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BIG DATA ANALYTICS WITH TWITTER
Aug 23, 2012 Marti Hearst & Gilad Mishne

WHAT IS TWITTER?

TWITTER AS CULTURAL PHENOMENON

Some 15% of online adults use Twitter as of February 2012, and 8% do so on a typical day. Although overall Twitter usage has nearly doubled since the Pew Research Center’s Internet & American Life Project first asked a stand-alone Twitter question in November 2010, the 15% of online adults who use Twitter as of early 2012 is similar to the 13% of such adults who did so in May 2011. At the same time, the proportion of online adults who use Twitter on a typical day has doubled since May 2011 and has quadrupled since late 2010—at that point just 2% of online adults used Twitter on a typical day.1 The rise of smartphones might account for some of the uptick in usage because smartphone users are particularly likely to be using Twitter.

TWITTER STATS

Twitter usage over time
% of internet users who use Twitter 40%

20% 13% 8% 5% August 2011 Total 8% February 2012 15% 12%

0%

2% November 2010

4% May 2011 Typical day

Source: Pew Research Center's Internet & American Life Project Winter 2012 Tracking Survey, January 20February 19, 2012. N=2,253 adults age 18 and older, including 901 cell phone interviews. Interviews conducted in English and Spanish. Margin of error is +/-2.7 percentage points for internet users (n=1,729).

Several demographic groups stand out as having high rates of Twitter usage relative to their peers:

TWITTER STATS
 Urban and suburban residents — Residents of urban and suburban areas are significantly more likely to use Twitter than their rural counterparts.

Who uses Twitter?
% of internet users within each group who use Twitter

All adult internet users (n=1729) Men (n=804) Women (n=925) Age 18-29 (n=316) 30-49 (n=532) 50-64 (n=521) 65+ (n=320) Race/ethnicity White, Non-Hispanic (n=1229) Black, Non-Hispanic (n=172) Hispanic (n=184) Annual household income Less than $30,000/yr (n=390) $30,000-$49,999 (n=290) $50,000-$74,999 (n=250) $75,000+ (n=523) Education level No high school diploma2 (n=108) High school grad (n=465) Some College (n=447) College + (n=698) Geographic location Urban (n=520) Suburban (n=842) Rural (n=280)

15% 14 15 26** 14 9 4 12 28** 14 19 12 14 17 22 12 14 17 19** 14** 8

Source: Pew Research Center's Internet & American Life Project Winter 2012 Tracking Survey, January 20-February 19, 2012. N=2,253 adults age 18 and older, including 901 cell phone interviews. Interviews conducted in English and Spanish. The margin of error is +/-2.7 percentage points for internet users. **Represents significant difference compared with all other rows in group.

2

Twitter use among 18-24 year olds increased dramatically between May 2011 and February 2012, both overall and on a “typical day” basis
Twitter use within the overall population remained steady over the last year, and usage rates within most major demographic groups changed little over the same time period. The youngest adults (those between the ages of 18 and 24) are the primary exception to this trend—nearly one third of internet users in this age group now use Twitter, up from 18% in May of 2011 and 16% in late 2010.3 Twitter use by those in their mid-20s to mid-40s largely leveled off in the last year after roughly doubling between late 2010 and mid 2011.

TWITTER STATS
November 2010 May 2011 13% 18 19 14 9 8 6 February 2012 15% 31 17 16 9 9 4

Twitter adoption by age, 2010-2012
% of internet users in each group who use Twitter

All adults 18-24 25-34 35-44 45-54 55-64 65+

8% 16 9 8 7 4 4

Sources: Pew Research Center’s Internet & American Life Project tracking surveys. 2012 data based on January 20-February 19, 2012 Tracking Survey. N=2,253 adults age 18 and older, including 901 cell phone interviews, margin of error is +/-2.7 percentage points based on internet users (n=1729).

In addition to increasing on an overall basis, the proportion of young internet users who use Twitter on a typical day also doubled over the last year. Fully one in five internet users ages 18-24 (20%) now use Twitter on a typical day, up from 9% in May 2011.

TWITTER HISTORY
Evan Williams on the genesis of Twitter, ICWSM, April 2007:
• A side project started on a whim; Jack
Dorsey’s idea; launched Oct, 2006.

