A Framework for Real-time Semantic Social Media Analysis

This paper presents a framework for collecting and analysing large volume social media content. The real-time analytics framework comprises semantic annotation, Linked Open Data, semantic search, and dynamic result aggregation components. In addition, exploratory search and sense-making are supported through information visualisation interfaces, such as co-occurrence matrices, term clouds, treemaps, and choropleths. There is also an interactive semantic search interface (Prospector), where users can save, refine, and analyse the results of semantic search queries over time. Practical use of the framework is exemplified through three case studies: a general scenario analysing tweets from UK politicians and the public's response to them in the run up to the 2015 UK general election, an investigation of attitudes towards climate change expressed by these politicians and the public, via their engagement with environmental topics, and an analysis of public tweets leading up to the UK's referendum on leaving the EU (Brexit) in 2016. The paper also presents a brief evaluation and discussion of some of the key text analysis components, which are specifically adapted to the domain and task, and demonstrate scalability and efficiency of our toolkit in the case studies

Leveraging video annotations in video-based e-learning

The e-learning community has been producing and using video content for a long time, and in the last years, the advent of MOOCs greatly relied on video recordings of teacher courses. Video annotations are information pieces that can be anchored in the temporality of the video so as to sustain various processes ranging from active reading to rich media editing. In this position paper we study how video annotations can be used in an e-learning context - especially MOOCs - from the triple point of view of pedagogical processes, current technical platforms functionalities, and current challenges. Our analysis is that there is still plenty of room for leveraging video annotations in MOOCs beyond simple active reading, namely live annotation, performance annotation and annotation for assignment; and that new developments are needed to accompany this evolution.Comment: 7th International Conference on Computer Supported Education (CSEDU), Barcelone : Spain (2014

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770

Web archives: the future

T his report is structured first, to engage in some speculative thought about the possible futures of the web as an exercise in prom pting us to think about what we need to do now in order to make sure that we can reliably and fruitfully use archives of the w eb in the future. Next, we turn to considering the methods and tools being used to research the live web, as a pointer to the types of things that can be developed to help unde rstand the archived web. Then , we turn to a series of topics and questions that researchers want or may want to address using the archived web. In this final section, we i dentify some of the challenges individuals, organizations, and international bodies can target to increase our ability to explore these topi cs and answer these quest ions. We end the report with some conclusions based on what we have learned from this exercise