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