dream catcher- synthesizing the cognitive maps

8/3/2019 Dream Catcher- Synthesizing the Cognitive Maps

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Dreamcatcher: Synthesizing the Cognitive Mapsof Collectivities

Myriam AbramsonNaval Research LaboratoryWashington, DC [email protected]

Abstract Dreamcatcher is a blueprint for synthesizing the cognitive maps of col-lectivities to gain insights into their beliefs, desires and intents. As the ease of pro-ducing and disseminating opinions on the Web increases through the proliferationof weblogs, there is a need to understand the formation and the propagation of opin-ions in the social context of the Web. Although opinions are not reliable predictorsof behavior at the individual level, they have been shown reliable at the collectivelevel, for example, in prediction markets. Dreamcatcher combines discovery, learn-ing and inferencing to produce dynamic cognitive maps from a set of weblogs. Thespecic problems related to the nature of weblogs and the key issues are outlined.

1 Introduction

Beyond the macrocospic view of blog sites and microcospic view of blogposts [5],it is interesting to study the content of blogs themselves, or memetic view, for thediscovery of opinions and trends of opinions. Once blogs have been aggregated andclassied by topics and sentiment analysis and that structural patterns have beenfound, what conclusions can be inferred from the ideas and opinions themselves?Cognitive maps are representations of the inferences we make, explicitly or im-plicitly. They have been used in the social sciences to represent and explain thereasoning found behind complex political decisions [4] and the impact of school re-forms on student engagement [6]. We claim that the cognitive map of collectivities,such as those formed virtually through weblogs and social media, might be differentfrom the cognitive map of any single individual and might better describe collectiveintelligence to infer trends of public opinion than polling methods.

The overall technical approach and key issues in deriving cognitive maps from we-blogs are rst presented. We then discuss the possible types of inference that can

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be made from the cognitive maps obtained and conclude with possible uses of thisapproach.

Cultural

Understanding

Military

Strikes

Population

Hardship

Infrastructure

Population

Well-being

Strength of

Government+0.65

+0.8 +0.65

-0.35

-0.45

+0.75

Insurgency Force

Protection

-0.35

Cooperation

with Localities

+0.25

+0.25

+0.8

-0.25

-0.25

-0.25

-0.25

Police

+0.15

-0.25

Intimidation

+0.45

-0.35

Fig. 1 Counter-insurgeny cognitive map manually extracted from several documents

2 Approach

Cognitive maps are graphical models of causal assertions expressed as directededges between concept nodes. They differ from other graphical representations,such as Bayesian nets and inuence diagrams, mainly because feedback loops, i.e.cycles, are possible to express many common beliefs and paradoxes (Fig. 1). Posi-tive or negative causality are specied on the edges to indicate whether an increasedstrength in the causal node effects an increased or decreased strength in the relatednode. Concept nodes can be further differentiated into policy and outcome nodes(desirable and undesirable) to formulate and predict the effect of different strategies[4][7]. This representation can be extended to a collectivity by parsing and aggre-gating several documents. In order to achieve this goal, some key issues must beaddressed.

Gated information sharing projects try to foster a community linked by interestand purpose. In the unrestricted connectivity of the Web, virtual communities formin an ad hoc manner and the problem becomes to identify dynamic groups. It is not

enough to link individuals through blogs and comments in a social network sincethey could have different opinions. There is a need to identify a collectivity througha combination of cluster analysis based on textual content and sentiment analysis.


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Dreamcatcher: Synthesizing the Cognitive Maps of Collectivities 3

Sentiment analysis further discriminates documents based on the polarity of theopinions expressed at the sentence level.

Once a collectivity has been identied, it remains to extract qualitative causal rela-tions from unstructured text using information extraction patterns, determine their

positive/negative qualitative relationship, and quantify the strength of those rela-tions (based on qualier words such as inevitably, best, etc.). Culture needs tobe taken into account to evaluate the relative strength of opinions. While certainwords, such as augment or inhibit, directly denote positive/negative causal re-lationships, causal relations are difcult to identify from predicate keywords alone.There is an implied causality from temporal clauses. For example, as the projectsprogress ... the attacks become less frequent. Often, an implied causality can be in-ferred from an entire storyline. For example, the Cinderella story has an underlyingmessage that hard work will lead to success embedded in its narrative (Fig. 2).

