Leerlingen groeien op in een wereld die permanent online is. Ze hebben toegang tot een grote hoeveelheid informatie en ze zijn constant online in interactie. Het onderwijs kan leerlingen opleiden tot mediawijze burgers.
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So-called fake news and problematic information on social media assume an increasingly important roles in political debate. Focusing on the (early) run-up to and aftermath of the 2020 U.S. presidential elections, this study examines the extent of the problematic information in the most engaged-with content and most active users in ‘political Twitter’. We demarcated three time spans, the first surrounding Super Tuesday (March 2-22, 2020), the second providing a snapshot of the aftermath of the elections and the run-up to both the Senate run-off elections in Georgia (December 24, 2020 – January 4, 2021) and the (unforeseen) Capitol Hill riots on January 6, 2021. In the third time span (March 10-21, 2021), when election activities had ceased, we examine the effects of Twitter’s deplatforming (or so-called purge) of accounts after the Capitol riots in January, 2021. In order to shed light on the magnitude of problematic information, we mapped shared sources, labelled them and assessed the actors engaged in their dissemination. It was found that overall, mainstream sources are shared more often than problematic ones, but the percentage of problematic sources was much higher in December compared to both the March, 2020 and 2021 periods. Significantly, (hyper)partisan sources are close to half of all sources shared in the first two periods, implying a robust presence of them on social media. By March 2021, both the share of problematic and of (hyper)partisan sources had decreased significantly, suggesting an impact from Twitter’s deplatforming actions. Additionally, highly active, problematic users (fake profiles, bots, or locked/suspended accounts) were found on both sides of the political spectrum, albeit more abundantly from conservative users.
Content Analysis has been developed within communication science as a technique to analyze bodies of text for features or (recurring) themes, in order to identify cultural indicators, societal trends and issues. And while Content Analysis has seen a tremendous uptake across scientific disciplines, the advent of digital media has presented new challenges to the demarcation and study of content. Within Content Analysis, different strategies have been put forward to grapple with these dynamics. And although these approaches each present ways forward for the analysis of web content, they do not yet regard the vast differences between web platforms that serve content, which each have their own ‘technicities,’ e.g. carry their own (often visually undisclosed) formats and formatting, and output their own results and rankings. In this dissertation I therefore develop Networked Content Analysis as a term for such techniques of Content Analysis that are adapted specifically to the study of networked digital media content. The case in question is climate change, one of the major societal challenges of our times, which I study on the web and with search engines, on Wikipedia as well as Twitter. In all, my contribution provides footing for a return to the roots of Content Analysis and at the same time adds to its toolkit the necessary web- and platform-specific research techniques for creating a fine-grained picture of the climate change debate as it takes place across platforms.
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