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An approach to find influencers analyzing complex social network

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dc.contributor.advisor Zaber, Dr. Moinul Islam Chaki, Dipankar 2018-01-11T08:51:13Z 2018-01-11T08:51:13Z 2017 2017-07
dc.identifier.other ID 15166004
dc.description This thesis report is submitted in partial fulfilment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2017. en_US
dc.description Cataloged from PDF version of thesis report.
dc.description Includes bibliographical references (pages 33-35).
dc.description.abstract Popularity of social media in Bangladesh is prodigious. 80 percent of internet users are on social networking websites like Facebook, Twitter. That is over 16 million people and counting. The rate of new Facebook users is outpacing the country’s birth rate as one new Bangladeshi Facebook account is opened every 20 seconds. This makes social media a great platform for government to reach out to citizens and stay up-to-date with current events and trends in society. That is why, a Facebook group named “Public Service Innovation Bangladesh” has been created. In this group, discussions related to public service innovation, public service related problems and solutions, decision making in administrative works etc. are being prioritized. The focus of this study is to construct complex network from posts given by the members of this Facebook group, analyze features of the complex network including degree distribution, assortative mixing and betweenness centrality. It is important to detect influencers of that Facebook group. We have analyzed group data from January 1, 2016 to June 30, 2017 and generated a report which has given some interesting insights about that group. During this time frame, 5183 posts have been posted and most amazingly, majority of these posts have been posted from November, 2016 to till date. So, it can be said that, this group is growing now. In our constructed network, we have seen that the people who give more posts, get more likes and comments. That is how, they tend to be connected with other highly connected people. If a person who has many connections, gives a post, gets more attention meaning likes and comments than other. Our study helps to understand the structure of this group and finds the influencers of the group. Index Terms: Complex Network Analysis, Social Network Analysis, Betweenness Centrality, Closeness Centrality, Degree Centrality, Characteristics Path Length en_US
dc.description.statementofresponsibility Dipankar Chaki
dc.format.extent 39 pages
dc.language.iso en en_US
dc.publisher BARC University en_US
dc.rights BRAC University thesis are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subject Complex social network en_US
dc.subject Betweenness centrality en_US
dc.subject Degree centrality en_US
dc.subject Closeness centrality en_US
dc.subject Social network analysis en_US
dc.title An approach to find influencers analyzing complex social network en_US
dc.type Thesis en_US
dc.contributor.department Department of Computer Science and Engineering, BRAC University M. Computer Science and Engineering

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