Pinboard on Pinterest is an emerging media to engage online social media users, on which users post online images for specific topics. Regardless of its significance, there is little previous work specifically to facilitate information discovery based on pinboards. This paper proposes a novel pinboard recommendation system for Twitter users. In order to associate contents from the two social media platforms, we propose to use MultiLabel classification to map Twitter user followees to pinboard topics and visual diversification to recommend pinboards given user interested topics. A preliminary experiment on a dataset with 2000 users validated our proposed system.
aims to merge social signals from different network to increase online social media platform engagement. For example, in @cite_4 , Yan al proposed to identify the best Twitter accounts to promote YouTube videos, by mining the associations between topics learning from user tweets and their favorite YouTube videos.
@cite_4
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We introduce a novel cross-network collaborative problem in this work: given YouTube videos, to find optimal Twitter followees that can maximize the video promotion on Twitter. Since YouTube videos and Twitter followees distribute on heterogeneous spaces, we present a cross-network association-based solution framework. Three stages are addressed: (1) heterogeneous topic modeling, where YouTube videos and Twitter followees are modeled in topic level; (2) cross-network topic association, where the overlapped users are exploited to conduct cross-network topic distribution transfer; and (3) referrer identification, where the query YouTube video and candidate Twitter followees are matched in the same topic space. Different methods in each stage are designed and compared by qualitative as well as quantitative experiments. Based on the proposed framework, we also discuss the potential applications, extensions, and suggest some principles for future heterogeneous social media utilization and cross-network collaborative applications. Ranking tweets is a fundamental task to make it easier to distill the vast amounts of information shared by users. In this paper, we explore the novel idea of ranking tweets on a topic using heterogeneous networks. We construct heterogeneous networks by harnessing cross-genre linkages between tweets and semantically-related web documents from formal genres, and inferring implicit links between tweets and users. To rank tweets effectively by capturing the semantics and importance of different linkages, we introduce Tri-HITS, a model to iteratively propagate ranking scores across heterogeneous networks. We show that integrating both formal genre and inferred social networks with tweet networks produces a higher-quality ranking than the tweet networks alone. 1 Title and Abstract in Chinese u By combining multiple social media datasets, it is possible to gain insight into each dataset that goes beyond what could be obtained with either individually. In this paper we combine user-centric data from Twitter with video-centric data from YouTube to build a rich picture of who watches and shares what on YouTube. We study 87K Twitter users, 5.6 million YouTube videos and 15 million video sharing events from user-, video- and sharing-event-centric perspectives. We show that features of Twitter users correlate with YouTube features and sharing-related features. For example, urban users are quicker to share than rural users. We find a superlinear relationship between initial Twitter shares and the final amounts of views. We discover that Twitter activity metrics play more role in video popularity than mere amount of followers. We also reveal the existence of correlated behavior concerning the time between video creation and sharing within certain timescales, showing the time onset for a coherent response, and the time limit after which collective responses are extremely unlikely. Response times depend on the category of the video, suggesting Twitter video sharing is highly dependent on the video content. To the best of our knowledge, this is the first large-scale study combining YouTube and Twitter data, and it reveals novel, detailed insights into who watches (and shares) what on YouTube, and when. There has been significant recent interest in using the aggregate information from social media sites (e.g., Twitter) to identify influencers. To investigate this issue, one dynamic diversity-dependent algorithm is proposed for detecting the influencers by evaluating the influence of users throughout social networks. Comparative analyses with the existing methods on either synthetic social networks or real Twitter data show that our strategy performs best. It implies that the pattern of the influence propagation should be updated dynamically to reflect the flow of influence spread to better capture the rapidly changing dynamics of microblogs. Our proposed scheme is therefore practical and feasible to be deployed in the real world.