Week 1

Question 1

Definition Algorithms

Answer 1

Algorithms are encoded procedures for transforming input data into a desired output, based on specified calculations (Gillespie, 2014)

Question 2

Algorithm

Answer 2

Set of rules to obtain the expected output from the given input

Question 3

Algorithmic power (4 fases)

Answer 3

1. Priorizaiton
2. Classification
3. Association
4. Filtering

Question 4

Fase 1 (Algorithmic power)

Answer 4

Priorization = making an ordered list
- Emphasize or bring attention to certain things at the expense of others
(Google page rank)

Question 5

Fase 2 (Algorithmic power)

Answer 5

Classification = picking a category

• categorize a particular entity to given class by looking at any number of that entity’s features
• inappropriate Youtube content

Question 6

Fase 3 (Algorithmic power)

Answer 6

Association = finding links

• Association decisions mark relationships between entities
• dating match

Question 7

Fase 4 (Algorithmic power)

Answer 7

Filtering = isolating what’s important

• including or excluding information according to various rules or criteria. Inputs to filtering algorithms often take prioritizing, classification and association decisions into account
• Facebook news feed

Question 8

Algorithmic power (2 algorithms)

Answer 8

1. Rule-based algorithms

2. Machine learning algorithms

Question 9

Rule-based algorithms

Answer 9

• based on a set of rules or steps
• IF - THEN statements –> if [condition] then [result]
  Pro: quick, easy to follow
  Con: only applicable to the specified conditions

Question 10

Machine learning algorithms

Answer 10

• Algorithms that learn by themselves (based on statistical models rather than deterministic rules)
• These algorithms are trained based on a corpus of data from which they may learn to make certain kinds of decisions without human oversight
  Pro: flexible and amenable to adaptions
  Con: need to be trained & black box

Question 11

Definition Recommender Systems

Answer 11

Recommender systems are algorithms that provide suggestions for content that is most likely of interest to a particular user (Ricci et al., 2015)

• these algorithms that decide which content to display to whom based on certain criteria
• users are thus receiving distinct streams of online content
• movies on Netflix, songs on Spotify, etc.

Question 12

Rationale Recommender System

Answer 12

• avoid choice overload
• maximize user relevance
• increase work efficiency

Question 13

Recommender Systems (3 techniques)

Answer 13

1. content-based filtering
2. collaborative filtering
3. hybrid filtering

Question 14

Content-based filtering (techniques RS)

Answer 14

These algorithms learn to recommend items that are similar to the ones that the user liked in the past (based on similarity of items)

Question 15

Collaborative filtering (techniques RS)

Answer 15

These algorithms suggest recommendations to the user based on items that other users with similar tastes liked in the past

Question 16

Hybrid filtering (techniques RS)

Answer 16

These algorithms combine features from both content-based and collaborative systems, and usually with other additional elements (mostly used)

Question 17

Factors Aversion vs Appreciation

Answer 17

• type of task
• level of subjectivity in decisions
• individual characteristics

Question 18

Definition Algorithmic Persuasion

Answer 18

Any deliberate attempt by a persuader to influence the beliefs, attitudes and behaviors of people through online communication that is mediated by algorithms

Question 19

Algorithmic Persuasion Framework

Answer 19

1. Input
2. Algorithm
3. Persuasion attempt
4. Persuasion process
5. Persuasive effects

Question 20

Fase 1 APF

Answer 20

Input:

• First party data = data a company collect and own by themselves
• Second party data = data used from a collaborative company (Google)
• Third party data = external, specialized at gathering data. You can buy this data
• Implicit data = all the data we leave behind and you are aware of it
• Explicit data = IP adress

Question 21

Fase 2 (APF)

Answer 21

Algorithm:

• techniques
• objective of persuader
• algorithmic bias = algorithm is never neutral. The developers are never neutral. Machine based algorithms are also never neutral because they are trained

Question 22

Fase 3 (APF)

Answer 22

Persuasion attempt:

• context = algorithmic persuasion happens everywhere and can happen in multiple context, not only marketing
• nature
• medium = algorithms take place on multiple mediums, smart tv, smartphone, internet, etc.
• modality = algorithmic persuasion can be a video, an audio, a text, etc. It can be personal

Question 23

Fase 4 (APF)

Answer 23

Persuasion Process:

• relevance
• reduction
• social norm
• automation
• reinforcement

Question 24

Fase 5 (APF)

Answer 24

Persuasive effects:

• Intended
• Unintended