Referral Traffic Analysis: A Case Study of the Iranian Students News Agency (ISNA)

  • سال انتشار: 1394
  • محل انتشار: فصلنامه سیستم های اطلاعاتی و مخابرات، دوره: 4، شماره: 1
  • کد COI اختصاصی: JR_JIST-4-1_004
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 336
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نویسندگان

Roya Hassanian-Esfahani

Research Institute for ICT, ACECR, Tehran, Iran

Mohammad-Javad Kargar

Department of Computer Engineering, University of Science and Culture, Tehran, Iran

چکیده

Web traffic analysis is a well-known e-marketing activity. Today most of the news agencies have entered the web providing a variety of online services to their customers. The number of online news consumers is also increasing dramatically all over the world. A news website usually benefits from different acquisition channels including organic search services, paid search services, referral links, direct hits, links from online social media, and e-mails. This article presents the results of an empirical study of analyzing referral traffic of a news website through data mining techniques. Main methods include correlation analysis, outlier detection, clustering, and model performance evaluation. The results decline any significant relationship between the amount of referral traffic coming from a referrer website and the website's popularity state. Furthermore, the referrer websites of the study fit into three clusters applying K-means Squared Euclidean Distance clustering algorithm. Performance evaluations assure the significance of the model. Also, among detected clusters, the most populated one has labeled as Automatic News Aggregator Websites by the experts. The findings of the study help to have a better understanding of the different referring behaviors, which form around 15% of the overall traffic of Iranian Students' News Agency (ISNA) website. They are also helpful to develop more efficient online marketing plans, business alliances, and corporate strategies.

کلیدواژه ها

Referral Traffic; Data Mining; K-means Clustering; Online News

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