Phil 7.14.17

7:00 – 8:00 Research

  • Wrote up some notes from meeting with Aaron
  • More C&C
    • The conflicts or differences between members of the group are normally resolved by convergence towards an extreme position. Yet, depending on whether the discussion is public or private, or the dialogue exterior or interior, the convergence will be more, or less, close to that position. In other words, discussion, in its current meaning, depending on whether the individuals involved are active or passive, determines the extent to which the decision will become polarized. [p 81]
      • The book doesn’t cover CMC discussion, but the following two papers appear to perform similar experiments to the Moscovici work
    • Group and computer-mediated discussion effects in risk decision making
      • Managers individually and in 3-person groups made multiattribute risk choices (two investment alternatives, each with multiple outcomes). Two group decisions were reached during face-to-face discussion, and two were reached during (real-time) computer-mediated discussion. In comparison with prediscussion individual preferences, groups’ multiattribute risk choices and attitudes after face-to-face discussion were risk averse for gains and risk seeking for losses, a tendency predicted by prospect theory and consistent with choice shift and other group extremitization research. By contrast, group decisions during computer-mediated discussion did not shift in the direction of prospect theory predictions. The results are consistent with persuasive-arguments theory, in that computer-mediated discussion contained less argumentation than face-to-face discussion. Social decision schemes were used to evaluate alternative assumptions about the group process. A “(prospect-theory) norm-wins” decision scheme described group choice well in the face-to-face discussion condition, but not in the computer-mediated discussion condition. Another decision scheme, first-advocate wins, which described choices well in both face-to-face and computer-mediated discussions, was explored in a discussion of the role of communication in group decision making.
    • Group Polarization and Computer-Mediated Communication
      • Group polarization is the tendency of people to become more extreme in their thinking following group discussion. It may be beneficial to some, but detrimental to other, organizational decisions. This study examines how computer-mediated communication (CMC) may be associated with group polarization. Two laboratory experiments were carried out. The first experiment, conducted in an identified setting, demonstrated that removal of verbal cues might not have reduced social presence sufficiently to impact group polarization, but removal of visual cues might have reduced social presence sufficiently to raise group polarization. Besides confirming the results of the first experiment, the second experiment showed that the provision of anonymity might also have reduced social presence sufficiently to raise group polarization. Analyses of process data from both experiments indicated that the reduction in social presence might have increased group polarization by causing people to generate more novel arguments and engage in more one-upmanship behavior. Collectively, process and outcome data from both experiments reveal how group polarization might be affected by level of social presence. Specifically, group discussion carried out in an unsupported setting or an identified face-to-face CMC setting tends to result in weaker group polarization. Conversely, group discussion conducted in an anonymous face-to-face CMC setting or a dispersed CMC setting (with or without anonymity) tends to lead to stronger group polarization. Implications of these results for further research and practice are provided

8:30 – BRI

  • Added publishers and subscribers to NLP, Gecoder, and Crawl. This is how I think it should work:
  • Publisher (NLP):
        <publisher id="masterdata-nlp" name="masterdata-nlp" default="true">
  • Subscriber (Geocoder, single channel):
        <subscriber name="subscriber-masterdata-nlp">
  • Subscriber (MDS, two channels):
        <subscriber name="subscriber-masterdata">
        <subscriber name="subscriber-masterdata-nlp">
  • That seems to be working fine. Now I need to parse out the LOCATION facts. Here’s the loop that gets the document ID and all the locations:
    String idString = event.getId();
    List<Fact> fList = event.getFacts();
    for(Fact f : fList){
        Result r = (Result)(f.getValue());
        Map<String, List<NamedEntity>> neMap = r.getNamedEntities();
        if(neMap.containsKey("LOCATION")) {
            List<NamedEntity> locList = neMap.get("LOCATION");
  • Now I need to assemble a message with document ID and all the locations that have lat/longs. I think the way to do this is to build an event that contains a set of geo-coordinate facts.
    • From Matt:
      • you send an Event with the same metadata fields (domain, id, tag, etc…) as the incoming event. Your event has a facts field. In that field you’ll add the Location entity
      • yes. Fact.value is the object and Fact.index is its index
  • Started to build the event in AMQPMessageListener.onMessage():
    Event gcDocEvent = new Event(event.getId(), event.getType(), event.getDomain(), event.getTag(),
            System.currentTimeMillis(), "MessagingService", "event containing geolocation facts",geoFactList.toString(),geoFactList,

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