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Home , Bob Odenkirk, Gina Rodriguez, Jane Seymour (actress), Queen Latifah, Regina King, Robin Wright

73rd Annual Golden Globes Analysis

Boying Gong, Jianglong Huang, Peter Sujan, Shamindra Shrotriya, Tomofumi Ogawa

February 8, 2016 Data Sources and Limitations

I Metadata - Golden Globe Nominees

I 87 people nominees and 35 movie nominees

I Manually collected/ annotated list of all nominees

I Twitter Screen Names I Gender Flag I Film/ TV Show Flag I Age of Nominee/ Release Date

I Timelines

I Typically searched for top 3200 tweets from API

I Based on most recent tweets since Dec 10 2015

I NLP processing performed e.g. removing stopwords etc Quick summary of tweet data collected Key themes of our data exploration

I Twitter Influence and Temporal Patterns - Jianglong I Social Popularity of Winners and Nominees - Peter I Sentiment Analysis - Boying I Pre-Post-During Golden Globe Analysis - Tomo When Do They Tweet? Heatmap For Tweet Density All Tweets Heatmap 7Sun 6Sat count 5Fri 1000 4Thu 3Wed 500 2Tue

Day of the week Day 1Mon 0 5 10 15 20 Hour of the day Official Accounts Tweets Heatmap 7Sun 6Sat count 1000 5Fri 750 4Thu 500

3Wed 250 2Tue

Day of the week Day 1Mon 0 5 10 15 20 Hour of the day When Do Celebrities Tweet? By Gender Heatmap For Tweet Density Female Tweets Heatmap 7Sun 6Sat count

5Fri 300

4Thu 200

3Wed 100 2Tue

Day of the week Day 1Mon 0 5 10 15 20 Hour of the day Male Tweets Heatmap 7Sun 6Sat count

5Fri 150

4Thu 100 3Wed 50 2Tue

Day of the week Day 1Mon 0 5 10 15 20 Hour of the day Tweet “POWER” VS Twitter “TENURE” Scatter Plot for Tweet Power VS Tweet Tenure

10.0

7.5

factor(factor) Old and Powerful Old and Weak 5.0 Young and Powerful Young and Weak Tweet Power Tweet

2.5

0.0 1000 2000 3000 Tweet Tenure Profile of “Weak and Old” VS “Young and Powerful” Profile Plot

winner

as.factor(group) tv Old and Weak Young and Powerful Categories of Interest

female

0.0 0.2 0.4 0.6 0.8 Proportion in Each Group Follower/Following Behavior Exhibits Distinct Groups

Nominee Following vs. Follower Counts

4000 Wolf Hall

Rachel Bloom Not shown: 3000 : 55029737 , 131197 The Fencer : NA , NA

Result 2000 L Active Amy Schumer Power W Number followed

AnomalisaVeepMr. Robot Mark Ruffalo 1000

Taraji P. Henson

Queen Latifah Popular

Leonardo DiCaprio 0 Aziz Ansari

0.0e+00 5.0e+06 1.0e+07 1.5e+07 Number of followers Movie/TV Accounts Appear Most Active

Breakdown of User Type by Style of Twitter Use Active Other

20

10

0 WINNER L Popular Power count W

20

10

0 F FILM/SHOW F FILM/SHOW M Type Following Similarity Distribution

Distribution of Following Similarity

Very dissimilar

2000 Frequency

1000

Similarity with self = 1

Similar pairs 0

0.00 0.25 0.50 0.75 1.00 Similarity Similarity Measures Match Real-life Connections

Following Similarity: Most Similar Pairs

Steve Carell, The Big Short

Queen Latifah,

Adam McKay, Amy Schumer

Rachel Bloom, Amy Schumer

Rachel Bloom, Adam McKay

Taraji P. Henson, Queen Latifah

Flesh and Bone, Sarah Hay

Felicity Huffman, American Crime Pair

Taraji P. Henson, Viola Davis

Inside Out, The Good Dinosaur

Spy, Spy

Fargo, American Horror Story: Hotel

Queen Latifah, Idris Elba

Taraji P. Henson, Idris Elba

Regina King, Viola Davis

0.0 0.1 0.2 0.3 0.4 Similarity Popularity Among All Users 6= Popularity Among Peers

4

log of mention count 2

0

6 8 10 12 14 16 log of retweet count Mention Counts Grouped by Celebrities

Caitriona Balfe Amy Schumer Jeffrey Tambor Melissa McCarthy Lady Gaga patrick wilson Viola Davis ryuichi sakamoto Aziz Ansari Julia Louis−Dreyfus Carter Burwell Mark Ruffalo Sarah Hay Liev Schreiber Leonardo DiCaprio Win Fonda Idris Elba L

Name Tomlin and Wagner W Taraji P. Henson Steve Carell Rachel Bloom Queen Latifah Mr. Gina Rodriguez Rob Lowe Patrick Stewart JudithLight Felicity Huffman Ennio Morricone Emma Donoghue Daniel Pemberton Christian Slater Adam McKay 0 2 4 logMentionCount Top Domains of External Links Percentage of External Links Used by Gender

Male PercentageM Female PercentageF youtube 0.91 6.01 instagram 0.55 whattheflicka 1.56 whosay 0.40 youtube 0.39 apple 0.28 facebook 0.28 facebook 0.15 twimg 0.22 usanetwork 0.14 latina 0.14 ifc 0.14 ew 0.13 hollywoodreporter 0.12 variety 0.13 twimg 0.11 theguardian 0.13 ew 0.10 yahoo 0.10 Males tweeted more Post-Globes than Pre-Globes

Male Winners vs Male Losers (mean) lag vs retweet count lag vs tweet count 15

3000

WINNER_FLAG WINNER_FLAG 10 2000 black black blue blue L L tweet count tweet retweet count retweet 1000 W 5 W

0 −20 −10 0 10 20 −20 −10 0 10 20 lag (days) lag (days) lag vs favorite count lag vs tweet length 8000 140

6000 120 WINNER_FLAG WINNER_FLAG black black

4000 blue 100 blue L L tweet length tweet favorite count favorite W W 2000 80

0 60 −20 −10 0 10 20 −20 −10 0 10 20 lag (days) lag (days) Females winners were less favorited Post-Globes!

Female Winners vs Female Losers (mean) lag vs retweet count lag vs tweet count 4000 30

3000 WINNER_FLAG WINNER_FLAG black 20 black 2000 blue blue L L tweet count tweet

retweet count retweet W W 10 1000

0 0 −20 −10 0 10 20 −20 −10 0 10 20 lag (days) lag (days) lag vs favorite count lag vs tweet length 4000 120

3000 WINNER_FLAG 100 WINNER_FLAG black black 2000 blue blue L L 80 tweet length tweet favorite count favorite W W 1000

60 0 −20 −10 0 10 20 −20 −10 0 10 20 lag (days) lag (days) Actors are using Twitter for activism post-Globes! Conclusion and Next Steps

I Nominee Analysis shows distinct behaviour patterns when summarised by gender, age, temporal components I Next Steps:

I Do the analysis for Golden Globes 2015, 2014, 2013

I Look at nominee influence via external data e.g. box office

I Download large amount of historical follower analysis

I Analysis of twitter users the nominees follow