Presented 7/13/2016 at the International Conference on Social Media & Society

1. Identifying the Influencers
who Flooded Twitter during
the #ALSIceBucketChallenge
Kelli S. Burns, Ph.D.
University of South Florida
@KelliSBurns

2. The ALS Ice Bucket Challenge
• Campaign gained momentum the summer of 2014.
• Considered one of the most successful viral campaigns
of all time.
• Origin:
– Televised ice bucket challenges started on a Golf Channel
program.
– Then, a professional golfer challenged his cousin, whose
husband has ALS.
– The cousin was connected to Pat Quinn who was friends
with Pete Frates, a former college baseball player, both
with ALS.
• More than 17 million videos with 440 million views.
• More than 2.5 million people donated $115 million.

3. Strengths of Online Activism Campaigns
“They create narratives that can be boiled down
to 140 characters while still engaging people
emotionally. They create action messages that
can be encapsulated into a hashtag. And they
already have a strong network of people who
are, by and large, young, passionate, active on
social media, and structurally disconnected from
one another.” --danah boyd
http://thelede.blogs.nytimes.com/2012/03/09/how-the-kony-video-
went-viral/

4. IBC Google Search Trend

6. Kony 2012 Twitter Trend
Invisible
Children, Vans
Warped Tour
& Celebs
Oprah
tweets
Seacrest,
Kim K
tweet
Justin
Bieber
tweets

7. Influencer Literature
• Measuring User Influence in Twitter: The Million
Follower Fallacy (Cha, Haddadi, Benevenuto,
Gammadi, 2010)
• The Multiple Facets of Influence: Identifying
Political Influentials and Opinion Leaders on
Twitter (Dubois & Gaffney, 2014)
• Mapping networks of influence: Tracking Twitter
conversations through time and space (Willis,
2015)
• Are you connected? Evaluating information
cascades in online discussion about the
#RaceTogether campaign (Feng, 2016)

8. Methodology
• Historical data grant from Texifter provided access to
more than 500,000 tweets and enterprise access to
DiscoverText, a “cloud-based, collaborative text
analytics solution.”
• Tweets selected based on the criteria of inclusion of
the hashtags of #alsicebucketchallenge or
#icebucketchallenge and use of the English language
during the period of August 18-22, 2014 (peak time).
• Fifteen percent of the tweets during this timeframe
were randomly selected for inclusion in the sample,
resulting in a final sample of 545,548 tweets.

9. IBC Tweets from 8/18-22/14
Nash Grier
uploads a
video
Cameron
Dallas
uploads a
video
Niall Horan
uploads a
video

10. Summary of Data
• Total retweets, mentions, singles
– Retweets: 339,385
– Tweets with mentions: 152,015
– No mentions/retweets (singles): 53,498
• Unique users who tweeted: 414,535
• Interactions (some users made multiple
mentions in a tweet): 818,778

11. Methods for Identifying Influencers
• Degree
– Indegree (followers), indicates potential audience
– Outdegree (following)
• PageRank
– How likely is a user to reach a specific node from other
nodes in a network? (Weng et al. 2010)
• Social Authority Scores from providers like Klout/Kred
• Betweenness Centrality
– How many shortest paths cross through node?
• K-shell
– Assign each node a ks-index by pruning all nodes with
k<=ks (Brown & Feng, 2011)

12. Other Methods for Identifying Influencers
• Retweet influence: Twitter user’s retweets or
quoted tweets, indicates the ability of that
user to generate content of interest to others
• Mention influence: Twitter user’s mentions by
others, indicates the desire by others to
engage that user in conversation (Cha et al., 2010)

13. Research Questions
• How well do the IBC tweets reflect the mission
of the campaign?
• How do the three influence measures (retweet,
mention, indegree) correlate?
• What types of users have the most
followers/retweets/mentions? Who tweeted
the most?
• What is the network structure of the data?

14. ALS and Donation Mentions
n %
Tweets mentioning ALS 406,158 74.4%
Tweets mentioning
“donate”
64,190 11.8%
Tweets mentioning
“donate” AND ALS
8,106 1.49%

15. Word Cloud

16. Spearman’s rank correlation coefficients
Correlation Coefficient
Followers vs. retweets -.638
Followers vs. mentions .383
Retweets vs. mentions .539
Top 10% of unique retweeted (6+ retweets) (3,910) reduced by those without follower
count data, resulting in sample size of 1,154
Top 7% of mentions (3+ mentions) (3,331) reduced by those without follower count data,
resulting in sample size of 642
Top 10% of unique retweeted (6+ retweets) (3,910) reduced by those without mentions,
resulting in sample size of 2,087

