1. news

Guannan Liu, Yanjie Fu, Tong Xu, Hui Xiong, Guoqing Chen
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Marketing goals
Companies open up official accounts on
social media such as Twitter, Facebook.
 Coupons, deals, products
 Let their names heard by maximal audience on
social media
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But, how…?
 URLs [Rodrigues et al, 2011], Hashtags [Zarrela, 2010]
 Pictures, videos
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Principles: Do right things at right time
 Summer sale, Holiday sale
 Customers behave periodically according to seasons
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What about timing in social media marketing?
 Do users follow some innate temporal patterns in
using social media?
 Some observations
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Time gap analysis
 Hours after publishing vs. retweeting frequency
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Assumption: The publishing time of the
tweets is significant in influencing the
number of retweets.
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Publishing time (24h) vs. number of retweets
Assumption: Each account has its own
optimal time to post tweets on specific topics
in order to get most retweets.
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What drives the difference of being retweeted?
 Users’ distinctive retweeting behaviors
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What factors influence users’ retweeting
decisions?
 When to sign in?
 Who(se) tweets to read?
 What topics interest the users?
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Examples:
 (early morning, news agency accounts, news)
 (night, entertainment accounts, movies/music)
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Factors influencing retweeting are
summarized as:
 (when, who, and what)=>3w patterns.
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Given the retweeting traces (u, tr, fd, d, tp)
 Discover the 3w retweeting patterns.
▪ Temporal popular topics
▪ Viral marketing strategies: personalized time-aware
tweets recommendation
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Mining 3w patterns directly?
 Each retweeting trace has particular time, author,
and contents
 Correlated with each other
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Shared retweeting context (SRC)
 Users have preferences towards the contexts of
using and retweeting
 Each SRC can be viewed as a mixture of time,
author, and contents
 Bridge all the influencing 3w factors
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User Retweet Model
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Likelihood
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EM algorithm
E-step
Conditional independent
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Dataset
 Sina Weibo [Jing et al., 2013]
 Sample a collection of popular tweets
▪ Tweets that are retweeted more than 5 times.
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Dataset Statistics
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When are particular topics popular?
Topic distribution w.r.t daily periods in hours
 p(z|t)
 k=20
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What to post/push to followers at particular time?
 p(d, f | u, t )
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Candidate tweets: online session
 User must be active at the time of real retweeting behaviors
 Pseudo the tweets that the user read during his online
 The tweets posted by the users’ followees near the active time
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Evaluation metrics
 Mean Average Precision (MAP)
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Baseline methods
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Number of retweets
Content-based LDA/Temporal LDA
RankSVM
sURM
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Parameter setting
Recommendation results
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Viral marketing
 Viral features (Barbieri et al, 2014)
 Predict the popularity of online contents (Szabo et
al, 2010)
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Topic model
 Temporal, spatial topic model (Yin et al, 2011;
Yuan et al, 2013)
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Tweets recommendation
 Exploit contextual information (Chen et al, 2012)
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We addressed the publishing time of a tweet
in propagating social media marketing
campaigns.
 Observations & assumptions
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A model that captured the 3w patterns was
proposed
 shared retweeting contexts
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We experimented on real dataset to test the
predictive power of the model
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Q&A
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Some statistical analysis revealed the fact,
but far more than enough
▪ Users behave distinctively
▪ Different topics may be prevailing at different time
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With diffusion cascades considered
 Only one-step retweeting is modeled in this study.
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Other retweeting contexts?
 Location?
 Social community?
▪ Similar users may have similar behavioral patterns. E.g.,
students, office workers