary for NBA basketball games by quoting the representative tweets issued during the games for the ﬁrst time,
while the previous researches dealt with soccer and American
football. It is more challenging to make a game summary forbasketball than for football or soccer, since one shoot/goalis less important for basketball. To enrich the summary, we
actively put not only the real-time actions such as beautiful
slum dunks which are described in the steep spikes in thetweet volume graph, but also the discussions at the breaktimes between quarters which usually contain both the gamestatus and the comments on the previous quarter. We propose a heuristic approach to focus on the spikes of long duration, even if they have rather gentle slopes in order to gathersuch discussions. Our algorithm successfully ﬁnds the groundtruth important moments in the game more accurately thanthe one which considers the slope of the spikes only. Furthermore, selecting the representative tweets per cluster which isderived by the hierarchical clustering algorithm applied tothe tweets associated with an important moment increases
the diversity of our summary. As the result, our summary
covers up to 87% of the items in the NBA Oﬃcial Game
Story. Although our summary can not cover all the items
stated in the NBA Oﬃcial Game Story, it also contains a lotof useful information not contained there.
There remains problems to be solved in future: First, weneed to make the evaluation method more reliable, since thecurrent evaluation method depends on the manual works byourselves much and is subjective to some extent. Next, ourcurrent system presumes an oﬄine environment. We will extend it, so that meaningful tweets are detected real-time fromthe tweet stream and displayed to the NBA fans. Increasing
the readability of our summary should be also pursued, since
our current system displays the representative tweets as theyare.

Acknowledgment
This work is supported by the Ministry of Education, Culture, Sports, Science and Technology, Grant-in-Aid for Scientiﬁc Research (C) 24500111, 2013.
References[1] T. Sakaki, M. Okazaki and Y. Matsuo, “Earthquake Shakes
Twitter users: Real-time Event Detection by Social Sensors”, in Proc, of WWW’10, pp. 851–860, 2010.
[2] S. Petrovic, M. Osborne and V. Lavrenko, “Streaming ﬁrst
story detection with application to Twitter”, in Human
Language Technologies: The 2010 Annual Conference of theNorth American Chapter of the Association for Computational Linguistics (NAACL-HLT), pp.181–189, 2010.
[3] D. Chakrabarti and K. Punera, “Event Summarization using Tweets,” in Proc. of ICWSM, 2011.
[4] S. Zhao, L. Zhong, J. Wickramasuriya and V. Vasudevan,“SportSense: Real-Time Detection of NFL Game Eventsfrom Twitter,” CoRR abs/1205.3212 (2012).
[5] J. Nichols, J. Mahmud, and C. Drews, “Summarizing Sporting Events Using Twitter,” in Proc. of IUI’12, pp. 189–198,
2012.
[6] D.A. Shamma, L. Kennedy and E.F. Churchill, “Tweet the
Debates: Understanding Community Annotation of Uncollected Sources,” in Proc. of the ﬁrst SIGMM workshop on
Social media, 2009.
[7] D.A. Shamma, L. Kennedy and E.F. Churchill, “Peaks andPersistence: Modeling the Shape of Microblog Conversations,” in Proc. of CSCW’11, pp.355-358, 2011.
[8] J. Weng and F. Lee, “Event Detection in Twitter,” in Proc.
of ICWSM, 2011.