The vote mechanism is widely utilized to rank answers in community-based question answering sites. In generating a vote, a user's attention is influenced by the answer position and appearance, in addition to real answer quality. Previously, these biases are ignored. As a result, the top answers obtained from this mechanism are not reliable, if the number of votes for the active question is not sufficient. In this paper, we solve this problem by analyzing two kinds of biases; position bias and appearance bias. We identify the existence of these biases and propose a joint click model for dealing with both of them. Our experiments in real data demonstrate how the ranking performance of the proposed model outperforms traditional methods with biases ignored by 15.1% in precision@1, and 11.7% in the mean reciprocal rank. A case study on a manually labeled dataset futher supports the effectiveness of the proposed model.

Current methods of sentiment analysis for social media images include low-level visual feature based approaches [Jia et Recently text-based sentiment prediction has been al., 2012; Yang et al., 2014], mid-level visual feature based extensively studied, while image-centric sentiment approaches [Borth et al., 2013; Yuan et al., 2013] and deep analysis receives much less attention. In this paper, learning based approaches [You et al., 2015]. The vast majority we study the problem of understanding human of existing methods are supervised, relying on labeled images sentiments from large-scale social media images, to train sentiment classifiers. Unfortunately, sentiment considering both visual content and contextual information, labels are in general unavailable for social media images, and such as comments on the images, captions, it is too labor-and time-intensive to obtain labeled sets large etc. The challenge of this problem lies in enough for robust training. In order to utilize the vast amount the "semantic gap" between low-level visual features of unlabeled social media images, an unsupervised approach and higher-level image sentiments. Moreover, would be much more desirable.

User interest inference from social networks is a fundamental problem to many applications. It usually exhibits dual-heterogeneities: a user's interests are complementarily and comprehensively reflected by multiple social networks; interests are inter-correlated in a nonuniform way rather than independent to each other. Although great success has been achieved by previous approaches, few of them consider these dual-heterogeneities simultaneously. In this work, we propose a structure-constrained multi-source multi-task learning scheme to co-regularize the source consistency and the tree-guided task relatedness. Meanwhile, it is able to jointly learn the task-sharing and task-specific features. Comprehensive experiments on a real-world dataset validated our scheme. In addition, we have released our dataset to facilitate the research communities.

Semantic relatedness (SR) measures form the algorithmic foundation of intelligent technologies in domains ranging from artificial intelligence to human-computer interaction. Although SR has been researched for decades, this work has focused on developing general SR measures rooted in graph and text mining algorithms that perform reasonably well for many different types of concepts. This paper introduces domain-specific SR, which augments general SR by identifying, capturing, and synthesizing domain-specific relationships between concepts. Using the domain of geography as a case study, we show that domain-specific SR — and even geography-specific signals alone (e.g. distance, containment) without sophisticated graph or text mining algorithms — significantly outperform the SR state-of-the-art for geographic concepts. In addition to substantially improving SR measures for geospatial technologies, an area that is rapidly increasing in importance, this work also unlocks an important new direction for SR research: SR measures that incorporate domain-specific customizations to increase accuracy.

There are lots of texts appearing in the web every day. This fact enables the amount of texts in the web to explode. Therefore, how to deal with large-scale text collection becomes more and more important. Clustering is a generally acceptable solution for text organization. Via its unsupervised characteristic, users can easily dig the useful information that they desired. However, traditional clustering algorithms can only deal with small-scale text collection. When it enlarges, they lose their performances. The main reason attributes to the high-dimensional vectors generated from texts. Therefore, to cluster texts in large amount, this paper proposes a novel clustering algorithm, where only the features that can represent cluster are preserved in cluster’s vector. In this algorithm, clustering process is separated into two parts. In one part, feature’s weight is fine-tuned to make cluster partition meet an optimization function. In the other part, features are reordered and only the useful features that can represent cluster are kept in cluster’s vector. Experimental results demonstrate that our algorithm obtains high performance on both small-scale and large-scale text collections.

