Phil 3.14.15(+1)

7:00 – 6:00 VTX

  • Happy PI day!
  • At CHIIR 16 – User Modelling Tutorial yesterday, presentations today.
  • Continuing A Survey on Assessment and Ranking Methodologies for User-Generated Content on the Web.
    • Found Beyond the filter bubble: interactive effects of perceived threat and topic involvement on selective exposure to information. It shows that confirmation bias will affect users when presented with differing accounts on the same page. This seems to give strength to the idea of trustworthy/distrustworthy inference networks that can trace to authoritative material in a positive or negative way.
    • Whooly.net – MSR – A mobile web app that makes latent, hyperlocal neighborhood communities more visible, to help neighbors connect. This project leverages intelligent filters and event detection algorithms to help users find relevant, spiking topics about what is happening here and now.
    • Finding and assessing social media information sources in the context of journalism From the abstract: [It is a]…challenge to finding interesting and trustworthy sources in the din of the stream. In this paper we develop and investigate new methods for filtering and assessing the verity of sources found through social media by journalists. We take a human centered design approach to developing a system, SRSR (“Seriously Rapid Source Review”), informed by journalistic practices and knowledge of information production in events.
      • They build classifiers to discriminate sources and value!
    • Reflect – Reflect makes a simple change to comment boards. Next to every comment, Reflect invites readers to succinctly restate the commenter’s points. These restatements are shown in a bulleted list.
    • Microsoft Academic Knowledge API looks useful for tying back to experts maybe?
  • Keynote
    • Mark Ackerman
    • Information reuse and context
    • Computer supported cooperative work –
    • You have build things before you understand the world.
    • Understand the world before you can build successful things
    • CSCW is now social computing???
    • CSCW has a pint of view
      • Use of collaborative contributions – i.e Google Docs.
      • Social navigation – ant trails This is diferent from wayfinding.
      • Iterative refinement
      • Reward systems dictate how people interact with social systems
      • Finding the background of the information for trust issues. With Lutters – aircraft engineers would throw out calculations of people they don’t know
      • The ‘cold start problem’ for recommender systems
      • Expert locators good programmers hang out in stackoverflow
      • Ifrastructuring == habituation?
      • The postmodern turn
      • FIT – How do you measure the information distance?
      • People placing themselves on their connectivist structure?? Can we know that this is true?
      • Activity traces
        • Mental Illness severity in online Pro-Eating disorder – CSCW 16
        • The livhoods project ICSWM 2012
        • Generalizing activity instantiations based on context
  •  Papers
    • Active and passive Utility of Search Interface Features
      • Hugo Huudeman
      • Interactive Search User INterfaces proven in micro-studies.
      • SearchAssist – it would be interesting to use a ranker as the backend
    • The Forgotten Needle in My Collections: Task-Aware Ranking of Documents in Semantic Information Space
      • Ranking of items that are being used in a task recently.
      • PIMO Personal Information Model – Semantic layer over workspace
      • Users create the annotations
      • Look at task aware ranking. Connections, Beagle+++, SURPA, T-Fresh
      • Uses machine learning to determine the weights.
      • http://pimo.opendfki.de/
    • Behaviour Mining for Automatic Task-Keeping and Visualisations for Task-Refinding
      • Using the interaction between documents as a way to connect tasks as a network Didn’t work at first so they added types of interactions and weighted those
      • Labeled weak nodes rather than discarding- Polynomial decay function for flags???
      • Are FF plugins easier to write?
    • Collaborative Information Retrieval
      • Rerank based on search context (chat, click through, previous queries from other team members)
      • We apply the following procedure to re-rank relevant documents for each to-be-supported query. For each candidate document, we estimate its document language model using Dirchilet smoothing [31], where we set the smoothing parameter µ = 100. The similarity between each candidate document and the contextual model is measured by the KL divergence between their estimated language models [30]. The matching between a candidate document and the query is determined by Google rank position of the given candidate document. This is because our experimental system uses Google results as the default. Instead of using linear interpolation proposed by Shen et al. [23], we employ LambaMART in RankLib2 to build a pairwise learning-to-rank approach for combining different features.
    • (The Lack of) Privacy Concerns with Sharing Web Activity at Work and the Implications for Collaborative Search.
      • Loosely coupled collaboration. Tools must have minimal effort to use.
      • Coupling – the amount of work that people can do individualy before they have to interact explicitly. It’s opportunistic.
      • Webwear???
    • An AID for Avoiding Inadvertent Disclosure: Supporting Interactive Review for Privilege in E-Discovery
      • Automated annotators – this is exactly what we need for flags. Don’t see exactly how the annotator is built other than ML.
        • Used Enron Corpus
        • Entity Linking – people and companies
        • Type
        • Propensity that the person is involved with the confidential communication
        • Unigram language models
          • Privileged communication
          • Non-privileged communication
          • Compared the entropy for the top-n words and used that.
      • Relevance Review
      • Privilege review – by senior lawyers because of the error cost.
      • Lawyers have no System Trust? Why? What do they trust?
        • They trust stories with provenance, not a number generated by machine learning system
      • Increase in recall, decrease in precision? Huh.
      • People highlighting was useful,
      • Term highlighting was useful.
      • Preference for sentence and paragraphs.
  • Posters
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