Prediction Model for Popularity of Online Articles based on Analysis of Hit Count

온라인 게시글의 조회수 분석을 통한 인기도 예측

  • 김수도 (부산대학교 사회급변현상연구소) ;
  • 조환규 (부산대학교 컴퓨터공학과)
  • Received : 2012.02.02
  • Accepted : 2012.03.21
  • Published : 2012.04.28


Online discussion bulletin in Korea is not only a specific place where user exchange opinions but also a public sphere through which users discuss and form public opinion. Sometimes, there is a heated debate on a topic and any article becomes a political or sociological issue. In this paper, we propose how to analyze the popularity of articles by collecting the information of articles obtained from two well-known discussion forums such as AGORA and SEOPRISE. And we propose a prediction model for the article popularity by applying the characteristics of subject articles. Our experiment shown that the popularity of 87.52% articles have been saturated within a day after the submission in AGORA, but the popularity of 39% articles is growing after 4 days passed in SEOPRISE. And we observed that there is a low correlation between the period of popularity and the hit count. The steady increase of the hit count of an article does not necessarily imply the final hit count of the article at the saturation point is so high. In this paper, we newly propose a new prediction model called 'baseline'. We evaluated the predictability for popular articles using three models (SVM, similar matching and baseline). Through the results of performance evaluation, we observed that SVM model is the best in F-measure and precision, but baseline is the best in running time.


Prediction;Popularity;Online Articles;Online Communities


Supported by : 한국연구재단


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