The wisdom of social multimedia: using flickr for prediction and forecast

Social multimedia hosting and sharing websites, such as Flickr, Facebook, Youtube, Picasa, ImageShack and Photobucket, are increasingly popular around the globe. A major trend in the current studies on social multimedia is using the social media sites as a source of huge amount of labeled data for solving large scale computer science problems in computer vision, data mining and multimedia. In this paper, we take a new path to explore the global trends and sentiments that can be drawn by analyzing the sharing patterns of uploaded and downloaded social multimedia. In a sense, each time an image or video is uploaded or viewed, it constitutes an implicit vote for (or against) the subject of the image. This vote carries along with it a rich set of associated data including time and (often) location information. By aggregating such votes across millions of Internet users, we reveal the wisdom that is embedded in social multimedia sites for social science applications such as politics, economics, and marketing. We believe that our work opens a brand new arena for the multimedia research community with a potentially big impact on society and social sciences.

[1]  F. Bass.  A new product growth model for consumer durables , 1976 .

[2]  Jiebo Luo,et al.  A WORLDWIDE TOURISM RECOMMENDATION SYSTEM BASED ON GEOTAGGED WEB PHOTOS , 2010 .

[3]  Yiannis S. Boutalis,et al.  CEDD: Color and Edge Directivity Descriptor: A Compact Descriptor for Image Indexing and Retrieval , 2008, ICVS.

[4]  Seymour Sudman,et al.  Survey Research in the United States: Roots and Emergence 1890-1960. , 1987 .

[5]  Irma J. Terpenning,et al.  STL : A Seasonal-Trend Decomposition Procedure Based on Loess , 1990 .

[6]  Jiebo Luo,et al.  Aworldwide tourism recommendation system based on geotaggedweb photos , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[7]  David M. Pennock,et al.  Using internet searches for influenza surveillance. , 2008, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[8]  P. Cowpertwait,et al.  Introductory Time Series with R , 2009 .

[9]  Jeremy Ginsberg,et al.  Detecting influenza epidemics using search engine query data , 2009, Nature.

[10]  Armin R. Mikler,et al.  A Computational Framework to Study Public Health Epidemiology , 2009, 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing.

[11]  Jing Huang,et al.  Image indexing using color correlograms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  James Ze Wang,et al.  Image retrieval: Ideas, influences, and trends of the new age , 2008, CSUR.

[13]  Scott B. MacKenzie,et al.  Common method biases in behavioral research: a critical review of the literature and recommended remedies. , 2003, The Journal of applied psychology.

[14]  Nenghai Yu,et al.  Flickr distance , 2008, ACM Multimedia.

[15]  Gang Wang,et al.  iRIN: image retrieval in image-rich information networks , 2010, WWW '10.

[16]  Frank M. Bass,et al.  A New Product Growth for Model Consumer Durables , 2004, Manag. Sci..

[17]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[18]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[19]  Wendy W. Chapman,et al.  Analysis of Web Access Logs for Surveillance of Influenza , 2004, MedInfo.

[20]  Gunther Eysenbach,et al.  Infodemiology: Tracking Flu-Related Searches on the Web for Syndromic Surveillance , 2006, AMIA.