Mariana Arantes
ABSTRACT
Faced with the challenge of attracting user attention and revenue, social media websites have turned to video advertisements (video-ads). While in traditional media the video-ad market is mostly based on an interaction between content providers and marketers, the use of video-ads in social media has enabled a more complex interaction, that also includes content creator and viewer preferences. To better understand this novel setting, we present the first data-driven analysis of video-ad exhibitions on YouTube.
- I. Abraham, S. Athey, M. Babaioff, and M. Grubb. Peaches, Lemons, and Cookies: Designing Auction Markets with Dispersed Information. In Proc. EC, 2013. Google Scholar
Digital Library
- M. C. Amarie, I. Pefkianakis, and H. Lundgren. Mobile Video Ad Caching on Smartphones. In Proc: Ubicomp, 2014. Google ScholarDigital Library
- M. C. Amarie, I. Pefkianakis, and H. Lundgren. Towards Understanding Mobile Video Ads. In Proc. HotMobile, 2014.Google Scholar
- J. M. Carrascosa, R. González, R. Cuevas, and A. Azcorra. Are Trending Topics Useful for Marketing? In Proc. COSN, 2013. Google ScholarDigital Library
- J. M. Carrascosa, J. Mikians, R. Cuevas, V. Erramilli, and N. Laoutaris. Understanding Interest-Based Behavioural Targeted Advertising. Arxiv preprint arXiv:1411-5281, 2014.Google Scholar
- M. Cha, H. Kwak, P. Rodriguez, Y.-Y. Ahn, and S. Moon. Analyzing the Video Popularity Characteristics of Large-Scale User Generated Content Systems. IEEE/ACM Transactions on Networking, 17(5):1357--1370, 2009. Google ScholarDigital Library
- X. Drèze and F. Zufryden. Measurement of Online Visibility and Its Impact on Internet Traffic. Journal of Interactive Marketing, 18(1):20--37, Jan. 2004.Google ScholarCross Ref
- A. Farahat and M. C. Bailey. How Effective is Targeted Advertising? In Proc. WWW, 2012. Google ScholarDigital Library
- F. Figueiredo, J. M. Almeida, M. A. Gonçalves, and F. Benevenuto. On the Dynamics of Social Media Popularity. ACM Transactions on Internet Technology, 14(4):1--23, Dec. 2014. Google ScholarDigital Library
- A. Finamore, M. Mellia, M. Meo, M. M. Munafo, and D. Rossi. Experiences of Internet Traffic Monitoring with Tstat. Network, IEEE, 25(3):8--14, 2011.Google ScholarCross Ref
- A. Ghose and S. Yang. An Empirical Analysis of Search Engine Advertising: Sponsored Search in Electronic Markets. INFORMS: Management Science, 2010. Google ScholarDigital Library
- A. Ghosh, M. Mahdian, R. P. McAfee, and S. Vassilvitskii. To Match or Not to Match: Economics of Cookie Matching in Online Advertising. ACM Transactions on Economics and Computation, 3(2):1--18, Apr. 2015. Google ScholarDigital Library
- P. Gill, M. Arlitt, Z. Li, and A. Mahanti. Youtube Traffic Characterization: A View From the Edge. In Proc. IMC, 2007. Google ScholarDigital Library
- P. Gill, V. Erramilli, A. Chaintreau, B. Krishnamurthy, K. Papagiannaki, and P. Rodriguez. Follow the Money: Understanding Economics of Online Aggregation and Advertising. In Proc. IMC, 2013. Google ScholarDigital Library
- S. S. Krishnan and R. K. Sitaraman. Understanding the Effectiveness of Video Ads: A Measurement Study. In Proc. IMC, 2013. Google ScholarDigital Library
- A. Lacerda, M. Cristo, M. A. Gonçalves, W. Fan, N. Ziviani, and B. Ribeiro-Neto. Learning to Advertise. In Proc. SIGIR, 2006. Google ScholarDigital Library
- Y. Liu, C. Kliman-Silver, R. Bell, B. Krishnamurthy, and A. Mislove. Measurement and Analysis of OSN Ad Auctions. In Proc. COSN, 2014. Google ScholarDigital Library
- P. Manchanda, J.-P. Dubé, K. Y. Goh, and P. K. Chintagunta. The Effect of Banner Advertising on Internet Purchasing. Journal of Marketing Research, 43(1):98--108, 2006.Google ScholarCross Ref
- H. Pinto, J. Almeida, and M. Gonçalves. Using Early View Patterns to Predict the Popularity of YouTube Videos. In Proc. WSDM, 2013. Google ScholarDigital Library
- K. Radinsky, K. Svore, S. Dumais, J. Teevan, A. Bocharov, and E. Horvitz. Behavioral Dynamics on the Web: Learning, Modeling, and Prediction. ACM Transactions on Information Systems, 32(3):1--37, 2013. Google ScholarDigital Library
- A. Rao, A. Legout, Y.-s. Lim, D. Towsley, C. Barakat, and W. Dabbous. Network Characteristics of Video Streaming Traffic. In Proc. CONEXT, 2011. Google ScholarDigital Library
- F. Schneider, A. Feldmann, B. Krishnamurthy, and W. Willinger. Understanding Online Social Network Usage from a Network Perspective. In Proc. IMC, 2009. Google ScholarDigital Library
- Variety. YouTube U.S. Video-Ad Revenue Will Top $1 Billion in 2014: Report, 2014.Google Scholar
- Variety. Facebook's Bite into TV: Social Giant to Hit $3.8 Billion in Video-Ad Sales by 2017, Analyst Says, Mar. 2015.Google Scholar
- Wall-Street-Journal. YouTube Alone Brings in 20% of Digital Video Ad Revenue, Sept. 2014.Google Scholar
- L. Wasserman. All of Statistics. Springer Publishing Company, Incorporated, 2010.Google Scholar
- J. Yang and J. Leskovec. Patterns of Temporal Variation in Online Media. In Proc. WSDM, 2011. Google ScholarDigital Library
Index Terms
- Understanding video-ad consumption on YouTube: a measurement study on user behavior, popularity, and content properties
Recommendations
The 'WeTube' in YouTube – creating an online community through video sharing
Video sharing has become a growing social practice, with YouTube being the predominant online video sharing site. Most of the research concerning YouTube's social impact has been focused on quantitative evaluation of the social interaction facilitated ...
Exploring the user-generated content (UGC) uploading behavior on youtube
WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide WebYouTube is the world's largest video sharing platform where both professional and non-professional users participate in creating, uploading, and viewing content. In this work, we analyze content in the music category created by the non-professionals, ...
Understanding video interactions in youtube
MM '08: Proceedings of the 16th ACM international conference on MultimediaThis paper seeks understanding the user behavior in a social network created essentially by video interactions. We present a characterization of a social network created by the video interactions among users on YouTube, a popular social networking video ...
Comments