7:00 – 5:00 VTX

• Continuing to code Creating Friction: Infrastructuring Civic Engagement in Everyday Life. Finished. Nice to see that this is a frictional design.
• Need to build new CorpusManager Artifact
• Sprint review. Don’t forget to mention that I also wrote up a CSE blacklist manager

7:00 – 3:30 VTX

• Thinking about Predictwise’s ostensible failure to get the Brexit right. (Brexit – PredictWise-6.24.2016). The comments are also hugely interesting. Added it to the corpus and coded it lightly. Don’t want to lose it. From the Predictwise blog:
  • Second, market did not pick up on enough idiosyncratic data in the field. Maybe this is because traders do not have the pulse of working masses? Possible. But, I go back to the first point as probably more important. This morning I have a new line of research that I am obsessed with: political impact on financial markets is under-explored and fascinating. I suspect that we underestimated the effect that the volatility of the underlying currency had on the prediction and financial markets.
• Back to creating friction and refining that part of the contribution. I need to find that article that does fact-checking by looking at Wikipedia hops. Found it: Computational Fact Checking from Knowledge Networks
• Writing report. Done!

7:00 – 4:00 VTX

• Back to reading. Working through Creating Friction: Infrastructuring Civic Engagement in Everyday Life
• Working through the data and breaking out sections. Need to make charts. Done! Report next.
• Started report

6:45 – 4:45 VTX

• Running analytics on the CSCW corpora
• My codebase at home was out of data, and I was having my missing bouncycastle jar file issue, so I updated the development folders. I also started updating my IntelliJ, which has taken 10 minutes so far…
• First pass of running the CSCW data through the tool.
  There are three categories:
  • CSCW17 – these are the submitted papers
  • MostCited – These are (generally) the most cited paper by the author where they are first or last author. It took me a while to start doing this, so the set isn’t consistent.
  • MostRecent – These are the most recent papers that I could get copies of. Same constraints and caveats as above.

  I also deleted the term ‘participants’, as it overwhelmed the rest of the relationships and is a pretty standard methods element that I don’t think contributes to the story the data tell.

  Here’s the top ten items, ranked by influence of terms inside of the top 52 items in the LSI ranking. It’s kind of interesting…

  CSCW2017	Most Cited	Most Recent		Most Cited	Most Recent
  older	social	media		Sean P. Goggins.pdf	Donald McMillan.pdf
  ageism	student	privacy		Chinmay Kulkarni.pdf	Mark Rouncefield.pdf
  adult	photo	twitter		Airi Lampinen.pdf	Sarah Vieweg.pdf
  blogger	awareness	behavior		Cliff Lampe.pdf	Jeffrey T. Hancock.pdf
  ageist	object	device		Anne Marie Piper.pdf	David Randall.pdf
  platform	class	interview		Frank Bentley.pdf	Cliff Lampe.pdf
  workplace	facebook	notification		Mor Naaman.pdf	Sean P. Goggins.pdf
  woman	friend	deception		Morgan G. Ames.pdf	Airi Lampinen.pdf
  gender	flickr	phone		Gabriela Avram.pdf	Wayne Lutters.pdf
  snapchat	software	facebook		Lior Zalmanson.pdf	Vivek K. Singh.pdf

• Finished rating! 530 pages. Now I need to get the outputs to Excel. I think the view_ratings should be enough…?
• I don’t have just names alone, but I’m going to assume that the initial set of queries (‘board actions’, ‘criminal’, ‘malpractice’ and ‘sanctions’) may modestly improve the search. So with a proxy for the current system, with my small data set, I have the following results:
  • Hits or near misses – 46 pages or 16.7% of the total pages evaluated
  • Misses – 230 or 83.3% of the total pages evaluated

  With the new CSE configuration (exactTerms=<name permutation>, query=<full state name>, orTerms=<TF-IDF string>, we get much better results:

  • Hits or near misses – 252 pages or 78% of the total pages evaluated
  • Misses – 71 or 22% of the total pages evaluated

  So it looks like we can expect something on the order of a 450% improvement in results.

• Good presentation on document similarity

8:00 – 3:00 VTX

• Finishing up the chairs. And then I have some more coding to do on the new papers.
• Anatomy of the Unsought Finding. Serendipity: Origin, history, domains, traditions, appearances, patterns and programmability (stored here)
• Supporting serendipity: Using ambient intelligence to augment user exploration for data mining and web browsing (stored here)
• Starting to create new json file of practitioners

  {
  	"first_name": "Nader",
  	"last_name": "Golian",
  	"pp_state": "New York",
  	"gender": "male",
  }, {
  	"first_name": "Ata",
  	"middle_name": "Ollah",
  	"last_name": "Mehrtash",
  	"pp_state": "New York",
  	"gender": "male",
  }, {
  	"first_name": "Souheil",
  	"last_name": "Saba",
  	"pp_state": "New Jersey",
  	"gender": "male",
  }, {
  	"first_name": "Kamal",
  	"last_name": "Patel",
  	"pp_state": "Illinois",
  	"gender": "male",
  }

• Built new queries

  sst = new SmartSearchTerm("schedule guideline license substance board sentence increase other prescription commission", null, null);
          termList.add(sst);
          sst = new SmartSearchTerm("schedule criminal license sentence prescription doctor defendant board practice research county", null, null);
          termList.add(sst);
          sst = new SmartSearchTerm("license board practice patient physician order health practitioner medicine medical", null, null);
          termList.add(sst);
          sst = new SmartSearchTerm("physician license professional return number effective", null, null);
          termList.add(sst);
          sst = new SmartSearchTerm("respondent consent committee probation agreement pursuant", null, null);
          termList.add(sst);

• Hmm. Getting good pages that are like this, but no matches on the names yet. Tempted to have an option that rejects pages that don’t have an entity with the right name in it and let it cook.
• Helped write up ML text for SOW.

7:30 – 5:30 VTX

• Still working through chairs. It’s been a really good exercise. I’m finding some very good stuff.
• Worked through equations that would help find paragons, oscillators, cyphers and criminals.
• Committed all the changes that I forgot about yesterday.
• “name” <state> license board practice patient physician order health practitioner medicine medical seems to work well.
• Building auto-trimmed matrix based on 50% of rank value. Done!

7:00 – 3:00 VTX

• Working on finishing the papers of the CSCW chairs
• Built a new version of RatingApp and sent over to Andy to deploy.
• More rating. Once done, run through the domains and see what comes up.
  • Finished!
  • Ok, I have a mix of html, pdf and msword docs.
    • Change corpusManager so the config file can handle multiple types
    • Convert the docs to pdf. Done
    • Parsing. Ran into a java.lang.NoClassDefFoundError: org/bouncycastle/jce/provider/BouncyCastleProvider error. Added the org.bouncycastle:bcprov-jdk16:1.46 and now all is fine…?
• Need to trap a reset connection and resubmit. Done

6:30 – 4:30 Writing

• Wondering if I should add a section on trust and credibility
• Huh – just saw this on Google image search. You get a bar of context words that allow for drilling down into a result
• Reworked a lot of the paper since the whole anonymous part has been shelved
• Started on the hypotheses and research questions in the plan section