Week 1 Flashcards
Definition Algorithms
Algorithms are encoded procedures for transforming input data into a desired output, based on specified calculations (Gillespie, 2014)
Algorithm
Set of rules to obtain the expected output from the given input
Algorithmic power (4 fases)
- Priorizaiton
- Classification
- Association
- Filtering
Fase 1 (Algorithmic power)
Priorization = making an ordered list
- Emphasize or bring attention to certain things at the expense of others
(Google page rank)
Fase 2 (Algorithmic power)
Classification = picking a category
- categorize a particular entity to given class by looking at any number of that entity’s features
- inappropriate Youtube content
Fase 3 (Algorithmic power)
Association = finding links
- Association decisions mark relationships between entities
- dating match
Fase 4 (Algorithmic power)
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
Algorithmic power (2 algorithms)
- Rule-based algorithms
2. Machine learning algorithms
Rule-based algorithms
- 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
Machine learning algorithms
- 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
Definition Recommender Systems
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.
Rationale Recommender System
- avoid choice overload
- maximize user relevance
- increase work efficiency
Recommender Systems (3 techniques)
- content-based filtering
- collaborative filtering
- hybrid filtering
Content-based filtering (techniques RS)
These algorithms learn to recommend items that are similar to the ones that the user liked in the past (based on similarity of items)
Collaborative filtering (techniques RS)
These algorithms suggest recommendations to the user based on items that other users with similar tastes liked in the past
Hybrid filtering (techniques RS)
These algorithms combine features from both content-based and collaborative systems, and usually with other additional elements (mostly used)
Factors Aversion vs Appreciation
- type of task
- level of subjectivity in decisions
- individual characteristics
Definition Algorithmic Persuasion
Any deliberate attempt by a persuader to influence the beliefs, attitudes and behaviors of people through online communication that is mediated by algorithms
Algorithmic Persuasion Framework
- Input
- Algorithm
- Persuasion attempt
- Persuasion process
- Persuasive effects
Fase 1 APF
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
Fase 2 (APF)
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
Fase 3 (APF)
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
Fase 4 (APF)
Persuasion Process:
- relevance
- reduction
- social norm
- automation
- reinforcement
Fase 5 (APF)
Persuasive effects:
- Intended
- Unintended
