Browsing by Person "Quayle, Michael"

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Not our kind of crowd! How partisan bias distorts perceptions of political bots on Twitter (now X)
(2025) Lüders, Adrian; Reiss, Stefan; Dinkelberg, Alejandro; MacCarron, Pádraig; Quayle, Michael; Lüders, Adrian; School of Communication Studies, University of Hohenheim, Stuttgart, Germany; Reiss, Stefan; Psychology Department, University of Salzburg, Salzburg, Austria; Dinkelberg, Alejandro; Department of Mathematics and Statistics, University of Limerick, Limerick, Ireland; MacCarron, Pádraig; Department of Mathematics and Statistics, University of Limerick, Limerick, Ireland; Quayle, Michael; Centre for Social Issues Research, University of Limerick, Limerick, Ireland
Social bots, employed to manipulate public opinion, pose a novel threat to digital societies. Existing bot research has emphasized technological aspects while neglecting psychological factors shaping human–bot interactions. This research addresses this gap within the context of the US‐American electorate. Two datasets provide evidence that partisanship distorts (a) online users' representation of bots, (b) their ability to identify them, and (c) their intentions to interact with them. Study 1 explores global bot perceptions on through survey data from N = 452 Twitter (now X) users. Results suggest that users tend to attribute bot‐related dangers to political adversaries, rather than recognizing bots as a shared threat to political discourse. Study 2 ( N = 619) evaluates the consequences of such misrepresentations for the quality of online interactions. In an online experiment, participants were asked to differentiate between human and bot profiles. Results indicate that partisan leanings explained systematic judgement errors. The same data suggest that participants aim to avoid interacting with bots. However, biased judgements may undermine this motivation in praxis. In sum, the presented findings underscore the importance of interdisciplinary strategies that consider technological and human factors to address the threats posed by bots in a rapidly evolving digital landscape.
Response Item Network (ResIN): A network-based approach to explore attitude systems
(2024) Carpentras, Dino; Lueders, Adrian; Quayle, Michael; Carpentras, Dino; Computational Social Science, ETH Zürich, Zürich, Switzerland; Lueders, Adrian; Department of Communication Science, University of Hohenheim, Stuttgart, Germany; Quayle, Michael; Department of Psychology, University of Limerick, Limerick, Ireland
Belief network analysis (BNA) refers to a class of methods designed to detect and outline structural organizations of complex attitude systems. BNA can be used to analyze attitude-structures of abstract concepts such as ideologies, worldviews, and norm systems that inform how people perceive and navigate the world. The present manuscript presents a formal specification of the Response-Item Network (or ResIN), a new methodological approach that advances BNA in at least two important ways. First, ResIN allows for the detection of attitude asymmetries between different groups, improving the applicability and validity of BNA in research contexts that focus on intergroup differences and/or relationships. Second, ResIN’s networks include a spatial component that is directly connected to item response theory (IRT). This allows for access to latent space information in which each attitude (i.e. each response option across items in a survey) is positioned in relation to the core dimension(s) of group structure, revealing non-linearities and allowing for a more contextual and holistic interpretation of the attitudes network. To validate the effectiveness of ResIN, we develop a mathematical model and apply ResIN to both simulated and real data. Furthermore, we compare these results to existing methods of BNA and IRT. When used to analyze partisan belief-networks in the US-American political context, ResIN was able to reliably distinguish Democrat and Republican attitudes, even in highly asymmetrical attitude systems. These results demonstrate the utility of ResIN as a powerful tool for the analysis of complex attitude systems and contribute to the advancement of BNA.