• Wanted a ubiquitous status message. • A community of people answering the question “what are you doing?”

• Exploded at SXSW; good for collective backchanneling; SF earthquakes.

• “Ambient intimacy” • Huge API usage was unexpected, as was the rise of the @ sign for replies http://www.icwsm.org/blog/2007/04/videos-for-keynotes.html TWITTER: A PLATFORM FOR RESEARCH

AN INSPIRATION FOR RESEARCH

TWITTER:

AN INSPIRATION FOR RESEARCH

TWITTER:

AN INSPIRATION FOR RESEARCH
• What

TWITTER:

is Twitter? (Kwan et al, “What is Twitter, a social network or a news media?”, WWW, 2009) uses Twitter? (Mislov et al., “Understanding the Demographics of Twitter Users,” IWCSM, 2011) do they Tweet About? (Java et al., “Why we twitter: understanding microblogging usage and communities,” WebKDD and 1st SNA-KDD Workshop, 2007; Ramage et al, “Characterizing microblogs with topic models,” ICWSM 2012)

• Who

• What

AN INSPIRATION FOR RESEARCH
• Who

TWITTER:

is influential? (Weng et al, “Twitterrank: finding topic-sensitive influential twitterers” WSDM, 2010) in large-scale emergencies (Krishnamurthy et al., “A few chirps about Twitter,” ACM workshop on Online Social Networks, 2008) social relationships (Huberman et al., “Social Networks that Matter, Twitter under the Microscope,” First Monday 14(1-5), 2009)

• Usage

• Reciprocal

AN INSPIRATION FOR RESEARCH
• Microblogging

TWITTER:

at work (Zhao & Rosson, “How and why people Twitter; the role of that micro-blogging plays in informal communication at work,” ACM Group, 2009) of the @ sign (Honey & Herring, “Beyond Microblogging: conversation and collaboration via Twitter,” HICSS 2009) disease outbreaks (Chew et al., “Pandemics in the age of Twitter: content analysis of Tweets during the 2009 H1N1 outbreak,” PloS One, 5(11), 2010)

• Use

• Monitoring

TWITTER BIG DATA CASE STUDY:

MOOD SWINGS
Golder & Macy, “Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures”, Science, 30 September 2011: Vol. 333 no. 6051 pp. 1878-1881

• Goal: obtain

data about people’s moods throughout the day and across the globe to see if and how they correspond to time of day. is in contrast to self-report: prompted by an experimenter reported after the fact a small sample of undergraduates.

• This

• not • not • not

TWITTER BIG DATA CASE STUDY:

MOOD SWINGS


Background:


Individual mood is an affective state, influenced by:


Neurochemicals and hormones, and • Social activity including daily routines • Positive and negative affect are independent dimensions • positive (PA): enthusiasm, delight, alertness • negative (NA): distress, fear, anger, guilt • Research suggests that low PA indicates the absence of positive feelings, not the presence of negative ones.


Golder & Macy, “Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures”, Science, 30 September 2011: Vol. 333 no. 6051 pp. 1878-1881

TWITTER BIG DATA CASE STUDY:

MOOD SWINGS
• 509 • 2.4

M posts

M users’ posts and < 400 posts/user 2008 - Jan 2010 English only

• >25 • Feb

• International, but

Golder & Macy, “Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures”, Science, 30 September 2011: Vol. 333 no. 6051 pp. 1878-1881

TWITTER BIG DATA CASE STUDY:

MOOD SWINGS

• Text

of tweets was analyzed for positive and negative affect LIWC, a common content analysis tool

• Used

Golder & Macy, “Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures”, Science, 30 September 2011: Vol. 333 no. 6051 pp. 1878-1881

Golder & Macy, “Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures”, Science, 30 September 2011: Vol. 333 no. 6051 pp. 1878-1881

Golder & Macy, “Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures”, Science, 30 September 2011: Vol. 333 no. 6051 pp. 1878-1881

Figure S3. Hourly changes in within-individual affect (y-axis) over a 24-hour cycle (x-axis), broken down by chronotype, for PA (top) and NA (bottom). Each series shows the mean affect (black lines) and 95% confidence interval (colored regions).

