Hancheng Ge

Organic or Organized?: Exploring URL Sharing Behavior

URL sharing has become one of the most popular activities on many online social media platforms. Shared URLs are an avenue to interesting news articles, memes, photos, as well as low-quality content like spam, promotional ads, and phishing sites. While some URL sharing is organic, other sharing is strategically organized with a common purpose (e.g., aggressively promoting a website). In this paper, we investigate the individual-based and group-based user behavior of URL sharing in social media toward uncovering these organic versus organized user groups. Concretely, we pro- pose a four-phase approach to model, identify, characterize, and classify organic and organized groups who engage in URL sharing. The key motivating insights of this approach are (i) that patterns of individual-based behavioral signals embedded in URL posting activities can uncover groups whose members engage in similar behaviors; and (ii) that group-level behavioral signals can distinguish between organic and organized user groups. Through extensive experiments, we find that levels of organized behavior vary by URL type and that the proposed approach achieves good performance -- an F-measure of 0.836 and Area Under the Curve of 0.921.

Multi-Aspect Streaming Tensor Completion

Tensor completion has become an effective computational tool in many real-world data-driven applications. Beyond traditional static setting, with the increasing popularity of high velocity streaming data, it requires efficient online processing without reconstructing the whole model from scratch. Existing work on streaming tensor completion is usually built upon the assumption that tensors only grow in one mode. Unfortunately, the assumption does not hold in many real-world situations in which tensors may grow in multiple modes, i.e., multi-aspect streaming tensors. Efficiently modeling and completing these incremental tensors without sacrificing its effectiveness remains a challenging task due to the uncertainty of tensor mode changes and complex data structure of multi-aspect streaming tensors. To bridge this gap, we propose a Multi-Aspect Streaming Tensor completion framework (MAST) based on CANDECOMP/PARAFAC (CP) decomposition to track the subspace of general incremental tensors for completion. In addition, we investigate a special situation where time is one mode of the tensors, and leverage its extra structure information to improve the general framework towards higher effectiveness. Experimental results on four datasets collected from various real-world applications demonstrate the effectiveness and efficiency of the proposed framework.

Uncovering the Spatio-Temporal Dynamics of Memes in the Presence of Incomplete Information

Modeling, understanding, and predicting the spatio-temporal dynamics of online memes are important tasks, with ramifications on location-based services, social media search, targeted advertising and content delivery networks. However, the raw data revealing these dynamics are often incomplete and error-prone; for example, API limitations and data sampling policies can lead to an incomplete (and often biased) perspective on these dynamics. Hence, in this paper, we investigate new methods for uncovering the full (underlying) distribution through a novel spatio-temporal dynamics recovery framework which models the latent relationships among locations, memes, and times. By integrating these hidden relationships into a tensor-based recovery framework -- called AirCP -- we find that high-quality models of meme spread can be built with access to only a fraction of the full data. Experimental results on both synthetic and real-world Twitter hashtag data demonstrate the promising performance of the proposed framework: an average improvement of over 27% in recovering the spatio-temporal dynamics of hashtags versus five state-of-the-art alternatives.