Navegando por Autor "Gummadi, Krishna P."

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Delayed information cascades in Flickr : measurement, analysis, and modeling.
(2012) Cha, Meeyoung; Souza, Fabrício Benevenuto de; Ahn, Yong-Yeol; Gummadi, Krishna P.
Online social networks exhibit small-world network characteristics, implying that information can spread in the network quickly and widely. This ability to spread information rapidly has led to high expectations for word-of-mouth and viral campaigns in online social networks. However, a recent study of the Flickr social network has shown that popular photos do not spread as quickly as one might expect, but show a steady linear growth of popularity over several years. In this paper, we investigate possible reasons for this delay in word-of-mouth propagation by studying the behavior of Flickr users. We identify two factors of a social network that can alter how information spreads: the burstiness of user login times and content aging. We study the impact of these factors using an epidemiological model that was adapted to allow us to investigate the speed of propagation in word-ofmouth propagation. Our simulation shows that the two factors can explain the patterns observed on the real data and help us to understand how these factors affect a small-world network’s ability to spread information quickly and widely.
Potential for discrimination in online targeted advertising.
(2018) Speicher, Till; Ali, Muhammad; Venkatadri, Giridhari; Ribeiro, Filipe Nunes; Arvanitakis, George; Souza, Fabrício Benevenuto de; Gummadi, Krishna P.; Loiseau, Patrick; Mislove, Alan
Recently, online targeted advertising platforms like Facebook have been criticized for allowing advertisers to discriminate against users belonging to sensitive groups, i.e., to exclude users belonging to a certain race or gender from receiving their ads. Such criticisms have led, for instance, Facebook to disallow the use of attributes such as ethnic affinity from being used by advertisers when targeting ads related to housing or employment or financial services. In this paper, we show that such measures are far from sufficient and that the problem of discrimination in targeted advertising is much more pernicious. We argue that discrimination measures should be based on the targeted population and not on the attributes used for targeting. We systematically investigate the different targeting methods offered by Facebook for their ability to enable discriminatory advertising. We show that a malicious advertiser can create highly discriminatory ads without using sensitive attributes. Our findings call for exploring fundamentally new methods for mitigating discrimination in online targeted advertising.