This presentation on Twitter Dynamics is part of the ARCOMEM training curriculum. Feel free to roam around or contact us on Twitter via @arcomem to learn more about ARCOMEM training on archiving Social Media.

1. Twitter Dynamics
ATHENA – Research and Innovation Center in Information,
Communication and Knowledge Technologies

2. Objective
• Based on data extracted from Twitter, we construct
a network of time-stamped term associations
• Queries that can be answered
– “What are the hashtags associated with #obama at time instance t?”
– “Give me the tweets that mention #cnn during the periods that
#obama is associated with #romney”
– “How the hashtags associated with #obamawins have evolved over
time?”
2Twitter Dynamics

3. Temporal Term Associations Model
• Model is a set of quintuples
• n and c are target and context nodes, respectively,
corresponding to terms in tweets
• T is the set of time instances for which a tuple is valid
• g is the time granularity
• w is the association weight
– based on co-occurrence of hashtags
– Higher weight means more likely to see n and c in the same tweet
3
M = n,c,w,T,g n,c ∈ V,w ∈ [0,1],T ∈ 2Ζ+
,g ∈ Z +{ }
Twitter Dynamics

4. Query operators
– M’ = filter(M, cond);
• Select the tuples of model M satisfying cond
– M’ = fold(M, g);
• Return model M’ with base time unit multiplied by g,
• from model with weights computed hourly, we get with g=24 a
model with weights computed daily
– M’ = merge(M);
• Merge all tuples from M having the same target n and context c
– M’’ = join(M, M’, cond);
• Select the tuples of model M satisfying cond with respect to
model M’
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5. Example Query
• What are the tweets that mention #cnn during the periods
that #obama is associated with #romney
m1=model("");
m2=filter(m1, m1.n="obama" AND m1.c="romney”);
m3=filter(m1, m1.n="cnn");
m4=join(m3, m2, m3.T EQ m2.T);
5Twitter Dynamics

6. Implementation
• Developed in JAVA
• Wide range of storage options for models
– Relational databases (MySQL, Postgresql)
– Graph database (Neo4J)
– RDF triplestore (H2RDF)
• Tested with up to 13 millions of Tweets
6Twitter Dynamics

7. Implementation
• Developed in JAVA
• Wide range of storage options for models
– Relational databases (MySQL, Postgresql)
– Graph database (Neo4J)
– RDF triplestore (H2RDF)
• Tested with up to 13 millions of Tweets
6Twitter Dynamics