Social Information Processing

Hafsa Maqbool

Taahirah S. Arain

2

Overview Madness of Crowds

Wisdom of Crowds

Collective Intelligence

SIP Systems

Collaborative Tagging

Collaborative Filtering

Recommendation Systems

The Madness of Crowds Crowd Psychology: The psychology of the crowd differs

significantly from the psychology of those individuals within it.

Crowd Behavior: When people in crowds express existing beliefs and values so that the mob reaction is the rational product of widespread popular feeling

Collective Conscious: Refers to the shared beliefs and moral attitudes which operate as a unifying force within society

Collective Behavior: Refers to social processes and events which do not reflect existing social structure, but which emerge in a "spontaneous" way.

Herd Mentality: How people are influenced by their peers to adopt certain behaviors, follow trends, and/or purchase items

The Wisdom of Crowds Idea was popularized by a book published in 2004 called The

Wisdom of Crowds by James Surowiecki

A diverse collection of independently-deciding individuals is likely to make certain types of decisions and predictions better than individuals or even experts

The aggregation of information in groups resulted in decisions that are often better than could have been made by any single member of the group

This book presented numerous case studies that touch several fields, primarily economics and psychology.

Delphi Method, Prediction Markets, County Fair example

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Not Every Crowd is Wise The key criteria for a wise crowds:• Diversity of Opinion: Each person should have private

information even if it's just an eccentric interpretation of the known facts.

• Independence: People's opinions aren't determined by the opinions of those around them.

• Decentralization: People are able to specialize and draw on local knowledge.

• Aggregation: Some mechanism exists for turning private judgments into a collective decision.

Crowds work best when they choose for themselves what to work on and what information they need.• The SARS-virus isolation- The free flow of data enabled laboratories

around the world to coordinate research without a central point of control

Failures of Crowd Wisdom The crowd can produce very bad judgment and the main reason is that

the system for making decisions has a systematic flaw.

When the decision making environment is not set up to accept the crowd it loses the benefits of individual judgments and private information and the crowd can only do as well as its smartest member.

Detailed case histories of such failures include:

• The Columbia shuttle disaster that suffered from centralization as the hierarchical NASA management bureaucracy that was totally closed to the wisdom of low-level engineers.

• 9/11 attacks due to division, prevention failed partly because information held by one subdivision was not accessible by another.

• Imitation - information cascade forms and can lead to fragile social outcomes.

• Emotionality - Emotional factors, such as a feeling of belonging, can lead to peer pressure, herd instinct, and in extreme cases collective hysteria.

Advantages of Crowd Wisdom

Thinking and information processing can be much faster, more reliable, and less subject to political forces than the deliberations of experts or expert committees.

Coordinating behavior because of the common understanding within a culture which allows for remarkably accurate judgments about specific reactions of other members of the culture.

• Optimizing the utilization of a popular bar and not colliding in moving traffic flows.

• Pedestrians optimizing the pavement flow

• The extent of crowding in popular restaurants.

Forming networks of trust without a central system controlling their behavior or directly enforcing their compliance.

Collective Intelligence Collective intelligence (CI) is a shared or group intelligence

that emerges from the collaboration and competition of many individuals

Intelligence vs. IQ

Some experts believe CI can help to overcome groupthink and individual cognitive bias in order to allow a collective to cooperate on one process

CI draws on the Internet and the shift to Web 2.0 to enhance the social pool of knowledge

Wikinomics, Prediction Markets

Principles for CI Collective intelligence is mass collaboration. In order for

this concept to happen, four principles need to exist:

• Openness: By loosening the hold over resources, people and companies, can reap more benefits.

• Peering: A form of horizontal organization with the capacity to create

information technology and physical products. Here participants have different motivations for contributing but it leverages self-organization

• Sharing: This can be a controversial principle for companies as too much sharing of intellectual property can reach competitors but limiting it can shut out all possible opportunities.

• Acting Globally: Globally integrated company would have no geographical boundaries but have global connections, allowing them to gain access to new markets, ideas and technology.

