Truth and Lies: Deception and Cognition on the Internet

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    What Am I Reading?: Article-style Native Advertisements in Canadian Newspapers
    ( 2019-01-08) Cornwell, Sarah ; Rubin, Victoria L.
    Native ads are ubiquitous in the North American digital news context. Their form, content and presentational style are practically indistinguishable from regular news editorials, and thus are often mistaken for informative content by newsreaders. This advertising practice is deceptive, in that it exploits loopholes in human digital literacy. Despite this, it is flourishing as a lucrative digital news advertising format. This paper documents and compares the 2018 Canadian news editorial writing and advertising practices in an effort to highlight their similarities and differences for potential automatic detection and categorization. We collected 10 native ads and 10 editorial pieces from 4 Canadian newspapers. The 80 analyzed articles consisted of 40 native ads content-matched to editorials in the same newspaper. The individually-matched pairs and overall practices in the 2 groups were content-analyzed and compared. Native ads did not differ much from editorial articles in content but were likely to be surrounded by different types of advertising. In addition, advertisement labelling practices were inconsistent across national papers. We call for increased efforts in regulation and automatic detection of convert advertising by a more nuanced categorization and their more explicit labeling in the digital news.
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    Creating Task-Generic Features for Fake News Detection
    ( 2019-01-08) Olivieri, Alex ; Shabani, Shaban ; Sokhn, Maria ; Cudré-Mauroux, Philippe
    Information spreads at a pace never seen before on online platforms, even when this information is fake. Fake news can have substantial impact, for instance when it concern politics and influences the results of legislations or elections. Finding a methodology to verify if some piece of news is true or false is hence essential. In this work, we propose a methodology to create task-generic features that are paired with textual features in order to detect fake news. Task-generic features are created by elaborating on metadata attached to answers from Google’s search engine, and by using crowdsourcing for missing values. We experimentally validate our method on a dataset for fake news detection based on the PolitiFact website. Our results show an improvement in F1-Score of 3% over the state of the art, which is significant for a 6-class task.
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    Factual or Believable? Negotiating the Boundaries of Confirmation Bias in Online News Stories
    ( 2019-01-08) Murungi, David ; Yates, David ; Purao, Sandeep ; Yu, Joy ; Zhan, Ruiting
    We examine the fake news phenomenon from a fresh perspective. Instead of assessing the factuality of news claims, our work explores the impact of these claims on reader beliefs. With the 2017 Alabama senate race as the empirical context, we examine how readers on both sides of the political spectrum evaluate online news stories considering their preconceived beliefs and values. Our analysis builds on concepts from argument and social representations theories to explore the role of argumentation in this process. We focus on detecting arguments in reader comments to depict challenges involved in reader consideration of newsworthy events and news stories. A key finding of the paper is that readers from both sides of the political spectrum appear to engage in similar strategies to confirm or negotiate acceptance or rejection of claims. The paper contributes to theory by depicting social representation as a process that mediates conflict in belief structures. We conclude by speculating about possibilities for future work, such as designing behavioral and technological interventions that can supplement fact-checking. An important goal here is to improve how we, in the presence of our biases, collectively consume online news stories and engage in the discourse that surrounds them.
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