Steven Van Canneyt

Ghent University | UGhent · Department of Information Technology

As the market of globally available online news is large and still growing, there is a strong competition between online publishers in order to reach the largest possible audience. Therefore an intelligent online publishing strategy is of the highest importance to publishers. A prerequisite for being able to optimize any online strategy, is to have...

Short text messages such as tweets are very noisy and sparse in their use of vocabulary. Traditional textual representations, such as tf-idf, have difficulty grasping the semantic meaning of such texts, which is important in applications such as event detection, opinion mining, news recommendation, etc. We constructed a method based on semantic wor...

Levering data on social media, such as Twitter and Facebook, requires information retrieval algorithms to become able to relate very short text fragments to each other. Traditional text similarity methods such as tf-idf cosine-similarity, based on word overlap, mostly fail to produce good results in this case, since word overlap is little or non-ex...

In this paper, we investigate how the category of a Twitter user can be used to better predict and optimize the popularity of tweets. The contributions of this paper are threefold. First, we compare the influence of content features on the popularity of tweets for different user categories. Second, we present a regression model to predict the popul...

Even though the problem of event detection from social media has been well studied in recent years, few authors have looked at deriving structured representations for their detected events. We envision the use of social media for extracting large-scale structured event databases, which could in turn be used for answering complex (historical) querie...

Detecting events using social media such as Twitter has many useful applications in real-life situations. Many algorithms which all use different information sources—either textual, temporal, geographic or community features—have been developed to achieve this task. Semantic information is often added at the end of the event detection to classify e...

In this paper, we investigate how discovering the topic di-cussed in a tweet can be used to improve its sentiment classification. In particular, a classifier is introduced consisting of a topic-specific classifier, which is only trained on tweets of the same topic of the given tweet, and a generic classifier, which is trained on all the tweets in t...

Databases of places have become increasingly popular to identify places of a given type that are close to a user-specified location. As it is important for these systems to use an up-to-date database with a broad coverage, there is a need for techniques that are capable of expanding place databases in an automated way. In this paper the authors dis...

Various methods for automatically detecting events from so-cial media have been developed in recent years. However, little progress has been made towards extracting structured representations of such events, which severely limits the way in which the resulting event databases can be queried. As a first step to address this issue, we focus on the pr...

The task of the SNOW 2014 Data Challenge is to mine Twitter streams to provide jour-nalists a set of headlines and complementary information that summarize the most news-worthy topics for a number of given time in-tervals. We propose a 4-step approach to solve this. First, a classifier is trained to determine whether a Twitter user is likely to pos...

Databases of places have become increasingly popular to identify places of a given type that are close to a user-specified location. As it is important for these systems to use an up-to-date database with a broad coverage, there is a need for techniques that are capable of expanding place databases in an automated way. In this paper we discuss how...

Place recommender systems are increasingly being used to find places of a given type that are close to a user-specified location. As it is important for these systems to use an up-to-date database with a wide coverage, there is a need for techniques that are capable of expanding place databases in an automated way. On the other hand, social media a...

In this paper, we show how the large amount of geographically annotated data in social media can be used to complement existing place databases. After explaining our method, we illustrate how this approach can be used to discover new instances of a given semantic type, using London as a case study. In particular, for several place types, our method...

It can be difficult to plan a tourist trip to an unknown city. One has to search through several guidebooks to decide what can be interesting to visit. One way to facilitate the planning of a trip is by using a context-based system. This system only provides recommendations that are interesting in the context in which the user is situated. Therefor...

We propose a system that recommends tourist attractions based on the moment that the user visits a given city. We start from a large collection of georeferenced photos on Flickr, and use Mean Shift clustering to determine points of interest within a city. We then estimate the probability that a random user would visit a given place within a given t...