Golder & Macy, “Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures”, Science, 30 September 2011: Vol. 333 no. 6051 pp. 1878-1881

DISCUSSION
• What • What

are some of the assumptions behind this work? are some of the computational challenges?

WHAT IS BIG DATA?

WHY IS IT CHALLENGING?

TWITTER BIG DATA CASE STUDY:

HOW DO PEOPLE USE TWITTER?
Java et al., “Why We Twitter: Understanding Microblogging Usage and Communities,” Joint 9th WebKDD and 1st SNA-KDD Workshop ’07, Aug 12, 2007, San Jose, CA.

• Goal: gain

an understanding of Twitter usage, mainly through quantitative techniques. the analysis in several interesting, varying ways.

• Approached • Questions • What • This

to consider:

are the data analysis problems?

was a very early paper; how well do is results stand up?

TWITTER BIG DATA CASE STUDY:

HOW DO PEOPLE USE TWITTER?
Java et al., “Why We Twitter: Understanding Microblogging Usage and Communities,” Joint 9th WebKDD and 1st SNA-KDD Workshop ’07, Aug 12, 2007, San Jose, CA.

• Data: • April • 1.3M • 76K

1, 2007 - May 30, 2007 posts

users

TWITTER BIG DATA CASE STUDY:

HOW DO PEOPLE USE TWITTER?
Java et al., “Why We Twitter: Understanding Microblogging Usage and Communities,” Joint 9th WebKDD and 1st SNA-KDD Workshop ’07, Aug 12, 2007, San Jose, CA.

• Network

Properties:
Twitter Social Network Scatter Plot

10000

Correlation Coefficient = 0.59

1000

Outdegree (follows)

100

from-to Asia Europe Oceana N.A S.A Africa Asia 13.45 0.64 0.10 5.97 0.005 0.01 Europe 0.53 9.48 0.25 6.16 0.17 0.02 Oceana 0.13 0.40 0.60 1.92 0.02 0.01 N.A 5.19 5.46 1.23 45.60 0.60 0.10 S.A 0.06 0.26 0.02 0.75 0.62 0.00 Africa 0.01 0.03 0.00 0.11 0.00 0.03

total nodes total links

88K

830K 19

10

Table 3: Table shows the distribution of Twitter social network links across continents. Most of the social network lies within North America. (N.A = North America, S.A = South America)

avg degree

1

1

10

100 Indegree (followed by)

1000

10000

degree corr. reciprocity

0.6 0.6

Figure 6: Scatter plot showing the degree correlation of Twitter social network. A high degree correlation signifies that users who are followed by many people also have large number of friends.
90° N

Distribution of Twitter Users Across the World

Property Total Nodes Total Edges Average Degree Degree Correlation Clustering Coefficient Percent Reciprocity

N.A 16,998 205,197 24.15 0.62 0.147 62.64

Europe 5201 42,664 16.42 0.78 0.54 71.62

Asia 4886 60519 24.77 0.92 0.18 81.40

45° N

Table 4: Network properties of social networks within a continent. Europe and Asia have a higher

TWITTER BIG DATA CASE STUDY:
Outdegree (follows)

from-to Asia Europe Oceana Asia 13.45 0.64 0.10 Europe 0.53 9.48 0.25 1000 Oceana 0.13 0.40 0.60 N.A 5.19 5.46 1.23 S.A 0.06 0.26 0.02 100 “Why We Twitter: Understanding Microblogging Usage and Communities,” Joint 9th WebKDD and Java et al., Africa 0.01 0.03 0.00
10000 Correlation Coefficient = 0.59

Twitter Social Network Scatter Plot

HOW DO PEOPLE USE TWITTER?
Distribution
Twitter Social Network Scatter Plot Correlation Coefficient = 0.59

N.A S.A Africa 5.97 0.005 0.01 6.16 0.17 0.02 1.92 0.02 0.01 45.60 0.60 0.10 0.75 0.62 0.00 1st SNA-KDD Workshop ’07, Aug 12, 2007, San Jose, CA. 0.11 0.00 0.03

