Inferring Document Readability by Integrating Eye Movement Features

Capturing user’s emotional state is an emerging way for implicit relevance feedback in information retrieval (IR). Recently, EEG-based emotion recognition has drawn increasing attention. However, a key challenge is effective learning of useful features from EEG signals. In this paper, we present our on-going work on using Deep Belief Network (DBN) to automatically extract high-level features from raw EEG signals. Our preliminary experiment on the DEAP dataset shows that the learned features perform comparably to the use of manually generated features for emotion recognition

Real-Time Classification of Twitter Trends

Social media users give rise to social trends as they share about common interests, which can be triggered by different reasons. In this work, we explore the types of triggers that spark trends on Twitter, introducing a typology with following four types: 'news', 'ongoing events', 'memes', and 'commemoratives'. While previous research has analyzed trending topics in a long term, we look at the earliest tweets that produce a trend, with the aim of categorizing trends early on. This would allow to provide a filtered subset of trends to end users. We analyze and experiment with a set of straightforward language-independent features based on the social spread of trends to categorize them into the introduced typology. Our method provides an efficient way to accurately categorize trending topics without need of external data, enabling news organizations to discover breaking news in real-time, or to quickly identify viral memes that might enrich marketing decisions, among others. The analysis of social features also reveals patterns associated with each type of trend, such as tweets about ongoing events being shorter as many were likely sent from mobile devices, or memes having more retweets originating from a few trend-setters.Comment: Pre-print of article accepted for publication in Journal of the American Society for Information Science and Technology copyright @ 2013 (American Society for Information Science and Technology

Towards evaluation of personalized and collaborative information retrieval

We propose to extend standard information retrieval (IR) ad-hoc test collection design to facilitate research on personalized and collaborative IR by gathering additional meta-information during the topic (query) development process. We propose a controlled query generation process with activity logging for each topic developer. The standard ad-hoc collection will thus be accompanied by a new set of thematically related topics and the associated log information, and has the potential to simulate a real-world search scenario to encourage retrieval systems to mine user information from the logs to improve IR effectiveness. The proposed methodology described in this paper will be applied in a pilot task which is scheduled to run in the FIRE 2011 evaluation campaign. The task aims at investigating the research question of whether personalized and collaborative IR retrieval experiments and evaluation can be pursued by enriching a standard ad-hoc collection with such meta-information

Overview of the personalized and collaborative information retrieval (PIR) track at FIRE-2011

The Personalized and collaborative Information Retrieval (PIR) track at FIRE 2011 was organized with an aim to extend standard information retrieval (IR) ad-hoc test collection design to facilitate research on personalized and collaborative IR by collecting additional meta-information during the topic (query) development process. A controlled query generation process through task-based activities with activity logging was used for each topic developer to construct the final list of topics. The standard ad-hoc collection is thus accompanied by a new set of thematically related topics and the associated log information. We believe this can better simulate a real-world search scenario and encourage mining user information from the logs to improve IR effectiveness. A set of 25 TREC formatted topics and the associated metadata of activity logs were released for the participants to use. In this paper we illustrate the data construction phase in detail and also outline two simple ways of using the additional information from the logs to improve retrieval effectiveness

Data Portraits and Intermediary Topics: Encouraging Exploration of Politically Diverse Profiles

In micro-blogging platforms, people connect and interact with others. However, due to cognitive biases, they tend to interact with like-minded people and read agreeable information only. Many efforts to make people connect with those who think differently have not worked well. In this paper, we hypothesize, first, that previous approaches have not worked because they have been direct -- they have tried to explicitly connect people with those having opposing views on sensitive issues. Second, that neither recommendation or presentation of information by themselves are enough to encourage behavioral change. We propose a platform that mixes a recommender algorithm and a visualization-based user interface to explore recommendations. It recommends politically diverse profiles in terms of distance of latent topics, and displays those recommendations in a visual representation of each user's personal content. We performed an "in the wild" evaluation of this platform, and found that people explored more recommendations when using a biased algorithm instead of ours. In line with our hypothesis, we also found that the mixture of our recommender algorithm and our user interface, allowed politically interested users to exhibit an unbiased exploration of the recommended profiles. Finally, our results contribute insights in two aspects: first, which individual differences are important when designing platforms aimed at behavioral change; and second, which algorithms and user interfaces should be mixed to help users avoid cognitive mechanisms that lead to biased behavior.Comment: 12 pages, 7 figures. To be presented at ACM Intelligent User Interfaces 201