This article advances a narrative approach to internet memes conceptualized as partial stories that reflect, capture, and contribute to wider storylines. One key difficulty in studying memes as stories rests in the fact that narrative analysis often focuses on plot at the expense of roles and characters. Building on narrative psychology and, in particular, transactional and linguistic types of analysis, we propose a typology of character roles—Persecutor, Victim, Hero, and Fool—that is useful to uncover scenarios within memes and, thus, reveal their intrinsic narrative structure. We apply this framework to the analysis of political narratives embedded within 241 coronavirus memes systematically sampled from Reddit’s r/CoronavirusMemes between January and May 2020. Five main scenarios or storylines emerged from this analysis, the first four depicting a more or less common narrative of protest against the incompetence and/or malevolence of the political class—from Donald Trump and the Republicans in the United States to Boris Johnson and the Conservatives in the United Kingdom and, finally, to politicians in Asia such as Xi Jinping and Kim Jong-un—while the fifth scenario brought to the fore social categories made salient by the pandemic and focused especially on the relation between people who respect and don’t respect measures. The psychological, social, and political implications of these scenarios in relation to the pandemic are discussed, as well as the broader consequences of studying memes as narrative structures.
Farage or the French Resistance of Le Pen -but because it seems more and more likely that they are bringing us back to the past as it actually happened -a past where populism successfully brought nationalist leaders to power. In this context, it seems particularly crucial to understand how we relate to our history, how we learn from it and the consequences it may have for the world we live in. These are the questions this special issue explores by adopting a cultural psychological perspective on collective memory -the lay representations of history -and proposing both theoretical and empirical contributions.In this editorial, we will try to first make the case for the political and social importance of collective memory. Second, we will argue why theoretical discussions -not just empirical research -are necessary to tackle these issues. Third, we will discuss the role we believe, cultural psychology should play in the current context and the dangers of turning it into a field disconnected from social and political realities. Finally, we will present the contents of this issue and how we hope it tackles some of the problems raised in this editorial. The social and political importance of collective memoryStating that collective memory studies particularly matter in today's post-truth world could be seen as a mere rhetorical move -after all, doesn't all expertise
There has been much hype, over the past few years, about the recent progress of artificial intelligence (AI), especially through machine learning. If one is to believe many of the headlines that have proliferated in the media, as well as in an increasing number of scientific publications, it would seem that AI is now capable of creating and learning in ways that are starting to resemble what humans can do. And so that we should start to hope – or fear – that the creation of fully cognisant machine might be something we will witness in our life time. However, much of these beliefs are based on deep misconceptions about what AI can do, and how. In this paper, I start with a brief introduction to the principles of AI, machine learning, and neural networks, primarily intended for psychologists and social scientists, who often have much to contribute to the debates surrounding AI but lack a clear understanding of what it can currently do and how it works. I then debunk four common myths associated with AI: 1) it can create, 2) it can learn, 3) it is neutral and objective, and 4) it can solve ethically and/or culturally sensitive problems. In a third and last section, I argue that these misconceptions represent four main dangers: 1) avoiding debate, 2) naturalising our biases, 3) deresponsibilising creators and users, and 4) missing out some of the potential uses of machine learning. I finally conclude on the potential benefits of using machine learning in research, and thus on the need to defend machine learning without romanticising what it can actually do.
Misinformation has been a pressing issue since the beginning of the COVID-19 pandemic, threatening our ability to effectively act on the crisis. Nevertheless, little is known about the actual effects of fake news on behavioural intentions. Does exposure to or belief in misinformation about COVID-19 vaccines affect people’s intentions to receive such a vaccine? This paper attempts to address this question via three preregistered experiments (N = 3463). In Study 1, participants (n = 1269) were exposed to fabricated pro- or anti-vaccine information or to neutral true information, and then asked about their intentions to get vaccinated. In Study 2, participants (n = 646) were exposed to true pro- and anti-vaccine information, while Study 3 (n = 1548) experimentally manipulated beliefs in novel misinformation about COVID-19 vaccines by increasing exposure to the information. The results of these three studies showed that exposure to false information about the vaccines had little effect on participants’ intentions to get vaccinated, even when multiple exposures led them to believe the headlines to be more accurate. An exploratory meta-analysis of studies 1 and 3, with a combined sample size of 2683, showed that exposure to false information both supporting and opposing COVID-19 vaccines actually increased vaccination intentions, though the effect size was very small. We conclude by cautioning researchers against equating exposure to misinformation or perceived accuracy of false news with actual behaviours.
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