| Abstrak/Abstract |
Twitter as widest micro-blogging and social media proves a billion of tweets from many users. Each tweet carry its own topic, and the tweet itself is can be retweeted by other user. Social network analysis is needed to reach the original issuer of a topic. Representing topic-specific Twitter network can be done to get the main issuer of the topic with graph based ranking algorithm. One of the algorithm is PageRank, which rank each node based on number of in-degree of that node, and inversely proportional to out-degree of the other nodes that point to that node. In proposed methodology, network graph is built from Twitter where user acts as node and tweet-retweet relation as directed edge. User who retweet the tweet points to original user who tweet. From the formed graph, each node's PageRank is calculated as well as other node properties like centrality, degree, and followers and average time retweeted. The result shows that PageRank score of node is directly proportional to closeness centrality and in-degree of node. However, the ranking with PageRank, closeness centrality, and in-degree ranking yield different ranking result. |
