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Web Science/Part3: Behavioral models/MoocIndex

< Web Science | Part3: Behavioral models

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

lesson|Processes on the Web Graph: The Example of Modelling the Dynamics of Meme Spreading

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  • 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

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unit|Experimental Setup and Methodology of the Memes spreading Model

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unit|Mathematical foundations of the Memes spreading Model

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unit|Results of the Memes spreading Model

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unit|Summary, Further readings, Homework

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lesson|More Micro Behavior and Macro Effect I: Collective Intelligence

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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

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  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

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  1. IDF aggregates common usage of vocabulary
  2. knowledge about common usage of vocabulary models term specificity

unit|Random surfer Model

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unit|Page rank of Graph/Matrix

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  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

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  1. understand: https://www.princeton.edu/~mjs3/salganik_dodds_watts06_full.pdf
  2. know some basics about: Herd behavior from the field of psychology
  3. w:Absolute_difference
  4. w:Randomized_experiment
  5. w:Randomized_controlled_trial
  6. w:Randomization
  7. w:Web-based_experiments
  8. w:Conditional_independence
  9. w:Independence_(probability_theory)
  10. w:Dependent_and_independent_variables
  11. w:Herd_behavior
  12. w: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
  6. How to conduct a web based experiment with a control group?
  7. Get to know one specific experiment and methodology that demonstrated herd behavior on the web.
  8. Understand how to empirically design an experiment that can demonstrate herd behavior.
  9. Discussing systematic errors in experiments
  10. Understand that it is non trivial to verify phenomenons of herding.
  11. 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

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  • 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. w:Falsifiability
  2. w:Design_of_experiments
  3. w:Research_question
  4. w:Experiment

unit|Experimental Setup and data collection process

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  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. w:Dependent_and_independent_variables
  2. w:Independence_(probability_theory)
  3. w:Treatment_and_control_groups
  4. w:Randomized_experiment
  5. w:Randomized_controlled_trial

unit|Discussion of Systematic errors

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  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

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  • 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=
  1. w:Gini_coefficient
  2. w:Mean_difference for unpredictability

unit|Results of the Music Recommendation hearding experiments

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  • 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.
  3. c

unit|Summary, Further readings, Homework

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  • 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 ([1])
  4. More on herding: link to http://slon.ru/upload/iblock/4a1/Science-2013.pdf


lesson|User modelling, personalizing, collaborative filtering, Recommendations

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  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
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unit|user modelling

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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

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  1. Understand how cross-site advertisement providers function on the Web
  2. Understand advertisement KPIs
  3. Relate to recommendations

unit|Introduction to Online Advertisement

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  1. w: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

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  • furtherReading=
  1. http://tlvmedia.com/pdf/CPM_CPC_CPA_dCPM.pdf
  2. w:Cost_per_mille
  3. w:Click-through_rate
  4. w:Pay_per_click
  5. w:Affiliate_marketing and w:Cost_per_acquisition
  6. w:Bounce_rate
  7. w: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

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  1. w:Conversion_optimization
  2. w:Landing_page_optimization
  3. w:Bait-and-switch
  4. w:Frequency_capping
  5. w:Lead_scoring
  6. w:Targeted_advertising
  7. w:Negative_keyword (very interesting, it shows the amount of data Google has due to ad products)
  8. w:Online_advertising#Trick_banners
  9. w:Behavioral_targeting
  10. w: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

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  1. w:Second_price_auction
  2. w:Auction_theory
  3. w:Game_theory
  4. w:Nash_equilibrium
  5. w:Generalized_second-price_auction
  6. original literature: paper and slides
  • learningGoals=
  1. Second price auctions
  2. Collective intelligence
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unit|Understanding the Problems with Click Fraud

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  1. w:Click_fraud
  2. w: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

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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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