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bias is included as keyword or extra keyword in 0 datasets, 0 tools and 7 publications.
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|Title||Author(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|The business and politics of search engines: A comparative study of Baidu and Google's search results of Internet events in China||Jiang M.||New Media and Society||English||2014||Despite growing interest in search engines in China, relatively few empirical studies have examined their sociopolitical implications. This study fills several research gaps by comparing query results (N = 6320) from China's two leading search engines, Baidu and Google, focusing on accessibility, overlap, ranking, and bias patterns. Analysis of query results of 316 popular Chinese Internet events reveals the following: (1) after Google moved its servers from Mainland China to Hong Kong, its results are equally if not more likely to be inaccessible than Baidu's, and Baidu's filtering is much subtler than the Great Firewall's wholesale blocking of Google's results; (2) there is low overlap (6.8%) and little ranking similarity between Baidu's and Google's results, implying different search engines, different results and different social realities; and (3) Baidu rarely links to its competitors Hudong Baike or Chinese Wikipedia, while their presence in Google's results is much more prominent, raising search bias concerns. These results suggest search engines can be architecturally altered to serve political regimes, arbitrary in rendering social realities and biased toward self-interest.||0||0|
|Clustering editors of wikipedia by editor's biases||Nakamura A.
|Proceedings - 2013 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2013||English||2013||Wikipedia is an Internet encyclopedia where any user can edit articles. Because editors act on their own judgments, editors' biases are reflected in edit actions. When editors' biases are reflected in articles, the articles should have low credibility. However, it is difficult for users to judge which parts in articles have biases. In this paper, we propose a method of clustering editors by editors' biases for the purpose that we distinguish texts' biases by using editors' biases and aid users to judge the credibility of each description. If each text is distinguished such as by colors, users can utilize it for the judgments of the text credibility. Our system makes use of the relationships between editors: agreement and disagreement. We assume that editors leave texts written by editors that they agree with, and delete texts written by editors that they disagree with. In addition, we can consider that editors who agree with each other have similar biases, and editors who disagree with each other have different biases. Hence, the relationships between editors enable to classify editors by biases. In experimental evaluation, we verify that our proposed method is useful in clustering editors by biases. Additionally, we validate that considering the dependency between editors improves the clustering performance.||0||0|
|In Search of the Ur-Wikipedia: Universality, Similarity, and Translation in the Wikipedia Inter-Language Link Network||Morten Warncke-Wang
|WikiSym||English||August 2012||Wikipedia has become one of the primary encyclopaedic information repositories on the World Wide Web. It started in 2001 with a single edition in the English language and has since expanded to more than 20 million articles in 283 languages. Criss-crossing between the Wikipedias is an interlanguage link network, connecting the articles of one edition of Wikipedia to another. We describe characteristics of articles covered by nearly all Wikipedias and those covered by only a single language edition, we use the network to understand how we can judge the similarity between Wikipedias based on concept coverage, and we investigate the flow of translation between a selection of the larger Wikipedias. Our findings indicate that the relationships between Wikipedia editions follow Tobler's first law of geography: similarity decreases with increasing distance. The number of articles in a Wikipedia edition is found to be the strongest predictor of similarity, while language similarity also appears to have an influence. The English Wikipedia edition is by far the primary source of translations. We discuss the impact of these results for Wikipedia as well as user-generated content communities in general.||0||0|
|An analysis of systematic judging errors in information retrieval||Gabriella Kazai
|ACM International Conference Proceeding Series||English||2012||Test collections are powerful mechanisms for the evaluation and optimization of information retrieval systems. However, there is reported evidence that experiment outcomes can be affected by changes to the judging guidelines or changes in the judge population. This paper examines such effects in a web search setting, comparing the judgments of four groups of judges: NIST Web Track judges, untrained crowd workers and two groups of trained judges of a commercial search engine. Our goal is to identify systematic judging errors by comparing the labels contributed by the different groups, working under the same or different judging guidelines. In particular, we focus on detecting systematic differences in judging depending on specific characteristics of the queries and URLs. For example, we ask whether a given population of judges, working under a given set of judging guidelines, are more likely to consistently overrate Wikipedia pages than another group judging under the same instructions. Our approach is to identify judging errors with respect to a consensus set, a judged gold set and a set of user clicks. We further demonstrate how such biases can affect the training of retrieval systems.||0||0|
|A scourge to the pillar of neutrality: A WikiProject fighting systemic bias||Livingstone R.M.||WikiSym 2011 Conference Proceedings - 7th Annual International Symposium on Wikis and Open Collaboration||English||2011||WikiProject Countering Systemic Bias consists of a small group of English-language Wikipedia editors attempting to counterbalance Western-leaning content on the site. A population survey of members of this WikiProject is currently underway and will be followed by online interviews with select editors. This poster will present preliminary findings from the survey and interviews in order to understand how this group perceives bias on Wikipedia and how they work together to fight it.||0||0|
|Places on the map and in the cloud: Representations of locality and geography in Wikipedia||Livingstone R.M.||WikiSym 2011 Conference Proceedings - 7th Annual International Symposium on Wikis and Open Collaboration||English||2011||This poster will present preliminary results of a study that considers the efforts of WikiProject Countering Systemic Bias, a collective of editors dedicated to combating bias on the English-language Wikipedia. Through a content analysis comparing the project to a sample from the general population, the scope of this group's labor is gauged and discussed.||0||0|
|Places on the map and in the cloud: representations of locality and geography in Wikipedia||Randall M. Livingstone||WikiSym||English||2011||0||0|