Pollution, bad-mouthing, and local marketing : the underground of location-based social networks.

Location Based Social Networks (LBSNs) are new Web 2.0 systems that are attracting new users in exponential rates. LBSNs like Foursquare and Yelp allow users to share their geographic location with friends through smartphones equipped with GPS, search for interesting places as well as posting tips about existing locations. By allowing users to comment on locations, LBSNs increasingly have to deal with new forms of spammers, which aim at advertising unsolicited messages on tips about locations. Spammers may jeopardize the trust of users on the system, thus, compromising its success in promoting location-based social interactions. In spite of that, the available literature is very limited in providing a deep understanding of this problem. In this paper, we investigated the task of identifying different types of tip spam on a popular Brazilian LBSN system, namely Apontador. Based on a labeled collection of tips provided by Apontador as well as crawled information about users and locations, we identified three types of irregular tips, namely local marketing, pollution and, bad-mouthing. We leveraged our characterization study towards a classification approach able to differentiate these tips with high accuracy

Characterizing user navigation and interactions in online social networks.

Understanding how users navigate and interact when they connect to socialnetworking sites creates opportunities for better interface design, richer studies of social interactions, and improved design of content distribution systems. In this paper, we present an in-depth analysis of user workloads in online social networks. This study is based on detailed clickstream data, collected over a 12-day period, summarizing HTTP sessions of 37,024 users who accessed four popular social networks: Orkut, MySpace, Hi5, and LinkedIn. The data were collected from a social network aggregator website in Brazil, which enables users to connect to multiple social networks with a single authentication. Our analysis of the clickstream data reveals key features of the social network workloads, such as how frequently people connect to social networks and for how long, as well as the types and sequences of activities that users conduct on these sites. Additionally, we gather the social network topology of Orkut, so that we could analyze user interaction data in light of the social graph. Our data analysis suggests insights into how users interact with friends in Orkut, such as how frequently users visit their friends’ and non-immediate friends’ pages. Results show that browsing, which cannot be inferred from crawling publicly available data, accounts for 92% of all user activities. Consequently, compared to using only crawled data, silent interactions like browsing friends’ pages increase the measured level of interaction among users. Additionally, we find that friends requesting content are often within close geographical proximity of the uploader. We also discuss a series of implications of our findings for efficient system and interface design as well as for advertisement placement in online social networks

Delayed information cascades in Flickr : measurement, analysis, and modeling.

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

Entendendo a twitteresfera brasileira.

O Twitter vem constantemente crescendo como um importante sistema onde usuários discutem sobre tudo, expressando opiniões, visão política, orientação sexual e ate mesmo humor, como felicidade ou tristeza. Redes sociais são apontadas como locais onde usuários influenciam e são influenciados por outros sendo, portanto, ambientes perfeitos para a realização de marketing boca-a-boca, propagandas e campanhas políticas. Com o intuito de oferecer entendimento sobre o uso do Twitter no Brasil, este trabalho prove uma ampla caracterização dos usuários brasileiros no Twitter e do conteúdo postado por esses usuários. Nos correlacionamos dados demográficos brasileiros com dados da localização dos usuários do Twitter para mostrar que alguns estados brasileiros estão subestimados nesse sistema. Alem disso, n´os caracterizamos os diferentes padrões linguísticos adotados, analisamos as URLs mais propagadas, e identificamos os usuários brasileiros mais influentes no Twitter em cada região brasileira.Twitter has been constantly growing as an important sys-tem where users discuss about everything, expressing opini-ons, political view, sexual orientation, and even their mood like happiness or sadness. Social networks are pointed as places where users influence and are influenced by others, being a perfect environment for word-of-mouth marketing, advertisement, and political campaigns. In order to offer a better understand of the use of Twitter in Brazil, this work provides a wide characterization of Brazilian users in Twitter as well as a deep understand of the content posted by Bra-zilians in Twitter. We correlate Brazilian demographic data with geographic data from the Twitter users to show that some Brazilian states are underestimated in Twitter. Ad-ditionally, we characterize the different linguistic patterns adopted, we analyzed the most propagated URLs, and we identified the most influential Brazilian users in Twitter on each Brazilian region