• Geographic
10

S.A Africa 0.005 0.01 1 1 10 100 1000 10000 Europe 0.53 9.48 0.25 6.16 0.17 0.02 Indegree (followed by) 1000 Oceana 0.13 0.40 0.60 1.92 0.02 0.01 Property N.A 5.46 Europe1.23 Asia N.A 5.19 45.60 0.60 0.10 Figure 6: Scatter plot showing the degree correlaTotal Nodes S.A 16,998 0.26 5201 0.02 4886 0.75 0.06 0.62 0.00 tion of Twitter social network. A high degree correTotal Edges Africa 205,197 0.03 42,664 0.00 60519 0.11 100 0.01 0.00 0.03 lation signifies that users who are followed by many Average Degree 24.15 16.42 24.77 people also have large number of friends. Degree Correlation 0.62 0.78 0.92 Table 3: Table shows the distribution of Twitter Clustering Coefficient 0.147 0.54 0.18 10 Distribution of Twitter Users Across the World social network links across continents. Most of the Percent Reciprocity 62.64 71.62 81.40 90 N social network lies within North America. (N.A = North America, S.A = South America) Table 4: Network properties of social networks 1 45 N 1 10 100 1000 10000 within a continent. Europe and Asia have a higher Indegree (followed by) reciprocity indicating closer ties in these social netProperty N.A Europe Asia works. (N.A = North America) Figure 6: Scatter plot showing the degree correla0 Total Nodes 16,998 5201 4886 180 W 135 W 90 W 0 45 E 90 E 135 E tion 45 W Twitter social network. A 180 E degree correof high Total Edges 205,197 42,664 60519 lation signifies that users who are followed by many Average Degree 24.15 16.42 24.77 45 S people also have large number of friends. Degree Correlation 0.62 0.78 0.92 high hub scores have relatively low authority scores, such as Clustering Coefficient 0.147 users 0.54 0.18 dan7, startupmeme, and aidg. They follow many other Distribution of Twitter Users Across the World 90 S Percent Reciprocity 71.62 81.40 90 N while have less friends instead. Based on this 62.64 caterough gorization, we can see that user intention can be roughly Table 4: Network properties of categorized into these 3 types: information sharing, infor-social networks 45 N Figure 7: Figure shows the global distribution of within a continent. Europe mation seeking, and friendship-wise relationship. and Asia have a higher Twitter users. Though initially launched in US reciprocity indicating closer ties in these social netTwitter is popular across the world. works. (N.A = we identify communiAfter the hub/authority detection, North America) 0 180 W 135 W 90 W 45 W 0 45 E 90 E 135 E within friendship-wise relationships by only considering 180 E ties the bidirectional links where two users regard each other as 4. USER INTENTION friends. A community in a network can be vaguely defined 45 S In this paper, we propose a two-level framework for user inas a group of nodes more densely connected to eachauthority scores, such as high hub scores have relatively low other tention detection. First, we used the HITS algorithm [17] to than to nodes outside the group. Often aidg. They follow many other users dan7, startupmeme, and communities are find the hubs and authorities 90 S the network. Hubs and auin topical or based on shared interests. To construct web com- this rough catewhile have less friends instead. Based on
10000
° ° ° ° ° °

Table 3: Table shows the distribution of Twitter social network links across continents. Most of the social network from-to Asia Europe Oceana N.A lies within North America. (N.A = North America, Asia = South America) 0.10 S.A 13.45 0.64 5.97

Outdegree (follows)

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

TWITTER BIG DATA CASE STUDY:
Java et al., “Why We Twitter: Understanding Microblogging Usage and Communities,” Joint 9th WebKDD and 1st SNA-KDD Workshop ’07, Aug 12, 2007, San Jose, CA.

HOW DO PEOPLE USE TWITTER?



