Librarian as Consultant #InternetLibrarian

Paul Barrows, Federal Resrve Bank of San Francisco

Slides: http://conferences.infotoday.com/documents/293/B201_Barrows.pptx

Pyramid: transacter -> problem solver -> consultant -> trusted advisor

Problem solver: more involved than a simple reference question.

Consultant: someone keeps coming back with a subject.

Trusted advisor: someonce comes to you when they’re really lost and they trust you to get them started.

The pyramid narrows at the top, because you can’t be a trusted advisor to very many people.

New MLIS: “My boss is very smart, she must have had a reason for hiring me.”

Perpetual curiosity, ask lots of questions, be a team member, speak up, change leader.

Fewer ready reference questions, needed to transform services.

Positioning the librarians as advisors, partners, consultants.

Transforming staff: re-training, tough decisions, playing to strengths, honing existing skills, emerging skills.

Think about your mission as furthering the success of your parent organization, rather than self-preservation.

Find about your organization’s mission, your management’s priorities. Commitment to customers’ goals (they want to look good and do well), future of librarianship, and your own professional development.

Librarians are already generally trusted, but people don’t know what we do. People think they’re bothering you. Soft skills, like empathy and emotional intelligence (know the difference between panic and curiosity!). Get in on the ground floor of projects. Iterative approach improves the product — and the relationship (don’t give all at once, make sure you’re on the right track). Regular brief meetings during larger projects. Over-deliver and maybe offer more. Learn and ask about something personal.

Build awareness of services, esp. online subscriptions. Have a strong web site, self service but can come to us for deeper levels of service. Pop-up tables.

Catch key clients: Meet with new executives and give them targeted recommendations. Presentations to departments and divisions. People are happy to find out you can help them do their job better. Check-in regularly: “What are you working on now?”

Trust your gut, even in the deep end of the pool.
Periodic SWOT analyses (organizational and personal)
Are all your strengths being used?
Go on field trips to where people are
Polish how you talk about the library, yourself, and your colleagues
Influence without authority (peers, executives)
Suggest the wacky if you can explain how it serves the mission
(Something for everyone is part of their goals.)

Secret Sauce of Search #InternetLibrarian

Marydee Ojala, editor of Online Searcher

Presentation slides

Our work starts where Google ends.
Anybody can Google, but not everybody searches well.
Search does not equal Google.

Other search engines:
Bing
Yandex
Country versions of Google
Duck Duck Go
Peekier (another one devoted to privacy)
Wolfram alpha
Million Short
Similar Sites
Wayback Machine (archive.org)

Advanced search:
Special syntax, prefixes (site:, filetype:, inurl:) (Bing and Yandex have others)
Phrase searching
Word order, synonyms, language

Non-textual search:
Images, audio, video, datasets
Specific databases at Google, Bing, Yandex
* YouTube, vimeo
* Flickr, Morguefile
* Zanran, Datahub

Specialized search engines:

Topic specific:
* Biznar
* Millie.northernlight.com (market research)
* PubMed

Academic search engines:
* Google Scholar
* Microsoft Academic
* BASE
* Semantic Scholar
* MetaBus

Academic document delivery:
* ResearchGate
* Academia.edu
* Sci-Hub (a pirate site)

Subscription search engines

Skepticism:
* combating fakes and frauds
* look for additional documentation
* what is the source?
* Not every issue has two sides
* Retractionwatch.com
* Not just fake news
* AllSides – News from left, center, and right political viewpoints

Being ethical:
* Doing the right thing
* Teaching people about copyright
* General Data Protection Regulation (GDPR) – protecting people’s personal info (European regulation, coming in 2018)

Going under the hood:
Knowing how search works
Search technologies
Personalization
Machine learning, AI
Semantic search, contextual
Moving away from keywords
Why did I get this result?
Why are these ads following me around?

Google decides to disregard some of your search terms and puts a note telling you what’s missing.

Secret sauce:
Knowledge about search
Be willing to experiment
Think non-linearly, accept imprecesion
Constant updating of our brains
Power of the info pro

Updated to add link to slides.

