The theme of this workshop is to bridge data mining and visual analytics for information and knowledge management. The topics include, but not limited to, the following: Big data mining and visual analytics, theory and foundations -- Knowledge discovery ...

The proliferation of social media platforms together with the wide adoption of smartphone devices has transformed how we communicate and share news. During large-scale emergencies, such as natural disasters or armed attacks, victims, responders, and ...

In this paper, we present a new approach of recommending hashtags for tweets. It uses Learning to Rank algorithm to incorporate features built from topic enhanced word embeddings, tweet entity data, hashtag frequency, hashtag temporal data and tweet URL ...

User modeling of individual users on the Social Web platforms such as Twitter plays a significant role in providing personalized recommendations and filtering interesting information from social streams. Recently, researchers proposed the use of ...

Recommendation for user generated content sites has gained significant attention nowadays. To satisfy the niche tastes of users, product recommendation poses more challenges due to the data sparsity issue. This work is motivated by a real world online ...

In this paper we study the friend recommendation problem in event-based social networks (EBSNs). Effective friend recommendation is of benefit to EBSNs, since it can promote user interaction and accelerate information diffusion for promoted events. ...

Geotagged tweets allow one to extract geo-information-trend, search local events, and identify natural disasters. In this paper, we propose a Hidden-Markov-based model to integrate tweet contents and user movements for geotagging. A language model is ...

In event-based social networks, such as Meetup, social groups refer to self-organized communities that consist of users who share the same interests. In many real-world scenarios, users usually have social group preference and join interested social ...

With the rapid growth of social tagging systems, many efforts have been put on tag-aware personalized recommendation. However, due to uncontrolled vocabularies, social tags are usually redundant, sparse, and ambiguous. In this paper, we propose a deep ...

Social recommendation has been widely studied in recent years. Existing social recommendation models use various explicit pieces of social information as regularization terms in recommendation, for instance, social links are considered as new ...

Many online social networks can be described by signed networks, where positive links signify friendships, trust and like; while negative links indicate enmity, distrust and dislike. Predicting the sign of the links in these networks has attracted a ...

Modeling and predicting the popularity dynamics of individual user generated items on online social networks has important implications in a wide range of areas. The challenge of this problem comes from the inequality of the popularity of content and ...

Aligning heterogeneous online social networks is a highly beneficial task proposed in recent years. It targets at automatically aligning accounts from multiple networks by whether they are held by the same natural person. Aligning the networks can ...

The classical influence maximization (IM) problem in social networks does not distinguish between whether a campaign gets viral in a week or in a year. From the practical standpoint, however, campaigns for a new technology or an upcoming movie must be ...

Recently, proactive systems such as Google Now and Microsoft Cortana have become increasingly popular in reforming the way users access information on mobile devices. In these systems, relevant content is presented to users based on their context ...

Spelling correction methods, widely used and researched, usually assume a low error probability and a small number of errors per word. These assumptions do not hold in very noisy input scenarios such as eye-based typing systems. In particular for eye ...

Finding decompositions of a graph into a family of communities is crucial to understanding its underlying structure. Algorithms for finding communities in networks often rely only on structural information and search for cohesive subsets of nodes. In ...

Predicting popularity, or the total volume of information outbreaks, is an important subproblem for understanding collective behavior in networks. Each of the two main types of recent approaches to the problem, feature-driven and generative models, have ...

On Twitter-like social media sites, the re-posting statuses or tweets of other users are usually considered to be the key mechanism for spreading information. How to predict whether a tweet will be retweeted by a user has received increasing attention ...

The newly emerging location-based social networks (LBSN) such as Tinder and Momo extends social interaction from friends to strangers, providing novel experiences of making new friends. Familiar strangers refer to the strangers who meet frequently in ...