Suchergebnisse

We present a preliminary but groundbreaking study of the media landscape of Twitter. We use public data on whom follows who to uncover common behaviour in media consumption, the relationship between various classes of media, and the diversity of media content which social links may bring. Our analysis shows that there is a non-negligible amount of indirect media exposure, either through friends who follow particular media sources, or via retweeted messages. We show that the indirect media exposure expands the political diversity of news to which users are exposed to a surprising extent, increasing the range by between 60-98%. These results are valuable because they have not been readily available to traditional media, and they can help predict how we will read news, and how publishers will interact with us in the future.

One might think that, compared to traditional media, social media sites allow people to choose more freely what to read and what to share, especially for politically oriented news. However, reading and sharing habits originate from deeply ingrained behaviors that might be hard to change. To test the extent to which this is true, we propose a Political News Sharing (PoNS) model that holistically captures four key aspects of social psychology: gratification, selective exposure, socialization, and trust & intimacy. Using real instances of political news sharing in Twitter, we study the predictive power of these features. As one might expect, news sharing heavily depends on what one likes and agrees with (selective exposure). Interestingly, it also depends on the credibility of a news source, i.e., whether the source is a social media friend or a news outlet (trust & intimacy) as well as on the informativeness or the enjoyment of the news article (gratification). Finally, a Twitter user tends to share articles matching his own political leaning but, at times, the user also shares politically opposing articles, if those match the leaning of his followers (socialization). Based on our PoNS model, we build a prototype of a news sharing application that promotes serendipitous political readings along our four dimensions.

International audience ; Data brokers such as Acxiom and Experian are in the business of collecting and selling data on people; the data they sell is commonly used to feed marketing as well as political campaigns. Despite the ongoing privacy debate, there is still very limited visibility into data collection by data brokers. Recently, however, online advertising services such as Facebook have begun to partner with data brokers-to add additional targeting features to their platform-providing avenues to gain insight into data broker information. In this paper, we leverage the Facebook advertising system-and their partnership with six data brokers across seven countries-in order to gain insight into the extent and accuracy of data collection by data brokers today. We find that a surprisingly large percentage of Facebook accounts (e.g., above 90% in the U.S.) are successfully linked to data broker information. Moreover, by running controlled ads to 183 crowdsourced U.S.-based volunteers, we find that at least 40% of data broker sourced user attributes are not at all accurate, that users can have widely varying fractions of inaccurate attributes, and that even important information such as financial information can have a high degree of inaccuracy. Overall, this paper provides the first fine-grained look into the extent and accuracy of data collection by offline data brokers, helping to inform the ongoing privacy debate.

International audience ; Data brokers such as Acxiom and Experian are in the business of collecting and selling data on people; the data they sell is commonly used to feed marketing as well as political campaigns. Despite the ongoing privacy debate, there is still very limited visibility into data collection by data brokers. Recently, however, online advertising services such as Facebook have begun to partner with data brokers-to add additional targeting features to their platform-providing avenues to gain insight into data broker information. In this paper, we leverage the Facebook advertising system-and their partnership with six data brokers across seven countries-in order to gain insight into the extent and accuracy of data collection by data brokers today. We find that a surprisingly large percentage of Facebook accounts (e.g., above 90% in the U.S.) are successfully linked to data broker information. Moreover, by running controlled ads to 183 crowdsourced U.S.-based volunteers, we find that at least 40% of data broker sourced user attributes are not at all accurate, that users can have widely varying fractions of inaccurate attributes, and that even important information such as financial information can have a high degree of inaccuracy. Overall, this paper provides the first fine-grained look into the extent and accuracy of data collection by offline data brokers, helping to inform the ongoing privacy debate.

International audience ; Data brokers such as Acxiom and Experian are in the business of collecting and selling data on people; the data they sell is commonly used to feed marketing as well as political campaigns. Despite the ongoing privacy debate, there is still very limited visibility into data collection by data brokers. Recently, however, online advertising services such as Facebook have begun to partner with data brokers-to add additional targeting features to their platform-providing avenues to gain insight into data broker information. In this paper, we leverage the Facebook advertising system-and their partnership with six data brokers across seven countries-in order to gain insight into the extent and accuracy of data collection by data brokers today. We find that a surprisingly large percentage of Facebook accounts (e.g., above 90% in the U.S.) are successfully linked to data broker information. Moreover, by running controlled ads to 183 crowdsourced U.S.-based volunteers, we find that at least 40% of data broker sourced user attributes are not at all accurate, that users can have widely varying fractions of inaccurate attributes, and that even important information such as financial information can have a high degree of inaccuracy. Overall, this paper provides the first fine-grained look into the extent and accuracy of data collection by offline data brokers, helping to inform the ongoing privacy debate.