Web Science/Part3: Behavioral models/MoocIndex

--MoocIndex for MOOC @ Web Science/Part3: Behavioral models

=lesson|Processes on the Web Graph: The Example of Modelling the Dynamics of Meme Spreading=
 * video=File:Web science mooc flipped class room spreading memes.webm
 * learningGoals=
 * 1) understand that network structure determines processes, such as individual communication
 * 2) understand that the network structure determines global communication results
 * 3) understand how to model micro-behavior of individuals at large
 * 4) understand how to related dying and exploding memes to the same model
 * 5) understand the difference of perspectives between micro interactions and macro effects
 * 6) Know http://www.nature.com/srep/2012/120329/srep00335/full/srep00335.html
 * 7) Know about effective distance http://link.springer.com/article/10.1140%2Fepjb%2Fe2011-20208-9 http://rocs.hu-berlin.de/D3/ebola/


 * furtherReading=
 * 1) understanding http://www.nature.com/srep/2012/120329/srep00335/full/srep00335.html

unit|Overview of the phenomenon

 * video=File:Under_construction_icon-blue.svg
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unit|Experimental Setup and Methodology of the Memes spreading Model

 * video=File:Under_construction_icon-blue.svg
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unit|Mathematical foundations of the Memes spreading Model

 * video=File:Under_construction_icon-blue.svg
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unit|Results of the Memes spreading Model

 * video=File:Under_construction_icon-blue.svg
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unit|Summary, Further readings, Homework
=lesson|More Micro Behavior and Macro Effect I: Collective Intelligence=
 * video=File:Under_construction_icon-blue.svg
 * learningGoals=
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 * learningGoals=
 * 1) Know about examples of collective intelligence in the Web (and beyond)
 * 2) Understand that clever aggregation of randomly noisy sensor output leads to high quality measurements
 * 3) Understand that independence of judgement is key to high quality collective decision making
 * 4) Relate this to law of large numbers
 * 5) Understand the idea of a social sensor: Model people output as sensor output
 * 6) Understand the idea of recursive aggregation of reputation
 * 7) Understand limitations of when collective intelligence cannot be derived

unit|IDF as Simple Form of Collective Intelligence

 * learningGoals=
 * 1) IDF aggregates common usage of vocabulary
 * 2) knowledge about common usage of vocabulary models term specificity

unit|In-degree as Form of Collective Intelligence

 * learningGoals=
 * 1) IDF aggregates common usage of vocabulary
 * 2) knowledge about common usage of vocabulary models term specificity

unit|Random surfer Model

 * video=File:Under_construction_icon-blue.svg
 * learningGoals=
 * a
 * b
 * c

unit|Page rank of Graph/Matrix

 * learningGoals=
 * 1) Eigenvalues are an important metric to describe graphs.
 * 2) Decomposing large matrices is computationally heavy.
 * 3) relation to the random surfer model

=lesson|More Micro Behavior and Macro Effect II: Herding=
 * furtherReading=
 * 1) understand: https://www.princeton.edu/~mjs3/salganik_dodds_watts06_full.pdf
 * 2) know some basics about: Herd behavior from the field of psychology
 * Absolute_difference
 * Randomized_experiment
 * Randomized_controlled_trial
 * Randomization
 * Web-based_experiments
 * Conditional_independence
 * Independence_(probability_theory)
 * Dependent_and_independent_variables
 * Herd_behavior
 * Systematic_error


 * learningGoals=
 * 1) Know and understand the notion of herding and swarms
 * 2) Know and understand that local information and positive feedback cycles may destroy collective intelligence (e.g. Groupthink, shitstorms, Klaas' tagging experiments, stock exchange.....)
 * 3) Know about examples of herding, such as preferential attachment, music experiment,...
 * 4) Understand how herding can be measured in an experiment
 * 5) How to conduct a web based experiment with a control group?
 * 6) Get to know one specific experiment and methodology that demonstrated herd behavior on the web.
 * 7) Understand how to empirically design an experiment that can demonstrate herd behavior.
 * 8) Discussing systematic errors in experiments
 * 9) Understand that it is non trivial to verify phenomenons of herding.
 * 10) understand: https://www.princeton.edu/~mjs3/salganik_dodds_watts06_full.pdf
 * video=File:Web science mooc recommendations.webm
 * video=File:Web science mooc recommendations.webm

unit|Research question of herd behavior, inequality and unpredictability of cultural markets

 * video=File:Under_construction_icon-blue.svg
 * learningGoals=
 * 1) What are the research questions that will be answered in the experiment
 * 2) understand that a good study starts with a research question
 * 3) The concept of falsifiability.
 * 4) Good research questions often start with an obervation (e.g.: experts have frequently failed to predict the success of musicians)
 * furtherReading=
 * 1) Falsifiability
 * 2) Design_of_experiments
 * 3) Research_question
 * 4) Experiment

unit|Experimental Setup and data collection process

 * learningGoals=
 * 1) difference between the dependent and independent group
 * 2) what is scientific control
 * 3) Repetition of the experiment (Why do the authors have 8 worlds?) to to conduct a randomized experiment.
 * furtherReading=
 * 1) Dependent_and_independent_variables
 * 2) Independence_(probability_theory)
 * 3) Treatment_and_control_groups
 * 4) Randomized_experiment
 * 5) Randomized_controlled_trial

