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--- otm:the_matching_of_causal_sequences:example1 [2011/02/14 12:03]
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+++ otm:the_matching_of_causal_sequences:example1 [2012/02/06 19:22] (current)
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@@ Line 1: / Line 1: @@
-A kind of W2As are social network applications like [[http://twitter.com|Twitter]] and [[http://www.facebook.com|Facebook]]. Nowadays, these applications are widely used, thereby, the analysis the flow of information is an interesting research topic. This flow of information is analyzed trough messages sent and retrieved between users of these applications. This analysis is complex because it is necessary to reason about the distributed computations of a social networks application. Next, we present the analysis of popularity
+====== Analyzing Tweet Popularity ======
-of a //tweet// (a publication of a small post) in Twitter.
-{{ :otm:the_matching_of_causal_sequences:twitter.png?390x300 | Tweets & Retweets}}
+The following example shows two independent Web applications that constitute a social network application. A user can publish a tweet and this tweet can be retweeted by the other user because both users follow each other. A OTM distributed stateful aspect observes and counts each retweet of a user.
-The analysis of popularity of user tweets is a novel topic in Twitter. This analysis allows a user to know the popularity of every tweet published by him, which is measured by the number of //retweets// (republications of a tweet) that its followers do of his tweet. For example, the figure shows above four Tweeter users: Toti, Peter, Kuky, and Paul. Toti follows Peter and Peter follows Paul; Kuky follows nobody and vice versa. On the one hand, the figure also shows that Paul publishes a tweet and Peter receives this tweet and retweets it. On the other hand, the figure also shows that Kuky publishes a tweet and nobody receives it. Based on the popularity measurement, the popularity of the Paul’s tweet is one and the Kuky’s tweet is zero. Although Kuky and Paul would have published the same tweet (the same string), the popularity of Kuky is zero because the Kuky’s tweet does not cause any retweet. Commonly, this kind of analysis is carried out through [[http://en.wikipedia.org/wiki/Graph_dynamical_system|dynamic graphs]]. Sadly, this analysis is not at real-time and is complex when social networks contains a big amount of users. An alternative way is the use of the [[http://en.wikipedia.org/wiki/Vector_clocks|vector clock]] algorithm. We use **[[../../weca|WeCa]]** to analyze the popularity of every tweets of a user when the //midnight// event happens.
-The following example shows two independent Web applications that constitute a social network application. A user can publish a tweet and this tweet can be retweeted by the other user because both users follow each other. A repeated tweet (the same string) and retweet are not  analyzed.
 <html>
@@ Line 56: / Line 51: @@
 </code>
-Finally, the definition and deployment of the entity (**tm**) that analyzes the popularity of tweets is:
+Finally, the definition and deployment of the **sAspTweetPopularity** stateful aspect that determines the popularity of tweets is:
 <code javascript>
-var tm = {
+var sAspTweetPopularity = {
     kind: AFTER,
-    seqExp: countRetweet,
+    sequence: countRetweet,
     advice: function(jp, env){
         addPopularity(env.idT, env.counter);
@@ Line 66: / Line 61: @@
     spawn: newPosts
 };
-deploy(tm);
+WeCa.OTM.deploy(sAspTweetPopularity);
 </code>
 Go [[otm/the_matching_of_causal_sequences|the parent Web page]].