1 / 34

Internet Traffic Search and Ethnic Relations in Russia

Internet Traffic Search and Ethnic Relations in Russia. The Promise. Jeremy Ginsberg, Matthew H. Mohebbi, Rajan S. Patel, Lynnette Brammer, Mark S. Smolinski &  Larry Brilliant, “Detecting influenza epidemics using search engine query data,” Nature Vol 457, 19 February 2009

erika
Télécharger la présentation

Internet Traffic Search and Ethnic Relations in Russia

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Internet Traffic Search and Ethnic Relations in Russia

  2. The Promise Jeremy Ginsberg, Matthew H. Mohebbi, Rajan S. Patel, Lynnette Brammer, Mark S. Smolinski &  Larry Brilliant, “Detecting influenza epidemics using search engine query data,” Nature Vol 457, 19 February 2009 • Developed a method to analyze a large volume of Google queries to detect social behavior • Improved early detection of social behavior http://dx.doi.org/10.1038/nature07634

  3. Key components of the study • Google Trends: Internet search traffic measurement • Search query database: including IP addresses associated with each query • Prior years’ hard data on the behavior of interest (CDC) • Automated query-selection process: Identification of 45 highest-scoring search queries that fit the data from prior years • linear regression with 4-fold cross validation • Regional data aggregation: Maximizing fit probability and minimizing false positives • fit models to four 96-point subsets of the 128 points in each region. • Final model fitting: Tests in later years and for each individual state

  4. Key lessons • Sensitivity to the nature of social behavior • Sensitivity to variation across regions • Sensitivity to variation over time • Multiple measures • Data reduction

  5. Applications for Ethnic Conflict ResearchBuilding Block 1:Search Engines & Traffic Calculators

  6. Applications for Ethnic Conflict ResearchBuilding Block 2:Search Query Sources

  7. SOVA Information-Analytical Center Project: “The Language of Hate in the Mass Media”http://xeno.sova-center.ru/213716E/21398CB

  8. ДПНИ and other xenophobic websites

  9. Islamist Radical Websites

  10. Other sources • Internet buzz: chat forums, blogs • Tube traffic: cell phone clips, provocative song titles, video posts • Yandex search spin-offs (trends by words) • Focus groups • Expert groups

  11. Applications for Ethnic Conflict ResearchBuilding Block 3:Behavioral Data Sources

  12. Violence Data • SOVA Center (xenophobic violence; systemic interethnic communal violence) • UCSJ: Anti-Semitism • Кавказский узел (хроники по Ингушетии, Дагестану, Чечне) • Voinenet.com (weekly event summaries across the North Caucasus) • Data on protest attendance (police, HR NGOs) • GEDS archives (UMD)

  13. Voting datahttp://www.vybory.izbirkom.ru/region/region/izbirkom?action=show&root=1&tvd=100100021960186&vrn=100100021960181&region=0&global=1&sub_region=0&prver=0&pronetvd=null&vibid=100100021960186&type=233

  14. Potentially trackable phenomena • Mobilization: • Xenophobic group names; party names; leader names; event names • Intergroup hostility • Expressions of hate (derogatory group epithets) • Violence: • Proxies (weapons purchase, martial arts clubs, extremist forums names) • Minority groups’ resistance: • Specific ethnic group names + rights; HR NGOs; defense lawyers names, etc • Trust in government/state capacity: • Legal texts, statutes (Art. 282), police measures

More Related