17. Most Followed Users who Tweeted about
#IceBucketChallenge
• @niallofficial, 19.6 million,
singer
• @ParisHilton, 12.9,
personality
• @FCBarcelona, 12.4, team
• @ashleytisdale, 12.2, actor
• @ESPN, 10.5
• @ricky_martin, 10.5, singer
• @ZacEfron, 9.3, actor
• @3gerardpique, 9.3, athlete
• @nickjonas, 8.5, singer
• @Victoriabeckham, 7.8,
fashion
• @NFL, 7.1
• @EvaLongoria, 7.0, actor
• @rickygervais, 5.9,
comic/host
• @PerezHilton, 5.9, blogger
• @106andpark, 5.6, show
• @premierleague, 5.4, news

18. Retweets
• 339,835 (62.3%) of the tweets were retweets.
• The retweets included 39,301 unique tweets.
• Each retweet was retweeted an average of 8.6
times.
• Max = 28,084

19. Most Retweeted Users
• @niallofficial, 28,084, singer
• @tweetlikeagiri, 21,161, parody
• @camerondallas, 17,291, Viner
• @drakevslilwayne 12,142, tour
• @nashgrier, 11,437, Viner
• @Justinbieber, 4,013, singer
• @relatablequote, 3,525, content
• @djokernole, 3,417, athlete
• @victoriabeckham, 2,452, fashion
• @jccaylen, 2,346, YouTuber
• @jarpad, 2,092, actor
• @girlposts, 2,064, anon. model
• @twhiddleston, 1,923, actor
• @peopie, 1,805, content
• @jackalltimelow, 1,735, musician
• @chelseafc, 1,660, team

20. Most Popular Accounts for Retweets

21. Mentions
• Tweets with mentions: 152,015
• Total user mentions: 198,158
• A total of 45,926 unique users included in
mentions.
• Max mentions = 54,976
• Many tweets mentioning two “Vine stars”
were sent from a small pool of users.

22. Most Mentioned Users
• @camerondallas, 54,976, Viner
• @nashgrier, 46,188, Viner
• @Justinbieber, 4,466, singer
• @youtube, 1,601
• @louis_tomlinson, 1,540, singer
• @niallofficial, 1,449, singer
• @tweetlikeagiri, 1,280, parody
• @djokernole, 1,064, athlete
• @upworthy, 874, content
• @alsassociation, 471
• @harry_styles, 435, singer
• @zaynmalik, 369, singer
• @wismichu, 335, YouTuber
• @jccaylen, 334, YouTuber
• @sashagrey, 324, singer
• @taylorcaniff, 314, Viner

23. Most Retweeted/Mentioned Accounts
0
10000
20000
30000
40000
50000
60000
70000
80000
Retweets Mentions

24. Users who sent the most tweets
• @Avonswaggy, 496
• @xdvx, 333
• @biebzmagcon, 290
• @draperfrank, 253
• @chris_grigg, 236
• @ibiebinjacob, 234
• @tnrvdgma, 230
• @biebergan687, 227
• @foreveraround, 206
• @drewfrescura, 178
• @_b3lieberrrrr, 165
• @trappedfears, 164
• @ktiadbiebs, 161
• @dallassface, 160
• @allleeexxx10, 150
• @bangingwilks, 140

25. Structure of IBC Conversation
• Used network analysis to model the social
groupings of tweets.
• First, used a sample of 1,000 and analyzed in
NodeXL.
• Then, reduced retweet data to 350 users
receiving 100+ retweets (n=220,685 retweets).
Randomly selected 30,000 for modeling on
Gephi.
• Also, used this same dataset (n=220,685
retweets) and modeled using D3.

26. Sample of 1,000 tweets, resulting in 1,457 edges analyzed in NodeXL.
Vertices were grouped by cluster using the Clauset-Newman-Moore
algorithm.

27. Force Atlas 2 with random sample of 30,000 tweets, using the Giant Component and
grouped by modularity on Gephi
@niallofficial@djokernole
@drakevslilwayne
@tweetlikeagiri
@nashgrier
@camerondallas
@justinbieber
@relatablequote

28. https://dl.dropboxusercontent.com/u/101985018/AVC/Retweets/index.html

30. Conclusion
• Although ALS was widely mentioned, donating
was not.
• The most prominent retweeted and mentioned
users were singers, Viners/YouTubers, and
content sites, especially those that appealed to
youth.
• Moderate correlation btw mentions and
retweets.
• Those who retweeted/mentioned often focused
on one celeb.
• Network graph could be described as
brand/public topic.

31. Discussion
• Twitter provides a platform for young fans to
interact with celebrities and for celebs to build
their followers.
• Users were possibly not very engaged in the
cause, but more concerned about being part of
the conversation.
• Twitter conversation can possibly lead to
increased awareness.
• Celeb worship may have inspired some users to
create their own videos/donate.

32. Social Media Activist Campaigns
• Slacktivism or Hashtag Activism?
– Interest in campaign online can lead to more
attention in mainstream media.
– Even though people are exposed to much content,
people also now have many different ways to
consume and distribute information.
– Social media provide a way to engage with issues
that might have seemed too distant previously.

33. Thank You!
@KelliSBurns