Topic Algorithmic Persuasion in the Digital Society

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