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This paper demonstrates a framework of processes for identifying potential witnesses of events from evidence they post to social media. The research defines original evidence models for micro-blog content sources, the relative uncertainty of different evidence types, and models for testing evidence by combination. Methods to filter and extract evidence using automated and semi-automated means are demonstrated using a Twitter case study event. Further, an implementation to test extracted evidence using Dempster Shafer Theory of Evidence are presented. The results indicate that the inclusion of evidence from micro-blog text and linked image content can increase the number of micro-bloggers identified at events, in comparison to the number of micro-bloggers identified from geotags alone. Additionally, the number of micro-bloggers that can be tested for evidence corroboration or conflict, is increased by incorporating evidence identified in their posting history. [ABSTRACT FROM AUTHOR]

This research investigates the use of image category classification to distinguish images posted to social media that are Witness Accounts of an event. Only images depicting observations of the event, captured by micro-bloggers at the event, are considered Witness Accounts. Identifying Witness Accounts from social media is important for services such as news, marketing and emergency response. Automated image category classification is essential due to the large number of images on social media and interest in identifying witnesses in near real time. This paper begins research of this emerging problem with an established procedure, using a bag-of-words method to create a vocabulary of visual words and classifier trained to categorize the encoded images. In order to test the procedure, a set of images were collected for case study events, Australian Football League matches, from Twitter. Evaluation shows an overall accuracy of 90% and precision and recall for both classes exceeding 83%. [ABSTRACT FROM AUTHOR]

Evaluates settlement services for immigrant women in Toronto, Ontario. Emphasis on the geographical distribution of immigrants and agencies offering settlement services; Contribution of nonprofit sector on the service needs of immigrants; Impact of immigrants' location on accessibility to settlement services.

Immigration since World War 2 has been a primary engine of economic, social, and cultural change in Canada. Two of its important characteristics have been its 'urban' character and the non-European origins of immigrants since the 1960s. The Toronto Census Metropolitan Area (CMA) has been a major destination for those immigrants who have entered the self-employed sector of the economy in ever-larger numbers. The authors focus on the barriers and challenges experienced by the Polish, Portuguese, Caribbean, Korean, and Somali immigrants in the establishment and operation of their businesses in the Toronto CMA. With information collected through key-informant interviews, a questionnaire survey, and focus groups, it is found that, despite the Canadian commitment to multiculturalism at all levels of government, visible-minority entrepreneurs still confront more barriers in their business practice than do non-visible-minority entrepreneurs, with access to financing being a persistent problem. Given the increasingly multicultural nature of major Canadian cities and the acknowledged role of immigrants as an engine of economic growth, the authors identify barriers to entrepreneurship among immigrants as an area of clear concern both for policymakers and for scholars, and suggest solutions to address this concern. [ABSTRACT FROM AUTHOR]