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UXDesign_ReadingList.rdf
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<dcterms:abstract>The proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers have made it challenging to identify trustworthy news sources, thus increasing the need for computational tools able to provide insights into the reliability of online content. In this paper, we focus on the automatic identification of fake content in online news. Our contribution is twofold. First, we introduce two novel datasets for the task of fake news detection, covering seven different news domains. We describe the collection, annotation, and validation process in detail and present several exploratory analysis on the identification of linguistic differences in fake and legitimate news content. Second, we conduct a set of learning experiments to build accurate fake news detectors. In addition, we provide comparative analyses of the automatic and manual identification of fake news.</dcterms:abstract>
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<dcterms:abstract>Machine learning system aims to determine if an information outlet is accurate or biased.</dcterms:abstract>
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<dcterms:abstract>This year's report reveals new insights about digital news consumption based on a YouGov survey of over 75,000 online news consumers in 38 countries including South Africa for the first time. The report focuses on the progress on new paid online business models, trust and misinformation, the impact of populism, the shift to private messaging apps, and the rise of podcasts.</dcterms:abstract>
<dc:title>Journalism, Media, and Technology Trends and Predictions 2020</dc:title>
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<dc:identifier>DOI 10.1145/3173574.3173950</dc:identifier>
<dc:title>Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems</dc:title>
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<foaf:surname>Flintham</foaf:surname>
<foaf:givenName>Martin</foaf:givenName>
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<foaf:surname>Karner</foaf:surname>
<foaf:givenName>Christian</foaf:givenName>
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<foaf:surname>Bachour</foaf:surname>
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<foaf:surname>Creswick</foaf:surname>
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<foaf:surname>Gupta</foaf:surname>
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<foaf:surname>Moran</foaf:surname>
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<z:AutomaticTag><rdf:value>social media</rdf:value></z:AutomaticTag>
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<dc:subject>
<z:AutomaticTag><rdf:value>facebook</rdf:value></z:AutomaticTag>
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<z:AutomaticTag><rdf:value>fake news</rdf:value></z:AutomaticTag>
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<rdf:value>https://doi.org/10.1145/3173574.3173950</rdf:value>
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<bib:pages>1–10</bib:pages>
<dc:date>April 21, 2018</dc:date>
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<dcterms:abstract>In the so called 'post-truth' era, characterized by a loss of public trust in various institutions, and the rise of 'fake news' disseminated via the internet and social media, individuals may face uncertainty about the veracity of information available, whether it be satire or malicious hoax. We investigate attitudes to news delivered by social media, and subsequent verification strategies applied, or not applied, by individuals. A survey reveals that two thirds of respondents regularly consumed news via Facebook, and that one third had at some point come across fake news that they initially believed to be true. An analysis task involving news presented via Facebook reveals a diverse range of judgement forming strategies, with participants relying on personal judgements as to plausibility and scepticism around sources and journalistic style. This reflects a shift away from traditional methods of accessing the news, and highlights the difficulties in combating the spread of fake news.</dcterms:abstract>
<dc:title>Falling for Fake News: Investigating the Consumption of News via Social Media</dc:title>
<z:shortTitle>Falling for Fake News</z:shortTitle>
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<foaf:givenName>Gordon</foaf:givenName>
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<foaf:surname>Rand</foaf:surname>
<foaf:givenName>David G.</foaf:givenName>
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<rdf:value>Analytic thinking</rdf:value>
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<dcterms:abstract>Why do people believe blatantly inaccurate news headlines (“fake news”)? Do we use our reasoning abilities to convince ourselves that statements that align with our ideology are true, or does reasoning allow us to effectively differentiate fake from real regardless of political ideology? Here we test these competing accounts in two studies (total N = 3446 Mechanical Turk workers) by using the Cognitive Reflection Test (CRT) as a measure of the propensity to engage in analytical reasoning. We find that CRT performance is negatively correlated with the perceived accuracy of fake news, and positively correlated with the ability to discern fake news from real news – even for headlines that align with individuals’ political ideology. Moreover, overall discernment was actually better for ideologically aligned headlines than for misaligned headlines. Finally, a headline-level analysis finds that CRT is negatively correlated with perceived accuracy of relatively implausible (primarily fake) headlines, and positively correlated with perceived accuracy of relatively plausible (primarily real) headlines. In contrast, the correlation between CRT and perceived accuracy is unrelated to how closely the headline aligns with the participant’s ideology. Thus, we conclude that analytic thinking is used to assess the plausibility of headlines, regardless of whether the stories are consistent or inconsistent with one’s political ideology. Our findings therefore suggest that susceptibility to fake news is driven more by lazy thinking than it is by partisan bias per se – a finding that opens potential avenues for fighting fake news.</dcterms:abstract>
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