As one of the leading platforms for creative content, Tumblr offers advertisers a unique way of creating brand identity. Advertisers can tell their story through images, animation, text, music, video, and more, and can promote that content by sponsoring it to appear as an advertisement in the users' live feeds. In this paper, we present a framework that enabled two of the key targeted advertising components for Tumblr, gender and interest targeting. We describe the main challenges encountered during the development of the framework, which include the creation of a ground truth for training gender prediction models, as well as mapping Tumblr content to a predefined interest taxonomy. For purposes of inferring user interests, we propose a novel semi-supervised neural language model for categorization of Tumblr content (i.e., post tags and post keywords). The model was trained on a large-scale data set consisting of @math billion user posts, with a very limited amount of categorized keywords, and was shown to have superior performance over the baseline approaches. We successfully deployed gender and interest targeting capability in Yahoo production systems, delivering inference for users that covers more than 90 of daily activities on Tumblr. Online performance results indicate advantages of the proposed approach, where we observed 20 increase in user engagement with sponsored posts in comparison to untargeted campaigns.
To the best of our knowledge, the Tumblr social network is considered by only a few scientific studies. In @cite_13 @cite_4 the authors discuss the problem of blog recommendation, while in @cite_14 explore Tumblr social norms. However, our work is the first paper that addresses ad targeting at Tumblr.
@cite_14 @cite_13 @cite_4
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Online social networks enable collectives of users to create and share content at scale. The diffusion of content through the network, and the resulting information cascades, are phenomena that have been widely investigated on various platforms, which facilitate information diffusion using diverse technical mechanisms, user interfaces and incentives. This paper focuses on Tumblr, an online microblogging social network with a core 'reblogging' functionality that allows information to diffuse across its network by appearing on multiple user blogs. The formation of any cascade network is visible as a list of reblogging events attached as notes to each appearance of the post in the cascade. In this paper, we examine cascade networks on Tumblr, recreated from the series of diffusion events, and analyse them from structural and temporal perspectives. To achieve this, we utilise a cascade construction model that create cascade networks, overcoming problems of a lack of contextual information and missing degraded data. Finally, we compare cascades in Tumblr with those appearing on other social network platforms. Our analysis shows that popular content on Tumblr creates 'large' cascades that are deep, branching into a large number of separate and long paths, having a consistent number of reblogs at each depth and at each given time. Tumblr, a microblogging platform and social media website, has been gaining popularity over the past few years. Despite its success, little has been studied on the human behavior and interaction on this platform. This is important as it sheds light on the driving force behind Tumblr's growth. In this work, we present a quantitative study of Tumblr based on the complete data coverage for four consecutive months consisting of 23.2 million users and 10.2 billion posts. We first explore various attributes of users, posts, and tags in detail and extract behavioral patterns based on the user generated content. We then construct a massive reblog network based on the primary user interactions on Tumblr and present findings on analyzing its topological structure and properties. Finally, we show substantial results on providing location-specific usage patterns from Tumblr, despite no built-in support for geo-tagging or user location functionality. Essentially this is done by conducting a large-scale user alignment with a different social media platform (e.g., Twitter) and subsequently propagating geo-information across platforms. To the best of our knowledge, this work is the first attempt to carry out large-scale measurement-driven analysis on Tumblr. In the age of information overload, collaborative filtering and recommender systems have become essential tools for content discovery. The advent of online social networks has added another approach to recommendation whereby the social network itself is used as a source for recommendations i.e. users are recommended items that are preferred by their friends. In this paper we develop a new model-based recommendation method that merges collaborative and social approaches and utilizes implicit feedback and the social graph data. Employing factor models, we represent each user profile as a mixture of his own and his friends' profiles. This assumes and exploits "homophily" in the social network, a phenomenon that has been studied in the social sciences. We test our model on the Epinions data and on the Tuenti Places Recommendation data, a large-scale industry dataset, where it outperforms several state-of-the-art methods. This paper intends to provide some insights of a scientific problem: how likely one's interests can be inferred from his her social connections -- friends, friends' friends, 3-degree friends, etc? Is "Birds of a Feather Flocks Together" a norm? We do not consider the friending activity on online social networking sites. Instead, we conduct this study by implementing a privacy-preserving large distribute social sensor system in a large global IT company to capture the multifaceted activities of 30,000+ people, including communications (e.g., emails, instant messaging, etc) and Web 2.0 activities (e.g., social bookmarking, file sharing, blogging, etc). These activities occupy the majority of employees' time in work, and thus, provide a high quality approximation to the real social connections of employees in the workplace context. In addition to such "informal networks", we investigated the "formal networks", such as their hierarchical structure, as well as the demographic profile data such as geography, job role, self-specified interests, etc. Because user ID matching across multiple sources on the Internet is very difficult, and most user activity logs have to be anonymized before they are processed, no prior studies could collect comparable multifaceted activity data of individuals. That makes this study unique. In this paper, we present a technique to predict the inference quality by utilizing (1) network analysis and network autocorrelation modeling of informal and formal networks, and (2) regression models to predict user interest inference quality from network characteristics. We verify our findings with experiments on both implicit user interests indicated by the content of communications or Web 2.0 activities, and explicit user interests specified in user profiles. We demonstrate that the inference quality prediction increases the inference quality of implicit interests by 42.8 , and inference quality of explicit interests by up to 101 .