Garantie de satisfaction à 100% Disponible immédiatement après paiement En ligne et en PDF Tu n'es attaché à rien
logo-home
Recherché précédemment par vous
Recherché précédemment par vous
logo
Topic Algorithmic Persuasion in the Digital Society €10,49
Ajouter au panier
Accédez rapidement à
Aperçu
  2.  
  3. Universiteit Van Amsterdam (UvA)
  4.  
  5. Topic Algorithmic Persuasion in the Digital Society (775334007Y)

Notes de cours

Topic Algorithmic Persuasion in the Digital Society

1 vérifier
 14 fois vendu
  • Cours
  • Topic Algorithmic Persuasion in the Digital Society (775334007Y)
  • Établissement
  • Universiteit Van Amsterdam (UvA)

This is a resume of the lectures for the course "Topic Algorithmic Persuasion in the Digital Society" for the school year 2023/2024. It has all the important models and theroies discussed in the lectures and in the texts :)

Aperçu 3 sur 25  pages

Infos sur le Document

  • 18 juin 2024
  • 25
  • 2023/2024
  • Notes de cours
  • Walraven merel
  • Toutes les classes

Sujets

  • algorithm
  • digital society
  • communication science
  • media and infomration
  • persuasion

École, étude et sujet

  • Universiteit van Amsterdam (UvA)
  • Communicatiewetenschap
  • Topic Algorithmic Persuasion in the Digital Society (775334007Y)
Tous les documents sur ce sujet (14)

1  vérifier

review-writer-avatar

Par: maria8bogdan • 1 semaine de cela

Vendeur

avatar-seller
annastampanoni

Avis reçus

Aperçu du contenu

ALGORITHMIC PERSSUASION

WEEK 1

Algorithms
 encoded procedures for transforming input data into a desired output, based on specific calculations
(comparable to cake recipe)




Algorithmic power (desired output)  four main functions of algorithms algorithms can

 Prioritization making an ordered list
- Emphasize or bring attention to certain things at the expense of others (es google page rank)
- Make a list of the most important things while disregarding other things

 Classification algo. Can easily pick a category
- Categorize a particular entity to given class by looking at any number of the entity’s features (es inappropriate
YT content)
- Classify based on two categories (good to go or not good to go)

 Association finding links
- Association decisions mark relationships between entities (es dating match, “if you like this, u’ll like this)
- Finding certain patterns or links between two different people and partnering people up

 Filtering isolating what’s important
- Including/excluding information according to various rules/criteria. Inputs to filtering algorithms often take
prioritizing, classification, or association decision into account (es insta feed)

Two broad categories

 Rule-based algorithms  Based on a set of rules you need to identify in advance
- Typically “IF – Then” statement “IF” condition, “THEN” result if a person like this content, give them
more of this content
- Pro easy to follow/understand/formulate
- Con only applicable to the specified conditions

Machine learning algorithms (supervised and unsupervised) Algorithms that “learn” by themselves  based
on statistical models rather than deterministic rules/set of specific instructions
- The algorithms are “trained” based on a corpus od data from which they may “learn” to make certain kinds of
decision without hum oversight (es algorithm that get the sentiment..)
- Pro flexible and amenable to adaptions
- Cons needs to be trained in order to perform well and black-box at some point you won’t understand the
output of the algorithms cause it trained itself)
- Most social media use machine learning algorithms  big corpus of data
 Es facebook’s deep face algorithm
- Facial recognition system that identifies human faces in digital images
- Trained on a large identity label data set of four million facial images
- Not always being used for good reasons to monitor people
- has 97% accuracy

Types of MLA

Supervised algorithms the algorithms learn from labeled data  which means the input data is paired with the
correct output
 es classify emails in spam or not spam based on features such as key words, sender information…

,  Classify news articles as fake or real based on fact-checking websites, expert judgment, or sources
characteristics

Unsupervised learning deals with unlabeled data the algorithms tries to find a patterns or structure in the data
without any guidance to what to look for
 Es Clustering similar news articles together based on their content, without any prior labels e.g. to identify
topics discussed around AI
 Clustering travel images related to destinations, landscapes and travel experiences ; Cluster fashion images
related to ….

Semi-supervised learning combination of supervised and unsupervised learning
 es: Training a model to classify customer reviews as positive or negative using a small labeled dataset and a
large amount of unlabeled text data.

