who are my audiences? evolution of target audiences in microblogs
DESCRIPTION
User behavior in online social media is not static, it evolves through the years. In Twitter, we have witnessed a maturation of its platform and its users due to endogenous and exogenous reasons. While the research using Twitter data has expanded rapidly, little work has studied the change/evolution in the Twitter ecosystem itself. In this pa- per, we use a taxonomy of the types of tweets posted by around 4M users during 10 weeks in 2011 and 2013. We classify users according to their tweeting behavior, and nd 5 clusters for which we can associate a different dominant tweeting type. Furthermore, we observe the evolution of users across groups between 2011 and 2013 and interesting insights such as the decrease in conversations and increase in URLs sharing. Our findings suggest that mature users evolve to adopt Twitter as a news media rather than a social network.TRANSCRIPT
Who are my Audiences? Evolution of Target
Audiences in Microblogs Ruth García-Gavilanes Andreas Kaltembrunner1, Diego Sáez-Trumper2
Ricardo Baeza-Yates2, Pablo Aragón1, David Laniado1
Universitat Pompeu Fabra, Barcelona Media1, Yahoo Labs2
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Imagined Audiences
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Imagined Audiences
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Contributions
• Propose a taxonomy to categorize tweets by language independent features
• Characterize users by the way they tweet
• Analyze the evolution of imagined audiences over time
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8M
Users who tweeted at least once during 10 weeks in 2011
(April-July)
DATA
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8M
4.3M
2011 Users who tweeted at least once
during 10 weeks (April-July)
2011 and 2013
DATA
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Active in 2011 & 2013
2011 2013
Users 1,315,313 1,125,968
English Tweets
406,719,999 256,330,241
Min 1 and max 22 tweet per working day.
8M
4.3M
770K
1.1M 2011
2013
2011 2013
DATA
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Inactive < 1 tweet per day Hyperactive > 22 per day
530K 570K
1.3M
Language Independent Approach
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Tweets
With links Without links With links Without links
Original tweets (OT) Re-tweets (RT)
Mentions No Mentions
Mentions No Mentions
Language Independent Approach
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No Mentions
Tweets
With links
Original tweets (OT)
Without links
Mentions
Re-tweets (RT)
No Mentions
With links Without links
Mentions
% % % % % % 2011
% % % % % % 2013
Clusters
0%
25%
50%
75%
100%
0%
25%
50%
75%
100%
0%
25%
50%
75%
100%
0%
25%
50%
75%
100%
0%
25%
50%
75%
100%
Type of tweet OT with links and mentionsOT with links and no mentions
OT with mentions and no linksOT without links or mentions
RT with linksRT without links
Endogenous Conversationalists Generalists
Echoers Link Feeders
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Endogenous
• Social network: self-contained original posts (no mentions or links) and conversations
0%
25%
50%
75%
100%Endogenous
Type of tweet OT with links and mentionsOT with links and no mentions
OT with mentions and no linksOT without links or mentions
RT with linksRT without links
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Conversationalists
• Social network: emphasis on interacting with other users more than sharing self-contained ideas.
Type of tweet OT with links and mentionsOT with links and no mentions
OT with mentions and no linksOT without links or mentions
RT with linksRT without links
Conversationalists
0%
25%
50%
75%
100%
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Generalists
• Without a distinctive type: links slightly higher.
Type of tweet OT with links and mentionsOT with links and no mentions
OT with mentions and no linksOT without links or mentions
RT with linksRT without links
0%
25%
50%
75%
100%Generalists
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Echoers
• News media: Forwarding other people's self-contained tweets.
Type of tweet OT with links and mentionsOT with links and no mentions
OT with mentions and no linksOT without links or mentions
RT with linksRT without links
Echoers
0%
25%
50%
75%
100%
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Link Feeders
• Mostly tweet messages containing external links and no mentions.
Type of tweet OT with links and mentionsOT with links and no mentions
OT with mentions and no linksOT without links or mentions
RT with linksRT without links
Link Feeders
0%
25%
50%
75%
100%
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Sankey diagram: Users 2011 vs 2013
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How about users who became inactive or hyperactive?
2013 2011
Endog. Conver. Generalists Echoers L. Feeders Inactive Hyper/Bots
Endog. 22.38% 5.89% 5.96% 3.56% 3.02% 58.33% 0.86%
Conver. 11.33% 20.79% 7.26% 3.54% 2.41% 53.80% 0.87%
Generalists 2.67% 3.88% 21.78% 2.17% 7.02% 62.07% 0.41%
Echoers 9.93% 3.72% 8.31% 9.93% 3.65% 63.62% 0.84%
L. Feeders 3.38% 1.47% 11.11% 1.25% 22.59% 59.45% 0.75%
Inactive 6.64% 3.30% 3.12% 2.38% 2.31% 82.00% 0.26%
Hyper./Bots 28.13% 17.42% 8.15% 6.48% 4.91% 26.71% 8.19%
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CONCLUSIONS
• Tendency to become “inactive” or same type of behavior over years with exception of echoers.
• Decrease of conversationalists over time.
• More Link Feeders and Generalists.
• What is Twitter, a Social Network or a News Media? [Kwak et al., 2010] : • Mature users tend to use Twitter more as news
media.
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QUESTIONS? @ruthygarcia
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Users per Cluster
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Future Work
• Look closely at the behavior of the inactive and hyperactive users
• Lexical variation in conversations, tweets
• Language vs. behavior
• Change in behavior vs. change in popularity
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K-Means
k = 5
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2 4 6 8 10 12 14
1e+0
52e
+05
3e+0
54e
+05
Number of Clusters
With
in g
roup
s su
m o
f squ
ares
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