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In recent times, the growth in the use of Social Network Service (SNS) has caused an exponential growth in the generation of data among people. As social relations are established and communication becomes more active through SNS in the Big Data era, defining relationships among people becomes increasingly important. One way to define this relationship is through Social Network Analysis. This study applies categories from news articles (e.g. Culture, Politics and Economy etc.) to Twitter and extracts thematic keywords from Twitter. Those users who refer to the extracted keywords are selected, and WKC (Weight of Key Player Connectivity) is given to the relationships among the selected users to form a weighted network. Finally, we detect the key player using M-reach closeness centrality to the weighted network. In this study, the user ID "161" with the highest M-reach closeness centrality value (0.1213) points to the user who is the key player. It means that the ID "161" turns out to be a key player in the network who can communicate in the best manner with all the other users on the topic "culture." Finding a key player for each subject is advantageous because information on the subject can then be obtained on the SNS through the key player. [ABSTRACT FROM AUTHOR]