Presentation is loading. Please wait.

Presentation is loading. Please wait.

San Francisco Bay Area News Ecology Daniel Ramos CS790G Fall 2010.

Similar presentations


Presentation on theme: "San Francisco Bay Area News Ecology Daniel Ramos CS790G Fall 2010."— Presentation transcript:

1 San Francisco Bay Area News Ecology Daniel Ramos CS790G Fall 2010

2 Outline Introduction Introduction Related Work Related Work Methodology Methodology Conclusion Conclusion

3 Evolution of News Keeping current on events has changed radically. Keeping current on events has changed radically. “Mass Media” “Mass Media” Radio, television, newspapers, magazines, books, etc. Radio, television, newspapers, magazines, books, etc. “Networked Media” “Networked Media” Based on the Internet. Based on the Internet. Collaborative and global in nature. Collaborative and global in nature.

4 Role of Journalists Traditional Reporting Traditional Reporting Journalists worked mostly alone and locally. Journalists worked mostly alone and locally. National/International news from other organizations (Associated Press). National/International news from other organizations (Associated Press). Network Media Reporting Network Media Reporting Journalists can easily talk to others across the globe. Journalists can easily talk to others across the globe. They can freelance for many news outlets. They can freelance for many news outlets.

5 Project Goals Use network analysis to characterize a “news ecosystem”. Use network analysis to characterize a “news ecosystem”. Traditional news outlets are shrinking. Traditional news outlets are shrinking. Start-up news organizations are quickly forming. Start-up news organizations are quickly forming. Using the San Francisco Bay area. Using the San Francisco Bay area. Transitioning from “mass media” to “networked media”? Transitioning from “mass media” to “networked media”?

6 Tracing Ties Between news organizations Between news organizations Between reporters and anybody else Between reporters and anybody else Between users and news organizations Between users and news organizations Potentially measure the density of ties Potentially measure the density of ties

7 Related Work I. Himelboim, “The International Network Structure of News Media: An Analysis of Hyperlinks Usage in News Web sites” I. Himelboim, “The International Network Structure of News Media: An Analysis of Hyperlinks Usage in News Web sites” Analyzed 6,298 foreign news stories. Analyzed 6,298 foreign news stories. 223 news web sites. 223 news web sites. 73 countries. 73 countries. Studied use of external hyperlinks. Studied use of external hyperlinks.

8 Related Work (Cont.) Found news sites rarely used external hyperlinks. Found news sites rarely used external hyperlinks. Only 6% had one or more. Only 6% had one or more. If they did, it followed patterns based on: If they did, it followed patterns based on: Preferential Attachment Theorem Preferential Attachment Theorem World System Theory World System Theory Conclusions Conclusions Journalists trained to not reveal sources. Journalists trained to not reveal sources. Distrust for outside sources. Distrust for outside sources. Lead users away from the news site. Lead users away from the news site.

9 Related Work (Cont.) Gordon, Contractor, and Johnson, “Linking Audiences to News and Information: A Network Analysis of Chicago Websites” Gordon, Contractor, and Johnson, “Linking Audiences to News and Information: A Network Analysis of Chicago Websites” Collected a list of 277 “seed sites” Collected a list of 277 “seed sites” Categorized the sites into: legacy, legacy-affiliated, micropublisher, organization/institution, national brand, and service. Categorized the sites into: legacy, legacy-affiliated, micropublisher, organization/institution, national brand, and service. Used a web crawler to examine links. Used a web crawler to examine links.

10 Related Work (Cont.) Conclusion Conclusion Organizations are authorities. Organizations are authorities. Micropublishers and organizations are hubs. Micropublishers and organizations are hubs. Organizations are intermediaries and switchboards. Organizations are intermediaries and switchboards. Organizations are most prestigious. Organizations are most prestigious.

11 Methodology Use network theory to study three main ties: Use network theory to study three main ties: News Organization to News Organization News Organization to News Organization Journalists to the “Community” Journalists to the “Community” Commenters to News Organizations Commenters to News Organizations

12 News Organization Ties Compiled a list of 143 different web sites we feel encompasses our news ecosystem. Compiled a list of 143 different web sites we feel encompasses our news ecosystem. Traditional news outlets web sites Traditional news outlets web sites Blogs Blogs Other non-traditional (e.g. news aggregators) Other non-traditional (e.g. news aggregators) Use a web crawler to crawl the seed sites and record all external links to a database. Use a web crawler to crawl the seed sites and record all external links to a database. Each site will be its own network at first. Each site will be its own network at first.

13 News Organization Ties Won’t record duplicates, but will record number of references. Won’t record duplicates, but will record number of references. News Organization graphs will be generated from the database. News Organization graphs will be generated from the database. Nodes are websites. Nodes are websites. Edges are directional hyperlink references. Edges are directional hyperlink references. Edge weights are number of times linked. Edge weights are number of times linked.

