Measurement and analysis of OSN ad auctions

Abstract

Advertising is ubiquitous on the Web; numerous ad networks serve billions of ads daily via keyword or search term auctions. Recently, online social networks (OSNs) such as Facebook have created site-specific ad services that differ from traditional ad networks by letting advertisers bid on users rather than keywords. With Facebook's annual ad revenue exceeding $4 billion, OSN-based ad services are emerging to be a significant fraction of the online ad market. In contrast to other online ad markets (e.g., Google's ad market), there has been little academic study of OSN ad services, and OSNs have released very little data about their advertising markets; as a result, researchers currently lack the tools to measure and understand these markets.In this paper, our goal is to bring visibility to OSN ad markets, focusing on Facebook. We demonstrate that the (undocumented) feature that suggests bids to advertisers is most likely calculated via sampling recent winning bids. We then show how this feature can be used to explore the relative value of different user demographics and the overall stability of the advertising market. Through the exploration of suggested bid data for different demographics, we find dramatic differences in prices paid across different user interests and locations. Finally, we show that the ad market shows long-term variability, suggesting that OSN ad services have yet to mature.

Multi-Cultural Interlinking of Web Taxonomies with ACROSS

“…It is well known that advertisements are ubiquitous on today's Web economics (Abraham et al, 2013;Amarie et al, 2014a;Amarie et al, 2014b;Carrascosa et al, 2014;Carrascosa et al, 2013;Farahat and Bailey, 2012;Ghose and Yang, 2010;Ghosh et al, 2015;Gill et al, 2013;Krishnan and Sitaraman, 2013;Lacerda et al, 2006;Liu et al, 2014). The profit of most social media applications and websites stem from the advertisement sector.…”

“…Due to the popularity of advertisements on the Web, the number of ad networks that serve ads to several websites is increasing. Since most of these services are implemented as auctions, recent research has been done in an effort to better understand them (Gill et al, 2013;Liu et al, 2014;Bachrach et al, 2016;Zhang et al, 2014). In special, Liu et al (Liu et al, 2014) performed a study of the online social network ad market, using data from Facebook.…”

“…Since most of these services are implemented as auctions, recent research has been done in an effort to better understand them (Gill et al, 2013;Liu et al, 2014;Bachrach et al, 2016;Zhang et al, 2014). In special, Liu et al (Liu et al, 2014) performed a study of the online social network ad market, using data from Facebook. The authors explored suggested bid data for users from different locations and interests and they 1 http://tubularinsights.com/2019-internet-video-traffic found huge differences in prices paid.…”

A longitudinal assessment of the persistence of twitter datasets

“…Another important reason is that datasets gathered from third party services, such as social media data (Chinthakayala, Zhao, Kong, & Zhang, ; Hutton & Henderson, 2015a, 2015b; Metaxas & Mustafaraj, ), cannot always be shared in its final form owing to restrictions in the terms and conditions of the data provider. To circumvent these restrictions, researchers can share unique identifiers of individual posts, which then enable recollection of the data, as is the case with Twitter; recollecting social media datasets from unique identifiers, however, leads to the collection of a dataset that differs from the original, as part of the data may no longer be available (McCreadie et al, ; Weller & Kinder‐Kurlanda, ), for example, because user accounts may have been deleted or suspended (Liu, Kliman‐Silver, & Mislove, ). Moreover, the metadata of social media posts can also change over time as their authors update their profile, their number of followers or friends keeps changing and/or the number of shares/likes of posts is altered (Holsapple, Hsiao, & Pakath, ).…”

Who Contributes to the Knowledge Sharing Economy?

“…While, arguably, much of this process is solely hedonic, these social conversations play an increasingly larger role in today's economy. The revenue of content publishers is now primarily driven by audience originating from online social networks [30]; brands increasingly channel their products to a targeted audience alongside content exchange [27]; new business models aim at integrating with peer connections, sometimes competing with traditional firms in providing accommodation, car ride, or financial services [12,16,5]. This is unsurprising since decades of empirical studies, predating any online conversation, have shown how individuals rely on their peers or contacts to acquire information before making a choice.…”