Semi-unsupervised learning involves an agent learning to make decisions by interacting with an environment
 Es Teaching a chatbot to interact with users, where positive user responses (e.g., ‘thank you’) are rewarded
with points, encouraging the bot to learn better responses over time.
 Learning a virtual influencer to generate personalized recommendations to the user based on engagment
metrics (e.g., likes)

Recommender systems  provide suggestions for content that is most likely of interest to a particular user 
important kinds of algorithms we see online
- They decide which content to display to whom based on certain criteria
- Users are thus receiving distinct streams of online content
- Es FB news feed, Netflix movie, Spotify songs

Reasons why recommender system are used:
1. Prevent choice overloadreduce pressure on users to choose something
2. Maximize user relevence make sure content aligns best with user’s interest
3. Increase work efficacy if u let the system recommend certain things, it is not necessary for people to figure
out what users want

 Techniques (types of rec. systems)
Content based filtering learn to recommend items that are similar to the ones users liked in the past (based on
similarity of items)
- If u like horror movies Netflix will recommend you more of them

Collaborative filtering suggest recommendations to users based on items other users with similar interests liked
in the past
- People with the same gender, age range, people in your location
- Used very often when you start using a platform since it doesn’t have enough data on you to do content-based
filtering

Hybrid filtering combine features from content based and collaborative systems while adding other elements
(es demographics)
- Most used on most platforms



How do people perceive algorithms range of perspectives on algorithms: appreciation < -- > aversion
 Algorithmic appreciation people rely more on algorithms than on people despite the fact that they don’t
understand algorithms  people prefer algorithmic decision over human
- Blind faith in automation tourists trust maps too much that bike in a car tunnel  blindly following
technology without using your own human decision
- Humans are imperfect, whereas computers are objective, rational, reliable..

 algorithmic aversion Prefer human decision over algorithmic decision  even when the human decisions are
clearly inferior

, - People are averse because they don’t understand the algorithmic process they think humans are easier to
understand
- Less tolerance for errors from algorithms than from humans (e.g.,GPS leads you to traffic jam→you get
angrier than if a human led you to a traffic jam)

Algorithmic anxiety lack of control and uncertainty over algorithms

Whether you appreciate algorithms depends on many other factors
 Type of task and level of subjectivity in decisions
- Mechanical, objective task  algorithm preferred (es. financial decision, rational decision making)
- More subjective task Human (es. Relationships or hiring someone)
 Individual differences
- knowledge, privacy concerns, belief in equality (es young, highly educated woman perceive algorithms most
useful, old man less)
- The more technical knowledge and the less privacy concerns you have, the more useful and fairer u’ll find it


Do people know how algorithms curate content?

 Algorithmic awareness (Zarouali, Boerman, & de Vreese, 2020)
- People have a very low algorithmic awareness
- Algorithms should be seen as tools that can support rather than replace humans in decision-making The
power of veto remains important: humans should always have the power to interfere.

 When asked, people come up with their own theories about how algorithms work on Facebook:
- The Theory of Loud and Quiet Friends  loud friends that post a lot are more often in my feed than shy friends
that post very often
- The Global Popularity Theory  he more friends someone has, the more likely their posts will be in my feed
because they are more popular
- The Fresh Blood Theory  recently added friends’ content is more often on your feed
- The OC Theory  people that post original content (instead of sharing other people’s
content) are more often in your feed
- The Randomness Theory D  the algorithm randomly shows you content


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
 Persuasion is intentional and deliberate
 A persuader can be a person, brand, political and commercial actor
 Influence is important, impact should be generated through using algorithms, whether that be weak/strong,
short/long term, it varies
 Through online communication algorithms always occur online through media, web
 Mediated by algorithms communication should be driven by algorithmic technology


Algorithmic persuasion is circular (feedback loop)




 Accounts for feedback loops new data we provide will always be used as new input, so the cycle continues

Les avantages d'acheter des résumés chez Stuvia:

Qualité garantie par les avis des clients

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

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

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 annastampanoni. Stuvia facilite les paiements au vendeur.

Est-ce que j'aurai un abonnement?

Non, vous n'achetez ce résumé que pour €10,49. Vous n'êtes lié à rien après votre achat.

Peut-on faire confiance à Stuvia ?

4.6 étoiles sur Google & Trustpilot (+1000 avis)

71166 résumés ont été vendus ces 30 derniers jours

Fondée en 2010, la référence pour acheter des résumés depuis déjà 15 ans

Commencez à vendre!
€10,49  14x  vendu
  • (1)
Ajouter au panier
Ajouté