# 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=
* understand that network structure determines processes, such as individual communication
* understand that the network structure determines global communication results
* understand how to model micro-behavior of individuals at large
* understand how to related dying and exploding memes to the same model
* understand the difference of perspectives between micro interactions and macro effects
* Know http://www.nature.com/srep/2012/120329/srep00335/full/srep00335.html
* Know about effective distance http://link.springer.com/article/10.1140%2Fepjb%2Fe2011-20208-9 http://rocs.hu-berlin.de/D3/ebola/
* furtherReading=
* understanding http://www.nature.com/srep/2012/120329/srep00335/full/srep00335.html

## unit|Overview of the phenomenon

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

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

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

* video=File:Under_construction_icon-blue.svg
* learningGoals=
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## unit|Summary, Further readings, Homework

* video=File:Under_construction_icon-blue.svg
* learningGoals=
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# lesson|More Micro Behavior and Macro Effect I: Collective Intelligence

* learningGoals=
* Know about examples of collective intelligence in the Web (and beyond)
* Understand that clever aggregation of randomly noisy sensor output leads to high quality measurements
* Understand that independence of judgement is key to high quality collective decision making
* Relate this to law of large numbers
* Understand the idea of a social sensor: Model people output as sensor output
* Understand the idea of recursive aggregation of reputation
* Understand limitations of when collective intelligence cannot be derived

## unit|IDF as Simple Form of Collective Intelligence

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

## unit|In-degree as Form of Collective Intelligence

* learningGoals=
* IDF aggregates common usage of vocabulary
* 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=
* Eigenvalues are an important metric to describe graphs.
* Decomposing large matrices is computationally heavy.
* relation to the random surfer model

# lesson|More Micro Behavior and Macro Effect II: Herding

* furtherReading=
* understand: https://www.princeton.edu/~mjs3/salganik_dodds_watts06_full.pdf
* know some basics about: Herd behavior from the field of psychology
* w:Absolute_difference
* w:Randomized_experiment
* w:Randomized_controlled_trial
* w:Randomization
* w:Web-based_experiments
* w:Conditional_independence
* w:Independence_(probability_theory)
* w:Dependent_and_independent_variables
* w:Herd_behavior
* w:Systematic_error
* learningGoals=
* Know and understand the notion of herding and swarms
* Know and understand that local information and positive feedback cycles may destroy collective intelligence (e.g. Groupthink, shitstorms, Klaas' tagging experiments, stock exchange.....)
* Know about examples of herding, such as preferential attachment, music experiment,...
* Understand how herding can be measured in an experiment
* How to conduct a web based experiment with a control group?
* Get to know one specific experiment and methodology that demonstrated herd behavior on the web.
* Understand how to empirically design an experiment that can demonstrate herd behavior.
* Discussing systematic errors in experiments
* Understand that it is non trivial to verify phenomenons of herding.
* understand: https://www.princeton.edu/~mjs3/salganik_dodds_watts06_full.pdf
* 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=
* What are the research questions that will be answered in the experiment
* understand that a good study starts with a research question
* The concept of falsifiability.
* Good research questions often start with an obervation (e.g.: experts have frequently failed to predict the success of musicians)
* furtherReading=
* w:Falsifiability
* w:Design_of_experiments
* w:Research_question
* w:Experiment

## unit|Experimental Setup and data collection process

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

## unit|Discussion of Systematic errors

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

## unit|Metrics and their mapping to the research questions

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

## unit|Results of the Music Recommendation hearding experiments

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

# lesson|User modelling, personalizing, collaborative filtering, Recommendations

* learningGoals=
* 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

* video=File:Under_construction_icon-blue.svg
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## unit|Recommendation

* video=File:Under_construction_icon-blue.svg
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## unit|Personalizing content

* video=File:Under_construction_icon-blue.svg
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## unit|Summary, Further readings, Homework

* video=File:Under_construction_icon-blue.svg
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# lesson|Advertisement Ecosystems

* learningGoals=
* Understand how cross-site advertisement providers function on the Web
* Understand advertisement KPIs
* Relate to recommendations

## unit|Introduction to Online Advertisement

* furtherReading=
* w:Online_advertising
* http://www.rene-pickhardt.de/retargeting-smart-online-marketing-system-by-criteo/
* 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=
* understand the interests of the 4 players (publisher (content owner), advertiser (some brand), ad-service, consumer)
* be aware of the online ad market and be able to relate it to other ad markets
* be aware of advertising formats
* be aware of payment formats for online advertisement
* test edit
* video=File:Introduction_to_Online_Advertisement.webm

## unit|Metrics for (online) advertisement

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

## unit|Factors that have impact on advertisement campaigns

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

## unit|Finding the true value of an advertisement

* furtherReading=
* w:Second_price_auction
* w:Auction_theory
* w:Game_theory
* w:Nash_equilibrium
* w:Generalized_second-price_auction
* original literature: paper and slides
* learningGoals=
* Second price auctions
* Collective intelligence
* video=File:Under_construction_icon-blue.svg

## unit|Understanding the Problems with Click Fraud

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

## unit|Summary, Further readings, Homework

* video=File:Under_construction_icon-blue.svg
* learningGoals=
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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

* video=File:Under_construction_icon-blue.svg
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