Browsing by Subject "Fact checking"
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Item Open Access Bias in Fact Checking?: An Analysis of Partisan Trends Using PolitiFact Data(2023-04-15) Colicchio, ThomasFact checking is one of many tools that journalists use to combat the spread of fake news in American politics. Like much of the mainstream media, fact checkers have been criticized as having a left-wing bias. The efficacy of fact checking as a tool for promoting honesty in public discourse is dependent upon the American public’s belief that fact checkers are in fact objective arbiters. In this way, discovering whether this partisan bias is real or simply perceived is essential to directing how fact checkers, and perhaps the mainstream media at large, can work to regain the trust of many on the right. This paper uses data from PolitiFact, one of the most prominent fact checking websites, to analyze whether or not this bias exists. Prior research has shown that there is a selection bias toward fact checking Republicans more often and that they on average receive worse ratings. However, few have examined whether this differential treatment can be attributed to partisan bias. While it is not readily apparent how partisan bias can be objectively measured, this paper develops and tests some novel strategies that seek to answer this question. I find that among PolitiFact’s most prolific fact checkers there is a heterogeneity in their relative ratings of Democrats and Republicans that may suggest the presence of partisanship.Item Open Access Computational Journalism: from Answering Question to Questioning Answers and Raising Good Questions(2015) Wu, YouOur media is saturated with claims of ``facts'' made from data. Database research has in the past focused on how to answer queries, but has not devoted much attention to discerning more subtle qualities of the resulting claims, e.g., is a claim ``cherry-picking''? This paper proposes a Query Response Surface (QRS) based framework that models claims based on structured data as parameterized queries. A key insight is that we can learn a lot about a claim by perturbing its parameters and seeing how its conclusion changes. This framework lets us formulate and tackle practical fact-checking tasks --- reverse-engineering vague claims, and countering questionable claims --- as computational problems. Within the QRS based framework, we take one step further, and propose a problem along with efficient algorithms for finding high-quality claims of a given form from data, i.e. raising good questions, in the first place. This is achieved to using a limited number of high-valued claims to represent high-valued regions of the QRS. Besides the general purpose high-quality claim finding problem, lead-finding can be tailored towards specific claim quality measures, also defined within the QRS framework. An example of uniqueness-based lead-finding is presented for ``one-of-the-few'' claims, landing in interpretable high-quality claims, and an adjustable mechanism for ranking objects, e.g. NBA players, based on what claims can be made for them. Finally, we study the use of visualization as a powerful way of conveying results of a large number of claims. An efficient two stage sampling algorithm is proposed for generating input of 2d scatter plot with heatmap, evalutaing a limited amount of data, while preserving the two essential visual features, namely outliers and clusters. For all the problems, we present real-world examples and experiments that demonstrate the power of our model, efficiency of our algorithms, and usefulness of their results.