73rd Annual Golden Globes Twitter Analysis Boying Gong, Jianglong - PowerPoint PPT Presentation

73rd Annual Golden Globes Twitter Analysis Boying Gong, Jianglong Huang, Peter Sujan, Shamindra Shrotriya, Tomofumi Ogawa February 8, 2016

Data Sources and Limitations ◮ Metadata - Golden Globe Nominees ◮ 87 people nominees and 35 movie nominees ◮ Manually collected/ annotated list of all nominees ◮ Twitter Screen Names ◮ Gender Flag ◮ Film/ TV Show Flag ◮ Age of Nominee/ Release Date ◮ Timelines ◮ Typically searched for top 3200 tweets from API ◮ Based on most recent tweets since Dec 10 2015 ◮ NLP processing performed e.g. removing stopwords etc

Quick summary of tweet data collected

Key themes of our data exploration ◮ Twitter Influence and Temporal Patterns - Jianglong ◮ Social Popularity of Winners and Nominees - Peter ◮ Sentiment Analysis - Boying ◮ Pre-Post-During Golden Globe Analysis - Tomo

When Do They Tweet? Heatmap For Tweet Density All Tweets Heatmap 7Sun Day of the week 6Sat count 5Fri 1000 4Thu 3Wed 500 2Tue 1Mon 0 5 10 15 20 Hour of the day Official Accounts Tweets Heatmap 7Sun Day of the week 6Sat count 1000 5Fri 750 4Thu 500 3Wed 250 2Tue 1Mon 0 5 10 15 20 Hour of the day

When Do Celebrities Tweet? By Gender Heatmap For Tweet Density Female Tweets Heatmap 7Sun Day of the week 6Sat count 5Fri 300 4Thu 200 3Wed 100 2Tue 1Mon 0 5 10 15 20 Hour of the day Male Tweets Heatmap 7Sun Day of the week 6Sat count 5Fri 150 4Thu 100 3Wed 50 2Tue 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 Tweet Power factor(factor) Old and Powerful Old and Weak 5.0 Young and Powerful Young and Weak 2.5 0.0 1000 2000 3000 Tweet Tenure

Profile of “Weak and Old” VS “Young and Powerful” Profile Plot winner Categories of Interest as.factor(group) Old and Weak tv Young and Powerful 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 Lady Gaga: 55029737 , 131197 The Fencer Game of Thrones: NA , NA Number followed Result Active Power 2000 L Amy Schumer W Veep Anomalisa Mr. Robot Mark Ruffalo 1000 Taraji P. Henson Popular Queen Latifah Leonardo DiCaprio Steve Carell Aziz Ansari 0 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 count L Popular Power W 20 10 0 F FILM/SHOW M 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, Viola Davis Adam McKay, Amy Schumer Rachel Bloom, Amy Schumer Rachel Bloom, Adam McKay Taraji P. Henson, Queen Latifah Flesh and Bone, Sarah Hay Pair Felicity Huffman, American Crime 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 � = 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 Brie Larson Lady Gaga patrick wilson Kirsten Dunst Viola Davis ryuichi sakamoto Aziz Ansari Julia Louis−Dreyfus Jamie Lee Curtis Carter Burwell Uzo Aduba Regina King Mark Ruffalo Bryan Cranston Sarah Hay Liev Schreiber Leonardo DiCaprio Win Name Jane Seymour Fonda L Idris Elba Tomlin and Wagner W Tobias Menzies Taraji P. Henson Steve Carell Rachel Bloom Queen Latifah Mr. Bob Odenkirk Gina Rodriguez Sylvester Stallone Robin Wright Rob Lowe Rami Malek Patrick Stewart JudithLight Joanne Froggatt Felicity Huffman Ennio Morricone Emma Donoghue Daniel Pemberton Christian Slater Alan Cumming 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 instagram 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 retweet count 10 tweet count black black 2000 blue blue L L W W 1000 5 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 favorite count tweet length black black 4000 blue blue 100 L L 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 retweet count tweet count black black 20 2000 blue blue L L 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 WINNER_FLAG 100 favorite count tweet length black black 2000 blue blue L L 80 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 ◮ Nominee Analysis shows distinct behaviour patterns when summarised by gender, age, temporal components ◮ Next Steps: ◮ Do the analysis for Golden Globes 2015, 2014, 2013 ◮ Look at nominee influence via external data e.g. box office ◮ Download large amount of historical follower analysis ◮ Analysis of twitter users the nominees follow