14 News Organization Ties Categorizing Links Categorizing Links First pass will be try to categorize news sites if they match the seed site list. First pass will be try to categorize news sites if they match the seed site list. Second pass will require manual human coding Second pass will require manual human coding Remove all links deemed not a news organization Remove all links deemed not a news organization Merge all independent networks together. Merge all independent networks together.

15 News Organization Ties Metrics Metrics Degree (both in and out) Degree (both in and out) Determine hubs and authorities. Determine hubs and authorities. Betweenness Betweenness Determine which sites link otherwise unconnected sites. Determine which sites link otherwise unconnected sites. Centrality Centrality Determine which sites are important to the network. Determine which sites are important to the network.

16 Journalists to the Community Determine the linking patterns of reporters who publish on the seed sites. Determine the linking patterns of reporters who publish on the seed sites. Traditional writing versus using the web to its full potential. Traditional writing versus using the web to its full potential. Use a web crawler to crawl the seed sites and record all external links to a database. Use a web crawler to crawl the seed sites and record all external links to a database. Focus on only a few larger sites. Focus on only a few larger sites. No standard for bylines of article authors. No standard for bylines of article authors. Requires site specific crawling rules. Requires site specific crawling rules.

17 Journalists to the Community Journalist graph will be generated from the database. Journalist graph will be generated from the database. Forms a bipartite graph. Forms a bipartite graph. Nodes are authors and sites. Nodes are authors and sites. Edges are an author linking a site. Edges are an author linking a site. Some manual human coding required to remove non-community sites. Some manual human coding required to remove non-community sites.

18 Journalists to the Community Metrics Metrics Degree (both for journalists and sites) Degree (both for journalists and sites) Determine which authors cite more often Determine which authors cite more often Determine which sites are referenced most often. Determine which sites are referenced most often.

19 Commenters to News Organizations Determine the patterns of users who comment on stories the seed sites. Determine the patterns of users who comment on stories the seed sites. How do they interact with news organizations and each other? How do they interact with news organizations and each other? Use a web crawler to crawl the seed sites and record all commenters to a database. Use a web crawler to crawl the seed sites and record all commenters to a database. Focus on only a few larger sites. Focus on only a few larger sites. No standard for user comments and accounts. No standard for user comments and accounts. Requires site specific crawling rules. Requires site specific crawling rules.

20 Commenters to News Organizations Commenter graph will be generated from the database. Commenter graph will be generated from the database. Forms a bipartite graph. Forms a bipartite graph. Each site will be its own graph. Each site will be its own graph. Nodes are commenters and news stories. Nodes are commenters and news stories. Edges are a user commenting on a story. Edges are a user commenting on a story. Might require some manual human coding to remove spam & bots. Might require some manual human coding to remove spam & bots.

21 Commenters to News Organizations Metrics Metrics Degree (both for users and stories) Degree (both for users and stories) Determine which users comment most. Determine which users comment most. Determine which stories garner most attention. Determine which stories garner most attention. Transform to a 1-mode network leaving users. Transform to a 1-mode network leaving users. Edge weights are how many of the same stories two users commented on. Edge weights are how many of the same stories two users commented on. Do users form clusters and communities? Do users form clusters and communities?

22 Tools WebSPHINX WebSPHINX Pajek Pajek GUESS or Gephi GUESS or Gephi

23 Conclusion Is news media transitioning because of new technologies like it has in the past? Is news media transitioning because of new technologies like it has in the past? How is the Internet affecting news outlets, journalists, and readers? How is the Internet affecting news outlets, journalists, and readers? Hopefully, network theory and analysis can help find these answers. Hopefully, network theory and analysis can help find these answers.

24 References [1] I. Himelboim, "The International Network Structure of News Media: An Analysis of Hyperlinks Usage in News Web sites," Journal of Broadcasting & Electronic Media, Volume 54, Issue 3, pp. 373-390, July 2010. [1] I. Himelboim, "The International Network Structure of News Media: An Analysis of Hyperlinks Usage in News Web sites," Journal of Broadcasting & Electronic Media, Volume 54, Issue 3, pp. 373-390, July 2010. [2] R. Gordon, N. Contractor, and Z. P. Johnson, "Linking Audiences to News and Information: A Network Analysis of Chicago Websites," unpublished, http://www.cct.org/sites/cct.org/files/CNM_LinkingAudien ces1.pdf [2] R. Gordon, N. Contractor, and Z. P. Johnson, "Linking Audiences to News and Information: A Network Analysis of Chicago Websites," unpublished, http://www.cct.org/sites/cct.org/files/CNM_LinkingAudien ces1.pdf http://www.cct.org/sites/cct.org/files/CNM_LinkingAudien ces1.pdf http://www.cct.org/sites/cct.org/files/CNM_LinkingAudien ces1.pdf


Download ppt "San Francisco Bay Area News Ecology Daniel Ramos CS790G Fall 2010."

Similar presentations


Ads by Google