With Community Question Answering (CQA) evolving into a quite popular method for information seeking and providing, it also becomes a target for spammers to disseminate promotion campaigns. Although there are a number of quality estimation efforts on the CQA platform, most of these works focus on identifying and reducing low-quality answers, which are mostly generated by impatient or inexperienced answerers. However, a large number of promotion answers appear to provide high-quality information to cheat CQA users in future interactions. Therefore, most existing quality estimation works in CQA may fail to detect these specially designed answers or question-answer pairs. In contrast to these works, we focus on the promotion channels of spammers, which include (shortened) URLs, telephone numbers and social media accounts. Spammers rely on these channels to connect to users to achieve promotion goals so they are irreplaceable for spamming activities. We propose a propagation algorithm to diffuse promotion intents on an "answerer-channel" bipartite graph and detect possible spamming activities. A supervised learning framework is also proposed to identify whether a QA pair is spam based on propagated promotion intents. Experimental results based on more than 6 million entries from a popular Chinese CQA portal show that our approach outperforms a number of existing quality estimation methods for detecting promotion campaigns on both the answer level and QA pair level.

In this paper we perform a large-scale homophily analysis on Twitter using a hierarchical representation of users' interests which we call a Twixonomy. In order to build a population, community, or single-user Twixonomy we first associate "topical" friends in users' friendship lists (i.e. friends representing an interest rather than a social relation between peers) with Wikipedia categories. A word-sense disambiguation algorithm is used to select the appropriate wikipage for each topical friend. Starting from the set of wikipages representing "primitive" interests, we extract all paths connecting these pages with topmost Wikipedia category nodes, and we then prune the resulting graph G efficiently so as to induce a direct acyclic graph. This graph is the Twixonomy. Then, to analyze homophily, we compare different methods to detect communities in a peer friends Twitter network, and then for each community we compute the degree of homophily on the basis of a measure of pairwise semantic similarity.We show that the Twixonomy provides a means for describing users' interests in a compact and readable way and allows for a fine-grained homophily analysis. Furthermore, we show that mid-low level categories in the Twixonomy represent the best balance between informativeness and compactness of the representation.

We propose a deep learning method for eventdriven stock market prediction. First, events are extracted from news text, and represented as dense vectors, trained using a novel neural tensor network. Second, a deep convolutional neural network is used to model both short-term and long-term influences of events on stock price movements. Experimental results show that our model can achieve nearly 6% improvements on S&P 500 index prediction and individual stock prediction, respectively, compared to state-of-the-art baseline methods. In Figure 1: Example news influence of Google Inc. addition, market simulation results show that our system is more capable of making profits than previously reported systems trained on S&P 500 stock of events can be better captured [Ding et al., 2014].

In recent times, social media has become a popular medium for many election campaigns. It not only allows candidates to reach out to a large section of the electorate, it is also a potent medium for people to express their opinion on the proposed policies and promises of candidates. Analyzing social media data is challenging as the text can be noisy, sparse and even multilingual. In addition, the information may not be completely trustworthy, particularly in the presence of propaganda, promotions and rumors. In this paper we describe our work for analyzing election campaigns using social media data. Using data from the 2012 US presidential elections and the 2013 Philippines General elections, we provide detailed experiments on our methods that use granger causality to identify topics that were most “causal” for public opinion and which in turn, give an interpretable insight into “elections topics” that were most important. Our system was deployed by the largest media organization in the Philippines during the 2013 General elections and using our work, the media house able to identify and report news stories much faster than competitors and reported higher TRP ratings during the election.

The Nystrom method provides an efficient sampling approach for large scale clustering problems, by generating a low-rank matrix approximation. However, existing sampling methods are limited by accuracy and computing time. This paper proposes an improved Nystrom-based clustering algorithm with a new sampling procedure, Minimum Sum of Squared Similarities (MSSS). Experiments on synthetic and real data sets show that the proposed sampling performs with higher accuracy than existing algorithms, applied to Nystrom-based spectral clustering problems. Furthermore, we provide a theoretical analysis that allows us to define the upper bound of the Frobenius norm error of the MSSS.