Instructor's notes - before lecture
Search, reputation/trust networks, and PageRank
Scholarly peer review: earliest practice 1665 in Royal Society; developed in 18th and 19th centuries; commonplace starting in 20th
Submitting scholarly papers:
Ideal goal: Want information to be as freely as possible
Journals
Conferences
Preprints
“Anyone” can post their scientific paper (even more fast paced); however, these papers are not peer reviewed.
Peer Review Process:
Single Blind
Double Blind
Conflict of Interest
Evaluating scientists: But how to identify/measure/rank scientific achievement, excellence?
Number of “published” papers. Limitations?
Number of citations. Advantages and limitations?
h-index by Jorge E. Hirsch in 2005. Advantages and limitations?
Search engines: AltaVista search engine (1995), Google takeover, Yahoo purchase (2003) and shutdown (2013)
Concept: pages “vote on” each other
But how much “voting power” does each page wield?
What’s wrong with 1 page 1 vote?
Page’s voting power weighted by reputation
But reputation depends on voting power … circular!
Solution: iterate with decay or resistance factor
Illustration: random surfer (Markov chain) model
PageRank as optimization-based linear programming
Twitter as an emergent influence network
Influence metrics: Indegree, Retweet, Mention influence
Briefly-mentioned alternate PageRank-like metric
“We limited the study duration because popular keywords were typically hijacked by spammers after certain time”
“most influential users hold significant influence over a variety of topics.”
“it is substantially more effective to target the top influentials than to employ a massive number of non-popular users in order to kick start a viral campaign”
“influence is not gained spontaneously or accidentally, but through concerted effort”
Post-lecture blackboard snapshot 2019: