Chapter 2 - Information Everywhere
Available for use under Creative Commons license
Summary and main points
This chapter discusses how digital tools help us to filter and make use of the large amount of data that is available to us. The problem of ‘information overload’, it argues, comes from not understanding the difference between ‘data’ and ‘information’, and the solution to this problem is learning how to create meaningful relationships among different pieces of data in order to make them more useful to us.
Information and relationships
It is important to make a distinction among data, information and knowledge.
- ‘Data’ consists of facts and perceptible phenomena in the external world.
- ‘Information’ is created when we form relationships between pieces of data.
- ‘Knowledge’ is the product of applying information to solve problems.
- The problem of information overload is not one of ‘too much information’, but rather one of working out how to filter the data available to us and relate it to other data in a meaningful way.
- While digital media may add to our confusion by making more data available, they also provide solutions by giving us new ways to filter data and form relationships among data.
- The most widely used organization systems are hierarchical taxonomies like library catalogue systems and the classification system for plants and animals developed by Linnaeus.
- Classification systems change what we can do with data, the kinds of meanings that we can make with data, the way we think about data as well as human relationships and identities.
- The disadvantages of the hierarchical taxonomy include the fact that categories are sometimes arbitrary, that the same piece of data cannot usually reside in different categories at the same time, and that it does not reflect the associative nature of our thought processes.
Networks and organization
Digital media make it easier to organize data based on networked associations through linking and tagging.
- ‘Linking’ involves creating hyperlinks between pieces of data.
- ‘Tagging’ involves attaching labels (or metadata) to data.
- Tagging allows us to organize data based on associations rather than hierarchical categories.
- Some websites allow social tagging where users can attach metadata to the content they find there or upload, and share this metadata with others.
- Classification systems derived from social tagging are called folksonomies.
Finding and filtering
- Algorithms are sets of procedures for completing tasks; they are the basis of all computer programs. Algorithms help us to find and filter data.
- Algorithms executed automatically by computer programs are called technological algorithms.
- Social algorithms or social filters filter data based on the recommendations of people in our online social networks.
- Personalized filters filter data based on our personal decisions or past behaviour.
Recovery or discovery?
- When searching for data it is important to determine whether our main goal is to recover the answer to a question we already have or to discover what kind of data is available about a particular topic independent of any specific question.
- Choosing useful search terms requires attention to both meaning (the semantic relationship of the search term to the topic we are interested in) and syntax (the way we combine different search terms). Often the results of searches can be used to refine our search terms.
The pragmatics of search
- Approaching search pragmatically means focusing on what we want to do with the data that we find.
- Another aspect of the pragmatics of search is regarding search as a kind of dialogue in which the results of a search suggests new strategies or new things to search for.
- Usually the best way to evaluate data is to examine the relationships they have with people, institutions and other pieces of data.
- It is important to remember that all data has ‘an agenda’ (that it was produced for a particular purpose or presupposes a certain view of the world). Even data that seems ‘objective’ (like statistics or sensory perceptions) are biased in that they include some aspects of reality and exclude others.
1. Visit the following search engines and use them to try to find:
- a photo of Sergey Brin;
- the definition of ‘algorithm’;
- Lady Gaga’s real name;
- a friend’s Facebook profile;
- a pizzeria near your home;
- a blog for cat lovers;
- a website where you can buy Washington apples online.
Compare and contrast the search engines in terms of their ease of use and the relevance of results that they returned. What are the different features these search engines offer and how do these features help you find what you are looking for?
Look at the following images and explain the comparisons. Do you think the comparisons are appropriate? Why or why not?
Photo credit FindYourSearch13 (CC BY license14)
Available for use under Creative Commons license
Come up with comparisons for each of the following:
Checking my email is like ___________________.
Using a search engine is like _________________.
An algorithm is like a/n _____________________.
3. Searching through history
Look at the infographic below and choose three events in the history of search that you think were the most significant to the way you search for information. Explain your choices.
[Embed the following infographic here (available for free embedding)
Infographic by PPC Blog
4. Popularity contest
Google Trends17 is a site that helps you to search for information about the most popular search terms and to compare the popularity of different search terms.
For an example see a comparison between Lady Gaga and President Obama18
This game can be played between individuals or teams
1. Player/team A chooses a search term he or she believes is currently popular.
2. Player/team B chooses a search term he or she believes is more popular.
3. The two search terms are typed into Google trends (separated by a comma) to determine the most popular term. The player/team who has chosen the more popular term is awarded a point.
3. The next round begins with player/team B choosing a term.
Players/teams can also compete in guessing what year/month a particular term was the most popular and/or in which country it was the most popular.
Xerox Information Overload Hub
IBM, Information Overload
The Economist, Data, Data everywhere
Social tagging and folksonomies
Flickr, Tagography case studies
Social bookmarking in plain English
Internet Tutorials: Basic search techniques
Google Blog, personalized search
SEOMOZ, Tools to hack Google’s personalized search
Search engine land