unit|Discussion of Systematic errors

 * learningGoals=
 * 1) Critical discussion of the web limitations that are posed in the paper. (web scientists can get rid of some of these mistakes)
 * 2) Understand that systematic errors are part of many experiments.
 * 3) Learn to discuss systematic errors of a paper.
 * 4) which measures have been taken to minimize the amount of systematic errors (e.g. introducing 8 worlds)
 * furtherReading=
 * 1) Systematic error
 * 2) Web based experiments

unit|Metrics and their mapping to the research questions

 * video=File:Under_construction_icon-blue.svg
 * learningGoals=
 * 1) a measure for inqueality: the gini coefficient
 * 2) unpredictability needs the 8 worlds to see how different rankings are
 * 3) market share
 * furtherReading=
 * Gini_coefficient
 * Mean_difference for unpredictability

unit|Results of the Music Recommendation hearding experiments

 * video=File:Under_construction_icon-blue.svg
 * learningGoals=
 * 1) we can observe clear hearding behavior.
 * 2) the way conent is presented on the web has an impact of how people consume it.
 * c

unit|Summary, Further readings, Homework

 * video=File:Under_construction_icon-blue.svg
 * learningGoals=
 * 1) music experiments are just one empirical indicator for hearding behavior
 * 2) other behavior might night another scientific methodology to identify the behavior.
 * 3) Dellschaft shows that herding may reduce quality of information categorization
 * 4) More on herding: link to http://slon.ru/upload/iblock/4a1/Science-2013.pdf

=lesson|User modelling, personalizing, collaborative filtering, Recommendations =
 * learningGoals=
 * 1) don't know where to place this lesson yet. It should somehow point out how collective intelligence is used for recommendations and how this is influenced
 * furtherReading=

unit|user modelling

 * video=File:Under_construction_icon-blue.svg
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unit|Collaborative filtering

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unit|Recommendation

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unit|Personalizing content

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unit|Summary, Further readings, Homework

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=lesson|Advertisement Ecosystems=
 * learningGoals=
 * 1) Understand how cross-site advertisement providers function on the Web
 * 2) Understand advertisement KPIs
 * 3) Relate to recommendations

unit|Introduction to Online Advertisement

 * furtherReading=
 * 1) Online_advertising
 * 2) http://www.rene-pickhardt.de/retargeting-smart-online-marketing-system-by-criteo/
 * 3) http://www.iab.net/media/file/IAB_Internet_Advertising_Revenue_Report_FY_2013.pdf and http://www.iab.net/research/industry_data_and_landscape/adrevenuereport
 * learningGoals=
 * 1) understand the interests of the 4 players (publisher (content owner), advertiser (some brand), ad-service, consumer)
 * 2) be aware of the online ad market and be able to relate it to other ad markets
 * 3) be aware of advertising formats
 * 4) be aware of payment formats for online advertisement
 * 5) test edit
 * video=File:Introduction_to_Online_Advertisement.webm

unit|Metrics for (online) advertisement

 * furtherReading=
 * 1) http://tlvmedia.com/pdf/CPM_CPC_CPA_dCPM.pdf
 * 2) Cost_per_mille
 * 3) Click-through_rate
 * 4) Pay_per_click
 * 5) Affiliate_marketing and Cost_per_acquisition
 * 6) Bounce_rate
 * 7) Conversion_rate
 * learningGoals=
 * 1) be able to list basic metrics of online advertisement (CPC, CTR, CR, BR, CPM) and calculate them
 * 2) be able to interpret the metrics.
 * 3) understand which player should optimize which metric
 * video=File:Metrics_for_online_advertisement.webm

unit|Factors that have impact on advertisement campaigns

 * furtherReading=
 * 1) Conversion_optimization
 * 2) Landing_page_optimization
 * 3) Bait-and-switch
 * 4) Frequency_capping
 * 5) Lead_scoring
 * 6) Targeted_advertising
 * 7) Negative_keyword (very interesting, it shows the amount of data Google has due to ad products)
 * 8) Online_advertising
 * 9) Behavioral_targeting
 * 10) Contextual_advertising
 * learningGoals=
 * 1) Relevance
 * 2) Targeting (which is a form of relevance)
 * 3) User Context
 * 4) Truthfulness of the add
 * 5) design of the landing page (usability)
 * 6) test
 * video=File:Factors_impact_on_advertisement_campaigns.webm

unit|Finding the true value of an advertisement

 * furtherReading=
 * 1) Second_price_auction
 * 2) Auction_theory
 * 3) Game_theory
 * 4) Nash_equilibrium
 * 5) Generalized_second-price_auction
 * 6) original literature: paper and slides
 * learningGoals=
 * 1) Second price auctions
 * 2) Collective intelligence
 * video=File:Under_construction_icon-blue.svg

unit|Understanding the Problems with Click Fraud

 * furtherReading=
 * 1) Click_fraud
 * 2) Click_farm
 * learningGoals=
 * 1) Understand reasons why people would produce click fraud
 * 2) Understand to whom click fraud is harmful.
 * video=File:Understanding_problems_with_click_fraud.webm

unit|Summary, Further readings, Homework

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=lesson|social capital and rational choice theory=

unit|unit 1

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unit|unit 2

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unit|unit 3

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unit|unit 4

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unit|Summary, Further readings, Homework

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