Interoperable Platforms & Linked Data #InternetLibrarian @rtennant

Roy Tennant, OCLC

Catalog data is:
* A collection of text strings
* Taken from the piece itself
* Sometimes enhanced with extra information
* Additional statements (like subject headings)
* Punctuation, which can be inconsistent
* Mostly uncontrolled and loosely connected to anything else
* Designed for description rather than discovery (describing differences, rather than similarity)

Problems:
* Identification: titles (the “Hamlet” problem)
* Identification: names (the Wang, Lee problem; John Rock the scientist or the abolitionist?)
* Data quality problems (e.g., inconsistencies in format indicator [245 h])

First, define all the things (entities)
Relationships:
Author: wrote a Title (Work): about a Subject

Authors can be identified by URIs in wikidata or VIAF.
Works can be identified in WorldCat Works.
Subjects can be identified in LCSH authority file.

Could have catalogs that show works, then all the different manifestations of them.

OCLC experiments:
War Between the States page.
Fiction Finder (different editions of fictional works) – helps solve the title problem.
Can link together translations of a given work.
Bringing authority control to a web environment (VIAFs in Wikipedia)

Make your MARC records more consistent, standard; use URIs — to make them more linked data-ready. (One recommendation: minimize use of 500 fields.)

Report: “Library Linked Data in the Cloud.”

Question about using 5xx fields to make things more searchable.
Structured fields work better.

Practitioner’s Panel: Search Tips and Millenial Searcher Secrets #InternetLibrarian @aainfopro

Amy Affelt
Slides at http://conferences.infotoday.com/documents/293/A103_Affelt.pptx

Fake news. Shared knowing it’s fake, shared without reading page linked to.

Facebook said it was crazy to think it had anything to do with influencing U.S. election. Then said maybe it did. Has some remedies, but they are much too slow.

Google has automatic links to face-checking sites. But are those correct? Or consistent?

CNN pointed to an IFLA document for spotting fake news. https://www.ifla.org/node/11175

Look at domain names. Read “about us” pages.

But respectable sites aren’t always correct. CBS News said Tom Petty died before it was true. Look for supporting sources. One source may not be enough.

Read about the author.

Check the date. Is it old? Is it April 1?

Check your bias. Does the site have a political slant?

Watch out for provocative headlines.

Watch for the “promoted” label.

Fake health news: Check source, plausibility. “The secret doctors won’t tell you.” Look for peer-reviewed article, human trials. HealthNewsReview.org debunks fake health news. Google it for other reactions.

Melissa Zimdars’ list of dubious news sources.

How to Lie with Statistics. Book from 1950s showing misleading graphics.

Fake videos coming next.

Tom Reamy:

Presentation:
http://conferences.infotoday.com/documents/293/A103_Reamy.ppt

Text analytics, taxonomy, training.

Book, “Deep Text”

There is no single definition of “fake news.”

Fake people, automatic bots (e.g., on Twitter)

Google: can be manipulated so top stories on a topic are fake news.

Two drivers: making money and manipulating people.

People make up news to get clicks, which get ad revenue.

Debunking: a fraction of the people who saw the original post will see the debunking one. No money in it. Effects linger.

Can block ads on a site, but that does nothing for politically motivated fake news.

Technical tools to finding misleading domain names, etc.

Automated systems aren’t smart enough and can be manipulated.

Case study: hybrid analysis of news
Inxight Smart Discovery (now SAP), multiple taxonomies.

Pulled in thousands of news stories, used rules to categorize them.

Faster than human review, smarter than automatic solutions.

Weight if word occurs in title, etc. There is no such thing as unstructured text.

Pronoun analysis: “The Secret Life of Pronouns.” All the words that search engines throw out. Analyzed e-mail and could often tell age, gender, power status of writer. Lying and fraud detection: fewer and shorter words, more positive emotion words. C. 76% accuracy.

Deep text solutions:
1. Database of known sites.
2. Deep learning of text/linguistic patterns
3. Flexible categorization rules

Fake news is a serious problem: undermines democracy, communication, civilization.

Multiple factors: multiple solutions

Hybrid human-machine solutions are getting best results. Ultimate solution is better education.

Books:

“Weaponized lies”
“Post-truth”
“Don’t Think of an Elephant”

Libraries can keep pushing information about how to spot fake news.  Colleges and universities can teach critical thinking.

Take a minute to check it out.  You don’t want to be “first but wrong.”