TY - GEN
T1 - Identification of opinion leader on rumor spreading in online social network Twitter using edge weighting and centrality measure weighting
AU - Dewi, Fatia Kusuma
AU - Yudhoatmojo, Satrio Baskoro
AU - Budi, Indra
PY - 2018/1/2
Y1 - 2018/1/2
N2 - Rumor spreading has been an essential issue for society. One of the platforms for rumor spreading is Twitter. Finding the opinion leader of its issue is also important in order to know who are users whom have a high impact of bringing the issue. So we can give the suggestion to the law authority to give the right authorization afterward. Opinion leader can be found using centrality measure metric on social network analysis study. This study has node and edge as its property. For recent years, there are many conducted researches about centrality measure. Some of them are combining some centrality measures together. Aside from defining the centrality measure, defining the edge is also important. Twitter has a different kind of relationships that can be turned into an edge, but not all the relationships have the same impact for spreading the rumor. This study conduct two experiments, first experiment is edge weighting. This experiment is aimed to see the importance of each defined edge type for finding the opinion leader. The second experiment is centrality weighting. This experiment is aimed to see the weight that could give more accurate opinion leader based on other evaluation algorithms. The study found the edge that has the ability to spread to wider audience (quote, retweet, and reply) tend to have a bigger impact for finding opinion leader than mention relationship. The study also finds that a low in-degree weight, high betweenness weight, and low or no PageRank weight could give 100% agreement upon other evaluation algorithms for finding the opinion leader.
AB - Rumor spreading has been an essential issue for society. One of the platforms for rumor spreading is Twitter. Finding the opinion leader of its issue is also important in order to know who are users whom have a high impact of bringing the issue. So we can give the suggestion to the law authority to give the right authorization afterward. Opinion leader can be found using centrality measure metric on social network analysis study. This study has node and edge as its property. For recent years, there are many conducted researches about centrality measure. Some of them are combining some centrality measures together. Aside from defining the centrality measure, defining the edge is also important. Twitter has a different kind of relationships that can be turned into an edge, but not all the relationships have the same impact for spreading the rumor. This study conduct two experiments, first experiment is edge weighting. This experiment is aimed to see the importance of each defined edge type for finding the opinion leader. The second experiment is centrality weighting. This experiment is aimed to see the weight that could give more accurate opinion leader based on other evaluation algorithms. The study found the edge that has the ability to spread to wider audience (quote, retweet, and reply) tend to have a bigger impact for finding opinion leader than mention relationship. The study also finds that a low in-degree weight, high betweenness weight, and low or no PageRank weight could give 100% agreement upon other evaluation algorithms for finding the opinion leader.
KW - Centrality Measure
KW - Opinion Leader
KW - Social Network Analysis
UR - http://www.scopus.com/inward/record.url?scp=85049415018&partnerID=8YFLogxK
U2 - 10.1109/ICDIM.2017.8244680
DO - 10.1109/ICDIM.2017.8244680
M3 - Conference contribution
T3 - 2017 12th International Conference on Digital Information Management, ICDIM 2017
SP - 313
EP - 318
BT - 2017 12th International Conference on Digital Information Management, ICDIM 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Conference on Digital Information Management, ICDIM 2017
Y2 - 12 September 2017 through 14 September 2017
ER -