Topic Algorithmic Persuasion in the Digital Society (775334007Y)
Établissement
Universiteit Van Amsterdam (UvA)
This contains the summary of the gazilion readings for UvA course 'Algorithmic persuasion' and notes. Readings a.o. include those of Boerman, Zarouali, Matz, Segijn, etc. Everything is organized according to weeks (W1-8), with headers and subheaders.
Exam grade: 8.8
Topic Algorithmic Persuasion in the Digital Society (775334007Y)
Tous les documents sur ce sujet (13)
6
revues
Par: annastampanoni • 5 mois de cela
Par: rahmafauziah • 6 mois de cela
Par: jesperappels • 1 année de cela
Traduit par Google
I made it through the summary a few times
Par: elenapapini07 • 1 année de cela
Par: larsoosterbeek • 1 année de cela
Par: bachhoathoigian • 1 année de cela
Vendeur
S'abonner
thaomynguyen
Avis reçus
Aperçu du contenu
Week 1
Algorithms are encoded procedures for Algorithm
transforming unput data into a desired output, Set of rules to obtain
Input Output
based on specific calculations. the edpected output
from given input
Algorithmic power
(functions)
Four functions
- Prioritization making an ordered list
o Emphasize or bring attention to certain things at the expense of others (e.g. google page
rank)
- Classification picking a category
o Categorize a particular entity to given class by looking at any number of the entity's
features (e.g. inappropriate YT content)
- Association finding links
o Association decisions mark relationships between entities (e.g. Okcupid dating match)
- Filtering isolating what's important
o Including/excluding info according to various rules/criteria. Inputs to filtering algorithms
often take prioritizing, classification, or association decisions into account (FB TL)
Two broad categories
Rule-based algorithms
- Based on a set of rules/steps
- Typically IF THEN
- Pro: easy to follow
- Con: only applicable to specified conditions
Machine learning algorithms
- Algorithms that 'learn' by themselves (based on statistical models rather than deterministic rules)
- The 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: needs to be trained & black-box (at some point you won't understand the output of the
algorithm cause it trained itself)
- Most social media use machine learning algorithms big corpus of data
Recommender systems
- Recommender systems are algorithms that provide suggestions for content that is most likely of
interest to a particular user
o These algorithms that decide which content to display to who based on certain criteria
o Users hence receive distinct streams of online content
E.g. FB, Netflix, Spotify, YT, etc.
- Rationale: avoid choice overload, to maximize user relevance, and to increase work efficiency.
,Techniques (types of rec systems)
1. Content-based filtering: these algorithms learn to recommend items that are similar to the ones
that the user liked in the past (based on similarity of items)
2. Collaborative filtering: these algorithms suggest recommendations to the user based on items
that other users with similar tastes liked in the past
3. Hybrid filtering: combine features from both content-based and collaborative systems, and
usually with other additional elements (e.g. demographics) most common type.
Perceptions of algorithms appreciation (e.g. blind faith) vs aversion. May depend
on may factors
- Type of task
o E.g. mechanical, objective tasks (efficiency of showing relevant google content) vs
subjective (dating matches) See also lvl of subjectivity.
- Level of subjectivity in decisions
- Individual characteristics
- Etc. (more research needed)
Algorithmic persuasion
Algorithmic persuasion: 'any deliberate attempt by a persuader to influence the beliefs, attitudes and
behaviours of people through online communication that is mediated by algorithms'
Feedback loop of algorithmic persuasion
,Input
- First party data: data you yourself
- Second party data: Google, using data from a collaborative trusted party
- Third party data: external party, data brokerage, specialised in data.
- Explicit data: data we explicitly leave behind (writing our FB profile), we're aware we leave that
behind
- Implicit data: subconsciously: cookies, IP address, search history.
Algorithm
- Techniques (rule-based vs machine learning, different power structures class, prior, etc.)
- Objective of persuader: changing attitudes, opinions, feelings, behaviours, etc.
- Algorithmic bias
o Developers' bias: they have prejudice.
o Machine learning algorithms are trained on data sets, which can be flawed.
Persuasion attempt
- Context: commercial and non-commercial corporate comm, health comm, marketing, etc.
- Nature:
o Paid: e.g. sponsored. You paying for getting your ads shown by the algorithm
o Organic: if FB is using its own algorithm to filter your TL, that's organic (non-paid)
- Medium: smartphones, laptop, smart TV, public transport, etc.
- Modality: visual, audio, (audio)visual, conversational (Alexa)
Persuasion process
- Relevance: provide us most relevant content
- Reduction: compressing content is more persuasive easier to process
- Social norm: show us what peers find relevant
- Automation: we have bias, we over rely on technology. more vulnerable to alg pers.
- Reinforcement: they are reinforcing previous attitudes showing us content that fit within our
world view.
- Etc…
Persuasive effects
- Intended: desired, expected
- Unintended: undesired
o Manipulation
o Privacy issues
o Exploitation
o Vulnerable
, Week 2| Online advertising
Paper 1 Boerman: OBA literature overview
OBA: ''The practice of monitoring people's online behaviour and using the collected information to show
people individually targeted advertisements'' Boerman, 2017
OBA (online behavioural advertising) is a sub-group of personalized advertising.
Effects depend on advertiser- and consumer-controlled factors.
Advertiser-controlled
- Ad characteristics
o Level of personalization (extent)
Type of info used (web-browsing, clicks, basket)
Amount of info (only 1 type of data or combination)
o Accuracy !!!
- OBA transparency
o Privacy statements and informed consent
Shows what type of data they use and collect
have little effect because we don't read them (and hard to understand)
o Disclosure (increasing transparency through self-regulation)
hardly effective cause we barely recognize the symbol/know what it is)
Consumer controlled factors
- Knowledge and abilities
o Consumers have little knowledge about OBA and hold misconceptions
o Even less knowledge about legal protections (GDPR: EU regulation that protects privacy)
o Consumers do not seem to understand tools to protect online privacy
Les avantages d'acheter des résumés chez Stuvia:
Qualité garantie par les avis des clients
Les clients de Stuvia ont évalués plus de 700 000 résumés. C'est comme ça que vous savez que vous achetez les meilleurs documents.
L’achat facile et rapide
Vous pouvez payer rapidement avec iDeal, carte de crédit ou Stuvia-crédit pour les résumés. Il n'y a pas d'adhésion nécessaire.
Focus sur l’essentiel
Vos camarades écrivent eux-mêmes les notes d’étude, c’est pourquoi les documents sont toujours fiables et à jour. Cela garantit que vous arrivez rapidement au coeur du matériel.
Foire aux questions
Qu'est-ce que j'obtiens en achetant ce document ?
Vous obtenez un PDF, disponible immédiatement après votre achat. Le document acheté est accessible à tout moment, n'importe où et indéfiniment via votre profil.
Garantie de remboursement : comment ça marche ?
Notre garantie de satisfaction garantit que vous trouverez toujours un document d'étude qui vous convient. Vous remplissez un formulaire et notre équipe du service client s'occupe du reste.
Auprès de qui est-ce que j'achète ce résumé ?
Stuvia est une place de marché. Alors, vous n'achetez donc pas ce document chez nous, mais auprès du vendeur thaomynguyen. Stuvia facilite les paiements au vendeur.
Est-ce que j'aurai un abonnement?
Non, vous n'achetez ce résumé que pour €7,89. Vous n'êtes lié à rien après votre achat.
