• DocumentCode
    2575500
  • Title

    An Empirical Study of User Behaviors on Pinterest Social Network

  • Author

    Ziming Feng ; Feng Cong ; Kailong Chen ; Yong Yu

  • Volume
    1
  • fYear
    2013
  • fDate
    17-20 Nov. 2013
  • Firstpage
    402
  • Lastpage
    409
  • Abstract
    Many previous research works have focused on analyzing online social networks in many dimensions, as they bring large commercial value and provide significant references for many other subjects. Pinterest, a pinboard-style image sharing social service, has attracted much attention recently and become one of the most popular social networks. The special mechanism and style of Pinterest distinguish it from other previous popular social networks and shape totally different user behaviors, which makes it worth launching a new study on Pinterest. In this paper, we have an empirical study of user behaviors on Pinterest, based on a large Pinterest dataset containing 1.13 million users, 57 million following relations, 19 million boards and 933 million pins. We focus on studying the characteristics, manifestations and overall effects of user behaviors from many aspects, including the user interests, correlations between neighboring users, the common features of the most popular users, the topology of the network structure, etc. The most distinguishing characteristic of Pinterest is that users focus on everyday lives and are willing to collect many images about decoration, food, fashion, etc, which makes Pinterest an ideal advertising and marketing platform for retail companies. Many other qualitative and quantitative analysis on user behaviors have been discussed in our paper. We provide a comprehensive understanding of user behaviors on the Pinterest social network, which is our main contribution.
  • Keywords
    advertising data processing; behavioural sciences computing; retail data processing; social networking (online); Pinterest characteristic; Pinterest dataset; Pinterest social network; advertising; marketing; network structure topology; online social networks; pinboard-style image sharing social service; qualitative analysis; quantitative analysis; retail companies; user behaviors; user interests; Facebook; Internet; Pins; Sociology; Twitter; Pinterest; Social Network; User Behavior;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013 IEEE/WIC/ACM International Joint Conferences on
  • Conference_Location
    Atlanta, GA
  • Print_ISBN
    978-1-4799-2902-3
  • Type

    conf

  • DOI
    10.1109/WI-IAT.2013.57
  • Filename
    6690043