Increasingly, Instagram is discussed as a site for misinformation, inau-thentic activities, and polarization, particularly in recent studies aboutelections, the COVID-19 pandemic and vaccines. In this study, we havefound a different platform. By looking at the content that receives themost interactions over two time periods (in 2020) related to three U.S.presidential candidates and the issues of COVID-19, healthcare, 5G andgun control, we characterize Instagram as a site of earnest (as opposedto ambivalent) political campaigning and moral support, with a rela-tive absence of polarizing content (particularly from influencers) andlittle to no misinformation and artificial amplification practices. Mostimportantly, while misinformation and polarization might be spreadingon the platform, they do not receive much user interaction.
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ConceptThe goal of the worksop/tutorial is to introduce participants to the fundamentals of Procedural Content Generation (PCG) based on generative grammars, have them experience an example of such a system first-hand, and discuss the potential of this approach for various areas of procedural content generation for games. The principles and examples are based on Ludoscope, a software tool developed at the HvA by Dr. Joris Dormans, e.a.Duration: 2 hoursOverviewWe will use the first 30 minutes to explain the basics of how to use generative grammars to generate levels. The principles of these grammars and model transformations will be demonstrated by means of the level generation system of Spelunky, which we have modeled in Ludoscope.Spelunky focuses solely on the generation of geometry, but grammar-based systems can also be used to transform more abstract concepts of level design into level geometry. In the next hour, the participants will be able to get some hands-on experience with Ludoscope. The assignment will be to generate a Mario-like level based on specific requirements, adapted to the interests of workshop participants.Finally, we are interested in the participants’ evaluation of this approach to PCG. We will use the last 20 minutes to discuss alternative techniques, and possible applications to other areas of PCG, like asset creation, scripting and game generation.Workshop participants are asked to bring a (PC) laptop to work on during the workshop, and are encouraged to work in pairs.
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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.