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© Tefko Saracevic1 What is information? Really, what is it?

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1 © Tefko Saracevic1 What is information? Really, what is it?

2 © Tefko Saracevic2 As a phenomenon … Answer: WE DON’T KNOW!

3 © Tefko Saracevic3 As a concept … Well, we understand it intuitively quite well We have a number of lexical definitions We use it and nobody has to define it for us  it is a y’know concept We easily adapt to various connotations (differing senses) depending on the context But formally, in a scientific sense, we really don’t know what information is as a phenomenon,

4 © Tefko Saracevic4 As a basic phenomenon We don’t know what information is any more than we know what is energy matter gravity life knowledge Basic phenomena in physics, biology, philosophy …we don’t know what they are, BUT:

5 © Tefko Saracevic5 So how do we study them? We study their manifestations behavior (or behaviour if you are British) effects All scholarship, every science does this  e.g. we don’t know what gravity is, but we have physical laws that describe its behavior and effects

6 © Tefko Saracevic6 this slide doesn’t have to do with the lecture topic but I liked the picture, so here it is

7 © Tefko Saracevic7 Information treated in various disciplines physics – basic property as energy, matter biology – senses neurophysiology – brain processes psychology – behavior, perception cognitive science – cognition telecommunication, computer science – signals, bits philosophy – knowledge

8 © Tefko Saracevic8 Information in information science Again: intuitively well understood, but formally not well stated  Several viewpoints, models emerged Shannon: source-channel-destination  signals not content – not really applicable Cognitive: changes in cognitive structures  content processing & effects Social: context, situation  information seeking, tasks

9 © Tefko Saracevic9 Shannon's information theory Developed a general model for communication systems, and a set of theoretical tools for analyzing such systems, by measuring information in terms of probabilities (numbers)

10 © Tefko Saracevic10 Shannon … reducing uncertainty Defined information in terms of probabilities  developed measure of the information we get from observing the occurrence of an event, BUT ignored any particular features of the event  e.g. flipping a fair coin once will give us events h and t each with probability 1/2, and thus a single flip of a coin gives us - log 2 (1/2) = 1 bit (Binary digit) of information (whether it comes up h or t)  Information measured in bits is the reduction in uncertainty in the mind of the receiver  information (in bits) is the amount of uncertainty a measure eliminates

11 © Tefko Saracevic11 Shannon’s impact Had great impact in treating of digital information as signals  e.g maximizing capacities of channels; coding Had disappointing impact (despite many tries) where information is connected with meaning, context, humans, values  the ideal to measure information in human terms is elusive  but keep trying …

12 © Tefko Saracevic12 Cognitive – basic idea: K(S) + ΔI = K(S + ΔS) (Brookes) Information when operating on a knowledge structure produces an effect whereby the knowledge structure is changed “Information is differences that make a difference” (find who said it!) Actually, it only states the problem –  “unoperational” in information systems  involves cognitive, mental events only  constructivists rejected it

13 © Tefko Saracevic13 Information manifestations (Buckland) Information as a process  what someone knows is changed when informed; “the action of informing” (similar as Brookes)  refers to cognitive changes + process of doing it Information as knowledge  knowledge communicated about x  uncertainty removal a special case  refers to that which is being communicated - intangible Information as a thing  data, documents with quality of imparting information - tangible  refers to potential information conveyed from objects

14 © Tefko Saracevic14 Information in information science Three senses (from narrowest to broadest) 1. Information in terms of decision involving little or no cognitive processing - Shannon  signals, bits, straightforward data - computing, telecommunication, economics, 2. Information involving cognitive processing & understanding - Brookes  understanding, matching texts 3. Information also as related to context, situation, problem- at-hand, process – Buckland to some extend  users, use, tasks For information science (including information retrieval): third, broadest interpretation necessary

15 © Tefko Saracevic15 What is in a book? (for discussion in class) Cover page of a book : What is in it?

16 © Tefko Saracevic16 A chapte r in that book

17 © Tefko Saracevic17 Here is also a cover page of a book : What is in it?

18 © Tefko Saracevic18 A chapte r in that book

19 © Tefko Saracevic19 Adequacy? None of the theories about information are adequate to cover fully information as a phenomenon Each covers a bit or simply describes manifestation Shannon’s theory is testable, but reduces “information” to signals only A scientific theory is one that can be refuted – tested for confirmation, rejection ( Popper) - they understood this

20 © Tefko Saracevic20 So we went to study Human information behavior  “how people need, seek, manage, give, and use information in different contexts” (Pettigrew )  many models, theories, studies on a variety of aspects  extends to study of web behavior Bibliometrics  “ the study, or measurement, of texts and information” (Norton)  many studies and formal statements about structure & patterns of literatures, citations, authors, journals, texts …  Bradford’s law, Lotka’s law

21 © Tefko Saracevic21 to be continued … We cover these in other lectures and courses and two 2005 books cover very nicely the topics of theories of human information behavior and integration of information seeking and information retrieval in context  highly recommended

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24 © Tefko Saracevic24 p.s. there is code in other slides

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