Success Working Hard

+

-

Fig. 2 Cultural Belief

The validity of those causal relations can be evaluated by similarity to those ex-tracted by human readers. Once causal relations have been extracted and their as-sociated cause and effect concepts generalized, opinion rules can be learned as as-sociation rules. The inuence score of blog posts weighs the causal relations indetermining the condence of an opinion rule. The strength of the causal relationscontributes to the support of the rule. As a nal step, implied causal assertions from

cultural norms and common sense reasoning need to augment the rule set in order tosynthesize intelligible cognitive maps. In an online setting, the temporal overlap of the causal relations determines the temporal frame of a cognitive map. The overallframework is illustrated in Fig. 3.


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Fig. 3 Dreamcatcher architecture

3 Reputation

The inuence of social media on current events has been noted in several occasionsfrom the Polish revolution of 1980 and the inuence of catholicism to the use of the

internet to spread terrorism [8] or to convey a more accurate picture of events [9].Recognition is one of the many facets of inuence [3] but, in contrast to pagerank mechanisms, should be qualitatively evaluated. All beliefs are not created equal.Some weight more than other if they are contained in a reputable blog. A reputationscore can be computed from inbound links where a blog is quoted positively ornegatively using the inference mechanism of cognitive maps (see below in Section4) that takes into account the qualitative reference aspects.


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Fig. 4 Reputation of blogs

4 Inference

After a cognitive map has been synthesized from the opinion rules discovered, itis possible to infer the strength of the concept nodes through stochastic dynamic

programming. The strength of a node Ai is dened recursively from an initial value,possibly random, and its causes A j where P ji is the condence of the rule A j AiandW ji is the strength of the causal relation between Ai and A j (Eq. 1).

Ai(t + 1) f ( Ai(t ) +causes

j= 1

P jiW ji A J (t )) (1)

If the function is a sigmoid function, this value is bounded within [0 , 1] and canbe evaluated comparatively with other value nodes. The process iterates until nochanges occur. Beliefs or soft events can be inferred such as strength of govern-ment or condence in the economy [1]. Because predictions are made about opin-ions, they cant always be veried with occurring events although there might bestrong correlations between them that might help to identify tipping points.

Provided some knowledge about the desirability of certain outcome nodes can beobtained, another type of inference is possible through agent-based simulation . By


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seeding an agent population with the cognitive maps obtained from diverse groups,interactions can be simulated by merging their cognitive maps and inferring thevalue of desirable/undesirable outcome nodes. Agents move closer or farther fromother agents in the cognitive space depending on the outcome of their interactions,

and learn to modify their cognitive map by imitation from their neighbors (peers)through a social learning method [2]. Agentsof change canbe introducedto measuretheir effect on coalition formation. For example, an avatar can be introduced tobridge or polarize virtual communities.

Conclusion

A dreamcatcher is a hoop ensnaring a spider web and used as a charm to protectagainst bad dreams. Likewise, Dreamcatcher tries to capture a part of the Web andunderstand opinions and ideologies. This work is at the intersection of machinereading, data mining and inference. Beyond predictive analysis and informationwarfare, Dreamcatcher can help summarize and visualize the content of weblogsand provide a tool for collective decision-making.

References

1. Abramson, M.: Causal Emergence of "Soft" Events. In: AAAI Fall Symposium, Workshop onEmergent Agents and Socialities (2007)

2. Abramson, M.: Coalition formation of cognitive agents. In: AAAI Fall Symposium, Workshopon Cultural Adaptive Agents (2008)

3. Agarwal, N., Liu, H., Tang, L., Yu, P.S.: Identifying the inuential bloggers in a community.In: First Intl Conference on Web Search and Data Mining (WSDM08) (2008)

4. Axelrod, R., Nozicka, G.J., Shapiro, M.J. (eds.): The Structure of Decision: The CognitiveMaps of Political Elites. Princeton University Press (1976)

5. Finin, T., Josh, A., Kolari, P., Java, A., Kale, A., Karandikar, A.: The information ecology of social media and online communities. AI Magazine 29(3) (2008)

6. Goldspink, C.: School reform: an exploratory case study of the impact of student-centred learn-ing in two primarcy schools. Retrieved from http://learningtolearn.sa.edu.au/ on 01/18/09

7. Kosko, B.: Fuzzy cognitive maps. International Journal of Man-Machine Studies 24, 6575(1986)

8. Weimann, G.: Terror on the Internet: The New Arena, the New Challenges. The United StatesInstitute of Peace (USIP) (2006)

9. Woodward, M.: Burmas generals and cyclone nargis: Incompetence, callous indifference orboth? COMPOS Journal: Analysis, Commentary and News from the World of Strategic Com-munications pp. 118 (2008)

dream catcher- synthesizing the cognitive maps

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