User Intention


First find “communities” based on link structure, using 2 algorithms:


Hubs & Authorities (Kleinberg ’99) to find top authorities Clique Percolation Method (Palla et al ’05) to find groups

Key Terms just:273 work:185 good:168 got:152 live:136 xbox:125 today:121 playing:115 day:108 play:100 night:90 getting:88 think:85 ll:85 watching:79 know:77 com:225 like:172 going:157 time:142 new:133 tinyurl:122 game:115 twitter:109 lol:10 halo:100 home:89 need:86 gamerandy:85 360:84 want:78





Key terms computed with log likelihood ratios based on when they appear and where in network they appear (Rayson & Garside ’00)

Figure 8: An example of a “gaming” community who also share daily experiences. Day a c-a c Rest of the Week b d-b d Total a+b c+d-a-b c+d

Freq of word Freq of other words Total

5. DISCUSSION

Following section presents a brief taxonomy of user inten tions on Twitter. The apparent intention of a Twitter pos was determined manually by the first author. Each pos

TWITTER BIG DATA CASE STUDY:

HOW DO PEOPLE USE TWITTER? com:175 just:133 good:82 time:74 jasona:73 day:63 work:58 ll:54 today:52 nice:49 got:47 yeah:44 watching:41 night:40 twitter:134 like:86 tinyurl:75 new:74 going:68 don:61 think:56 scottw:54 hkarthik:50 getting:47 really:46 need:43 love:41 home:40 twitter:132 going:59 good:51 day:49 today:45 think:40 need:35 great:31 thanks:28 just:109 blog:56 new:50 people:46 google:42 night:38 got:33 looking:29 video:26

Java et al., “Why We Twitter: Understanding Microblogging Usage and Communities,” Joint 9th WebKDD and 1st SNA-KDD Workshop ’07, Aug 12, 2007, San Jose, CA

com:93 just:35 tinyurl:29 ll:22 jaiku:21 leo:21 like:19 google:18 feeds:18 yeah:16 people:15 com:93 just:32 going:24 blog:21 don:19 google:17 got:16

twitter:74 new:32 going:24 blog:21 don:21 flickr:21 video:18 today:18 getting:16 good:15

com:198 tinyurl:87 like:55 url:50 time:45 don:41 ll:38 ireland:33 work:29

twitter:76 new:28 ll:22 leo:21 gamerandy:19 live:16 know:15

tinyurl:34 video:26 jaiku:22 like:19 yeah:18 people:16 time:15

com:121 ustream:43 today:39 new:33 video:32 like:28

twitter:76 tv:42 hawaii:36 time:33 leo:30 watching:28

just:50 live:42 day:33 good:33 work:30 tinyurl:28

Figure 10: Example Communities in Twitter Social Network. Key terms indicate that these communities are talking mostly about technology. The user Scobliezer connects multiple communities in the network.

TWITTER BIG DATA CASE STUDY:

HOW DO PEOPLE USE TWITTER?
Java et al., “Why We Twitter: Understanding Microblogging Usage and Communities,” Joint 9th WebKDD and 1st SNA-KDD Workshop ’07, Aug 12, 2007, San Jose, CA.

• •

Final step: manual classification of messages (how many not stated) Main intentions:
• • •

Daily chatter Conversations Sharing information / URLs Information source (hub with many followers) Friends Information seeker



Main categories of users:
• • •

THIS COURSE
• Focusing • Not

on software methods for handling big data

focusing on machine learning, not data mining, not natural language processing, although we will touch on these learning

• Fast-paced, self-directed •3

or 4 programming assignments project

• Class

CLASS PROJECT
• Project • Teams

Mentor from Twitter

of 3 REQUIRED at both UCB and Twitter

• Presentations • Wide

range of topics: ...

• Infrastructure, apps, analysis

http://statuscalendar.cs.washington.edu/

TWITTER APPS

TO DO

• Readings • Get

for next week

set up with Hadoop and Pig

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...Waiting Many critics consider Samuel Beckett’s Waiting for Godot, rst performed in Paris in 1953, the most important twentieth-century play in the Western canon. Despite the undeniable historical and aesthetic signi cance of Waiting for Godot, however, the question poses itself: to what extent may an absurdist play—about two bums waiting on the side of a country road for a person who never arrives— still strike us as relevant today? is question cannot be answered univocally, but depends on the interpretive choices made in the actual process of producing Beckett’s play on stage. My goal as the director of this Kennedy eatre production is to create a thoroughly contemporary experience that evades the usual clichés many have come to associate with Beckett’s style, such as monotony and leadenness. From this vantage point, I will now identify two major challenges to any stage production of Waiting for Godot in 2010—challenges relating to the historical and metaphysical background of the play. e setting (country road, tree), costume items (bowler hats, halfhunter watch), and habits of the characters (the pipe-smoking Pozzo), as well as the poverty and frugality of the two protagonists (a diet of turnips, radishes and carrots for Vladimir and Estragon), clearly suggest earlier historical periods such as the Irish Potato Famine from around 1850, the wasteland of northern France in the wake of the trench warfare of WWI, or America’s Great Depression in the 1930s. e names of the characters...

