Shareholder Activist Company Monitor

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Excerpt from 2009-2010 research project with Professor Anya Maria Schiffrin, Columbia University School of International and Public Affairs

Google Earth screen shot with a proof-of-concept for the proposed interface. Lines symbolize connections between producers and consumers of information. These are connections between the headquarters of Newmont Mining in Colorado, its gold mines in Ghana, Ghanaian NGOs, international NGOs, shareholder organizations, and media organizations.


In order to better serve the needs of NGOs, shareholders and scholars who do research on extractive industry companies in developing nations, I propose to build a platform that will serve as a media mapping company research portal for activists, shareholder, re- searchers and companies. This platform will aggregate media coverage in the form of XML feeds, perform data mining and filtering functions, visualize associations of publications with locations on a world map, and allow users to interact with the data by tagging articles and drawing connections between sources and receivers to analyze the flow of information. The platform can be built from freely available platform components such as Ushahidi, Swift River and Open Calais, bringing in data feeds from existing services such as Google News and I examined a case study example of the flow of information around two events — a shareholder resolution filed against Newmont Gold Mining Corporation and a cyanide leak at its Ahafo mine in Ghana. The report concluded with recommendations for the development of a Media Mapping Company Monitoring Research Portal for shareholder activists.


The platform will help researchers — reporters, shareholder activists, investors, and NGO’s; — find information about social and environmental issues that arise with extractive industry multinational corporations that do business in Africa. In addition, a feature will be implemented that allows researchers to connect sources and receivers of information in order to visualize and analyze the flow of information between various sources, for instance between journalists, local/international NGOs and shareholders of extractive industry multinationals. Studying these patterns will help identify gaps and help find ways to make media from a large variety of sources more accessible. Create Information Flow pattern using a variety of data points.

Proposed Workflow

  1. Either directly access (geotagged)archives of news media using feeds or batch process archives of newsmedia( Use Ushahidi for event categorization and mapping, Swift River for verification.
  2. Build OWL ontology (then use SPARQL for querying?)
  3. On the fly — definition of sources/receivers of information and topic categories (e.g. company names, NGOs, media organizations)
  4. Make standardized Bibliographic/other ontologies available as appropriate
  5. Allow for both RSS or other automated data feeds, raw text and manually entered or imported metadata(COinS / Mendeley / Zotero feed
  6. Mine and tag data with tags set up with keyword queries, in this case for example “Newmont” “Ghana” “Resolution” and “Shareholder” (perhaps using Open Calais for additional tags)
  7. Map geotagged newsmedia and animate over time, coloring visualizations of articles by keywords, visualizing “flows” of information by using the Z axis. Using 3D to prevent edge crossings. Imagine a combination of MIT’s “NY talk exchange” and Pavel Risenberg’s “Nooblast”(uses Processing).

January 2011 follow up

Unfortunately, the SIPA project never materialized as resources were constrained. Then…about a year later, while browsing for human rights maps for a GIS course, I encountered a paper by Tomaszewski, who built an app called the “Context Discovery Application” , this reminded me of the shareholder activist company monitor — I’d be curious to try this CDA sometime to see how it would work for shareholder activists.


Tomaszewski (2010)

image from CDA project flyer, "Results of a CDA search on news stories related to West Nile Virus in Pennsylvania and shown in Google EarthTM: Each place found in the search is connected to the origin point using a line. The thickness of the line indicates the number of times a place was referenced in the story, point symbols represent the geographic scale of the entity found (town, county etc.). The transparency of the line indicates how old the story is relative to the time when the geovisualization was created. These approaches are used to give a quick overview of the information returned before removing unneeded information."

Here’s a video of Tomascewski’s latest development called SensePlace: