Swarm-based Web Coolhunting with Condor

The Web has become a mirror of the real world. Reading and interpreting the collective mind allows discovering trends for weeks into the future. Our coolhunting approach gathers open-source information, analyzes and filters it to identify trends and leaders, and provides interfaces supporting various visualizations and interactive data analysis using our software tool CONDOR. CONDOR was developed and tested over the last ten years at the MIT Center for Collective Intelligence, Dartmouth College, University of Cologne, and the University of Applied Sciences Northwestern Switzerland. CONDOR applies social network analysis to discover Collaborative Innovation Networks (COINs) (Gloor 2006). To put it in other words, CONDOR is discovering trends through the identification of trendsetters by measuring their social network position, and filtering their discussion by contents, creating subnetworks of people talking about the same topics. At the same time CONDOR  is combining these efforts with automatic sentiment analysis.

3 Infospheres

Our coolhunting approach combines 4 dimensions: people (actors), contents (facts), sentiment, and time series analysis. Coolhunting calculates predictive models by combining five unique components:  
(1) CONDOR finds trends by finding the trendsetters. The key differentiator of our approach is the use of social networking techniques to determine those actors that are most influential.  Our approach weighs every instance of a search term with the social networking position of the actor using the search term. 

(2) CONDOR uses implicit data to interpret “honest signals.”  One of the core concepts of CONDOR’s analysis philosophy is that the metadata surrounding the explicit data associated with a posting (e.g. author, date, text) is as important as the data that expresses the hidden intent of the author, frequently without him/her actually being aware of it. For example, Wikipedia not only provides content, but these articles are linked together, allowing users to easily view these explicit connections. Wikipedia has a great amount of hidden, latent value in the metadata surrounding these content pages: the authors (and what else they’ve authored), the course of activity (edits/time), the location of links in the content, the networks of links and authorship. The analysis of this metadata contributes greatly to the quality of the early discovery of events through the many types of context it provides.

(3) CONDOR segments the Web data into three information spheres: Crowd, Experts, and Swarms. It predicts societal trends based on Web buzz time series captured through identifying and weighing the appropriate data source in the three different information spheres. The crowd’s output is collected by analyzing the Web through the Google/Bing/Yahoo search indices, the experts’ output is collected through Wikipedia and scientific records such as PubMed, and the swarm is found in online forums such as IMDB or Yahoo Finance. The social network of actors and their honest signals are computed differently for each of the different information spheres.

(4) Content: Through automated text and sentiment analysis coolhunting analyzes if people talk positively or negatively about a certain trend, and what its key attributes are.

(5) Longitudinal trend observation: By continuously monitoring changes and applying customized longitudinal social network analysis algorithms, coolhunting generates trend curves and predictions for tomorrow’s trends in key blogs, Facebook and Twitter influencers, Wikipedia, and relevant forums.


Crowd (Twitter & Web)

Swarm (Wikipedia)

Expert (News, Blogs)

General overview

Megatrends, key people

Megatrends, key people

Megatrends, key people

Compare key markets/countries

What the crowd says in the US, Europe, Asia, etc

What the swarm says in the US, Europe, Asia, etc

What the experts says in the US, Europe, Asia, etc

Compare key competitors

Comparing related topics on twitter

Comparing related topics on Wikipedia

Comparing related topics on blogs

Coolhunting  identifies:
1.    What are information producers saying online in forums, Twitter, blogs, and Wikipedia
2.    In which different geographic regions
3.    Who are the opinion leaders
4.    What is the demographics of influencers
5.    What are the key Web sites

Relevant Topics

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For academic use only. Please, drop us a line if you plan to use it.

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