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Strategic Intelligence
From Information Processing to Meaning Making
Neuman et al.
The Laboratory for the Interdisciplinary Study of Symbolic ProcessBen-Gurion University of the Negev, Israel
Strategic intelligence involves the efforts to understand the "Big Picture" emerging from data sources
In other words, the role of strategic intelligence is to expose the “forest,” the whole of which is different from the sum of its parts, the “trees”
In this sense, strategic intelligence deals with meaning-making: The exposure of a macro-structure (e.g. a story) from a collection of micro-structure entities
The shift from the “trees” level to the “forest” level is far from trivial
Meaning involves the simultaneous apprehension of the interrelationships betweenheterogonous micro level components
This task is cognitively demanding
The question is whether we can develop effective tools that reduce the cognitive load of shifting from the micro level of information to the macro level of meaning
We address this question with two basic assumptions
There is no substitute to the human analyst
However, it is possible to develop tools that scaffold the production of meaning
Our second assumption concerns textual data that are the focus of our project
There is a logic underlying the structure of a text
This logic can help us to understand the meaning of a target-concept that appears in the text
In this talk we present a novel methodology for meaning-based analysis of textual data for strategic intelligence analysis
Our analysis focuses on news articles
Why?
“Newspapers are significant part of public discourse and are thus representative of the way in which a speech community publicly constructs its cultural models through language”
(Stephanowitsch 2004)
In this sense, newspapers can serve as a window to the collective mind of a group
The Methodology
The methodology involves several phases
These phases are implemented after we recognize a target concept that we would like to understand
We illustrate the methodology with the target concept “Iraq”
Phase 1. The identification of a relevant corpus of texts
Our corpus
All the news articles (N = 800) in the online monthly English-language Palestinian newspaper Palestine Times
Phase 2. Identification of the “organizing concepts” in each text
We argue that a text can be understood through a few “organizing concepts ”
How can we identify these concepts?
A text may be represented as a network of signs (words) in which the nodes represent signs and the links represent their relationship
A scale-free network of signs is a network in which just a few signs have the greatest connectivity
In this case, the full meaning of the text may be adduced by paying close attention only to these highly connected concepts, the “hubs” of the text
In the pre-processing stage, each newspaper article was automatically segmented into sentences
Part-of-speech tagging was used to assign a grammatical tag for each word
Only nouns, corresponding to concepts, were used for the network analysis
Each article was treated as a graph
For each news article we ranked the nouns according to their degree in the graph and identified the 5 highest ranking nouns as hubs
Phase 3. Identification of texts in which the target concept functions as an organizing concept
To understand a target concept, we first identify a set of articles in which the target concept functions as a hub, i.e., a major organizing concept
“Iraq” appears as a word in 44 news articles
However, “Iraq” appears as a “Hub” only in 9 news articles ranging from March 2000 to October 2004
After we identify the relevant texts we try to understand the conceptual network in which our target concept is embedded
Phase 4. Extraction of “context sentences” from each text
The context sentences include the organizing concepts of each text and the first semantic context in which they appear to the reader
The heuristic we used to extract the context sentences is “hubs first ”
The hubs-first heuristic elicits valuable information for diagnosis and prediction
Hamas case
In January 2006, Hamas — the Islamic terrorist organization —won a victory in Palestinian parliamentary elections
Leading news channels described this victory as a surprise and leading commentators cited the failure of the Israeli and American intelligence agencies to have predicted it
To see whether Hamas’ victory could have been anticipated by the hubs-first heuristic, we extracted the entire context sentences from the intersection of the keywords “Hamas” and “elections” (30 context-sentences)
The sentences span over a period of years ranging from June 1999 to December 2005
We compared this textual data with that elicited by the intersection of “Fatah” (the rival Palestinian party) and “elections” (11 context-sentences)
We conducted a small-scale study in which several subjects were asked to judge whether for each context sentence Hamas or Fatah was described as wining, losing, or neutral
The judges were three Israeli army officers and an army psychologist (reserve forces)
All the judges had a previous experience with sense making at the highest level of army command
It was found that on average the Hamas was portrayed as a winner in 65% of the context-sentences
In contrast, the Fatah was portrayed as a winner only in 16% of the context-sentences!
That is, by observing a pattern that emerged over several years, it could have been anticipated that Hamas would win the elections
Phase 6. Mapping the context-sentences to a visual representation
We produce short segments of telegraphic speech that embody the essential conceptual relations in the context sentences
The analyst maps the sentences into a visual network representation and condenses the network by merging several nodes/concepts under a given title
Phase 7. Exposing the meaning of the target concept
Analyzing articles that spread across a time span, the evolving visual network allows us to expose a conceptual map that represents the meaning of the target concept
The macro structure we found was an interesting story:
U.S. attack in Iraq is for Israel's sake because it will solve the Palestinian problem through transfer of Palestinians to Iraq !
This story emerges from the articles across a time-span
Without the methodology we implemented, it would have been extremely difficult to expose it
Mama loves our project but where are the pitfalls?
The major problem is the mapping from the context-sentences to the visual graph
Conclusions
Meaning-making is one of the most complex human tasks; any tool that aims to support it should be judged within this scope
Thank you