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The Deconstruction of Waiting for Go Dot

...concepts that are opposite in meaning.” “Waiting for Godot”, a classic of modern theatre, is a tragicomedy in two acts which tells the story of two men, Vladimir and Estragon, who are waiting to meet a man named Godot. By using deconstructive literary criticism, the play can be analyzed threw the following binary oppositions: passive/active hopelessness/hope, forgetfulness/remembrance and staying/going. Vladimir and Estragon are in a constant state of waiting for Godot: “Nothing to be done. / I'm beginning to come round to that opinion."(Waiting for Godot). Although they are being passive they try to occupy themselves while waiting for Godot. Derrida states that in binary oppositions there is a unspoken hierarchy in which the first term functions as superior to the second term which is considered inferior: “ Derrida’s procedure is to invert the hierarchy in which the first term functions as privileged and superior and the second term as derivative and inferior. By showing that the primary term can be made out to be derivative from or a special case of the secondary term” By reversing the first term with the second a greater meaning can obtained. Although Vladimir and Estragon as in a passive state of waiting they attempt to keep active in order to pass the time. This shows that being active is valued over being passive: “ What about trying them. / I’ve tried everything/ No I mean the boots/ Would that be a good thing? / It’d pass the time. I assure you, it’d...

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Who or What Is Waited for in Waiting for Godot ?

...Waiting for Godot  is hailed as a classic example of the "Theatre of the Absurd," Such dramatic works present a world in which daily actions are without meaning, language fails to effectively communicate. The characters reflect a sense of artifice, even wondering aloud whether perhaps they are on a stage. Waiting for Godot begins with two men on a barren road by a leafless tree. These men, Vladimir and Estragon, are often characterized as "tramps". The world of this play is operating on its own set of rules, its own system. There nothing happens, nothing is certain, and there’s never anything to do. Vladimir and Estragon are waiting for Godot, a man or perhaps a deity. The tramps can’t be sure if they’ve met Godot, if they’re waiting in the right place, if this is the right day, or even whether Godot is going to show up at all. While they wait, Vladimir and Estragon fill their time with a series of mundane activities (like taking a boot on and off) and trivial conversations (turnips, carrots) scattered with more serious reflection (dead voices, suicide, the Bible). "We always find something," Estragon casually remarks in Act II, "to give us the impression we exist." The tramps are soon interrupted by the arrival of Lucky, a man/servant/pet with a rope tied around his neck, and Pozzo, his master, holding the other end of the long rope. The four men proceed to do together what Vladimir and Estragon did earlier by themselves: namely, nothing. Lucky and Pozzo then leave so that...

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Godat

...keep the viewers interested but the intense dialogue and message kept the audience watching. the best way to describe this play is boring. Little happens, not action, very few twist and character changes but when you finish this play you really think and changes how you think about your life. The average play is for pure entertainment while this is more of a life lesson within a script. Some similarity between this play and the average is the use of repetition. The main c characters repeatedly say dialogue such as “were waiting for goat” and seeing the boy twice to empathize the importance. 2. I think The overall theme is this play is existentialism. The human struggle to find meaning in a meaningless life. Most importantly to find fulfillity in life, lack of purpose: the uncertainty of life. The characters are both anxiety driven men who wait around for the mysterious Godot. They believe waiting for him in necessary for them to take action. They are waiting for something external to give them meaning. Their is also an religious interpretation in the sense people wait for religion to give them direction to the next course of action they should take. They rely on God to give their life purpose who could be an interpretation of Godot. 3. I believe in the original black and white movie the characters are portrayed more accurate rather then the newer colored movie. Information about their appearance is slim but “he walks in "short stiff strides, legs wide apart," and is heavier...

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Operations Management

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