Video summaries or highlights are a compelling alternative for exploring and contextualizing unprecedented amounts of video material. However, the summarization process is commonly automatic, non-transparent and potentially biased towards particular aspects depicted in the original video. Therefore, our aim is to help users like archivists or collection managers to quickly understand which summaries are the most representative for an original video. In this paper, we present empirical results on the utility of different types of visual explanations to achieve transparency for end users on how representative video summaries are, with respect to the original video. We consider four types of video summary explanations, which use in different ways the concepts extracted from the original video subtitles and the video stream, and their prominence. The explanations are generated to meet target user preferences and express different dimensions of transparency: concept prominence, semantic coverage, distance and quantity of coverage. In two user studies we evaluate the utility of the visual explanations for achieving transparency for end users. Our results show that explanations representing all of the dimensions have the highest utility for transparency, and consequently, for understanding the representativeness of video summaries.

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Event detection is still a difficult task due to the complexity and the ambiguity of such entities. On the one hand, we observe a low inter-annotator agreement among experts when annotating events, disregarding the multitude of existing annotation guidelines and their numerous revisions. On the other hand, event extraction systems have a lower measured performance in terms of F1-score compared to other types of entities such as people or locations. In this paper we study the consistency and completeness of expert-annotated datasets for events and time expressions. We propose a data-agnostic validation methodology of such datasets in terms of consistency and completeness. Furthermore, we combine the power of crowds and machines to correct and extend expert-annotated datasets of events. We show the benefit of using crowd-annotated events to train and evaluate a state-of-the-art event extraction system. Our results show that the crowd-annotated events increase the performance of the system by at least 5.3%.

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In this project we explore the presence of ambiguity in textual and visual media and its influence on accurately understanding and capturing bias in news. We study this topic in the context of supporting media scholars and social scientists in their media analysis. Our focus lies on racial and gender bias as well as framing and the comparison of their manifestation across modalities, cultures and languages. In this paper we lay out a human in the loop approach to investigate the role of ambiguity in detection and interpretation of bias.@en

Bias is inevitable and inherent in any form of communication. News often appear biased to citizens with dierent political orientations, and understood dierently by news media scholars and the broader public. In this paper we advocate the need for accurate methods for bias identication in video news item, to enable rich analytics capabilities in order to assist humanities media scholars and social political scientists. We propose to analyze biases that are typical in video news (including framing, gender and racial biases) by means of a human-in-the-loop approach that combines text and image analysis with human computation techniques.@en

The results of our exploratory study provide new insights to crowdsourcing knowledge intensive tasks. We designed and performed an annotation task on a print collection of the Rijksmuseum Amsterdam, involving experts and crowd workers in the domain-specific description of depicted ow- ers. We created a testbed to collect annotations from ower experts and crowd workers and analyzed these in regard to user agreement. The findings show promising results, demonstrating how, for given categories, nichesourcing can provide useful annotations by connecting crowdsourcing to domain expertise.@en

In this work we present an in-depth analysis of the user behaviors on different Social Sharing systems. We consider three popular platforms, Flickr, Delicious and StumbleUpon, and, by combining techniques from social network analysis with techniques from semantic analysis, we characterize the tagging behavior as well as the tendency to create friendship relationships of the users of these platforms. The aim of our investigation is to see if (and how) the features and goals of a given Social Sharing system reflect on the behavior of its users and, moreover, if there exists a correlation between the social and tagging behavior of the users. We report our findings in terms of the characteristics of user profiles according to three different dimensions: (i) intensity of user activities, (ii) tag-based characteristics of user profiles, and (iii) semantic characteristics of user profiles.@en

In this paper we try to identify requirements, opportunities and problems in home media centers and we propose an approach to address them by describing an intelligent home media environment. The major issues investigated are coping with the information overflow in the current provision of TV programs and channels and the need for personalization to specific users by adapting to their age, interests, language abilities, and various context characteristics. The research presented in this paper follows from a collaboration between Eindhoven University of Technology, the Philips Applied Technologies group and Stoneroos Interactive Television. The work has been partially carried out within the ITEA-funded European project Passepartout, which also includes partners like Thomson, INRIA and ETRI. In the following chapter we describe the motivation and research problem in relation to related work, followed by an illustrative use case scenario. Afterwards, we explain our data model which starts with explaining the TV-Anytime structure and its enrichments with semantic knowledge from various ontologies and vocabularies. The data model description then serves as the background for understanding our proposed system architecture SenSee. Afterwards we go deeper into the user modeling part and explain how our personalization approach works. The latter elaborates on a design targeting interoperability and on semantic techniques for enabling intelligent context-aware personalization. In the implementation chapter we describe some practical issues as well as our main interface showcase, iFanzy. Future work and conclusions end this chapter.@en