73Rd Annual Golden Globes Twitter Analysis
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73rd Annual Golden Globes Twitter 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 Lady Gaga: 55029737 , 131197 The Fencer Game of Thrones: 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 Steve Carell 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 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 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 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 Jane Seymour Fonda Idris Elba L Name 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 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.