Social Information Processing

An activity through which a group of people (collective human) create, organize, and process information

Typically computer tools are used• Authoring tools, collaboration tools, tagging systems, social networking,

collaborative filtering, social information aggregation

Not an invention of the 21st century. • Trictionary, Oxford English Dictionary

Wisdom of Crowds + Collective Intelligence = Swarm Intelligence

• Swarm intelligence (SI) describes the collective behaviour of decentralized, self-organized systems, natural or artificial.

Information Ecology Connection between ecological ideas and

properties of the increasingly dense, complex and important digital informational environment

Often used as metaphor, viewing the informational space as an ecosystem

Analysis of information systems from a perspective that considers • the distribution and abundance of organisms,

• their relationships with each other,

• how they influence and are influenced by their environment. Virtual lack of boundaries between information

systems and the impact of information technology on economic, social and environmental activities

Collaborative Tagging A tag is a keyword or term assigned to a piece of information, it is a

kind of metadata

Also known as folksonomy, social bookmarking, social indexing, social classification, and social tagging

In these systems users assign tags to resources shared with other users giving rise to a type of information organization

The resulting information structures reflect the CI of a community of users

These systems exhibit a form or self-organizing dynamics

These systems can provide navigational cues for other users to explore information

Vocabulary Problem

Models of Collaborative Tagging

Behavioral Models• ask the question of "what“

• Describing the patterns that emerge as individual behavior is aggregated in a large social information system

Predictive Models• ask the question of "why“

• Provide explanations to why different system characteristics may lead to different aggregate patterns, and can therefore potentially provide information on how systems should be designed to achieve different social purposes.

Recommendation Systems Work from a specific type of information filtering

system technique

Attempts to recommend information items that are likely to be of interest to the user

Typically, compares a user profile to some reference characteristics, and seeks to predict the 'rating' that a user would give to an item they had not yet considered

Content-based approach - These characteristics may be

from the information item

Collaborative filtering approach - the user's social environment

Content-Based Approach Rating of an item on a sliding scale. Ranking of a collection of items from favorite to

least favorite. Asking a user to create a list of items that

he/she likes. Observing the items that a user views in an

online store. Keeping a record of the items that a user

purchases online. Obtaining a list of items that a user has

listened to or watched on his/her computer.

Collaborative Filtering Collaborative filtering (CF) is the process of

filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources, etc. 

Collaborative filtering systems usually take two steps:• Look for users who share the same rating patterns with the active

user (the user whom the prediction is for).

• Use the ratings from those like-minded users found in step 1 to calculate a prediction for the active user

Memory based Model Based Hybrid

Memory-Based CF Uses user rating data to compute similarity between users

or items Used for making recommendations It is easy to implement and is effective Examples: neighborhood based CF and item-based/user-

based top-N recommendations Advantages: • explainability of the results, which is an important aspect of

recommendation systems.

• easy to create and use

• New data can be added easily and incrementally. Disadvantages: • depends on human ratings

• performance decreases when data gets sparse, which is frequent with web related items.

• cannot handle new users or new items.

Model-Based CF Developed using data mining, machine learning algorithms to

find patterns based on training data and then used to make predictions for real data

Has a more holistic goal to uncover latent factors that explain observed ratings

Advantages: • handles the scarcity better than memory based ones

• Gives an intuitive rationale for the recommendations. Disadvantages:• Expensive model building

• A number of models have difficulty explaining the predictions.

Hybrid

Combines the memory-based and the model-based CF algorithms.

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ReviewMadness of Crowds

Wisdom of Crowds

Collective Intelligence

SIP Systems

Collaborative Tagging

Collaborative Filtering

Recommendation Systems

http://en.wikipedia.org/wiki/Social_computing http://en.wikipedia.org/wiki/The_Wisdom_of_Crowds http://en.wikipedia.org/wiki/Crowd_psychology http://en.wikipedia.org/wiki/Collective_intelligence http://en.wikipedia.org/wiki/Social_Information_Processing http://en.wikipedia.org/wiki/Models_of_collaborative_tagging