Markus Prior's research while affiliated with Princeton University and other places

... It is intriguing, however, since a number of recent studies challenge whether people truly believe the facts, they claim to, particularly in areas where partisans hold divergent views on what is true (Prior, Sood, & Khanna, 2015;Bullock, Gerber, Hill, & Huber, 2015). This is referred to as "expressive response," and it occurs when individuals "deliberately propagate misleading information" to bolster their political identification (Schaffner & Roche, 2018, p. 136). ...

... The work investigating the link between Islamophobia and fear related to Islam aligns with what some researchers call symbolic threatsthose threats that are concerned with cultural differences (Landmann et al., 2019;Stephan & Renfro, 2002;Stephan & Stephan, 2000). Fearing the loss of cultural homogeneity, national identity, and the values of an ingroup are expressions of symbolic threat (Davidov et al., 2020;Fetzer, 2000;Raijman & Semyonov, 2004;Raijman et al., 2008;Sniderman et al., 2004). These symbolic threats are associated with negative attitudes toward refugees and migrants (Esses et al., 2017;Landmann et al., 2019;Riek et al., 2006;Stephan, 2014). ...

... While these contributions serve as a natural reference point for our discussions in this chapter, we would like to underscore that we are not att empting a case comparison between the United States aft er 9/11 and Norway aft er 7/22. Existing research points to a number of contextual factors that may infl uence rallying dynamics, among them the characteristics of the event itself (Perrin & Smolek, 2009), the response of the government (Gaines, 2002;Skocpol, 2002), and media coverage (Prior, 2002), and controlling for this range of factors would be impossible based on the data that we possess. Instead, what we seek to achieve is an examination of the specifi c mechanism linking (or not linking) fear and institutional trust within the context of a high-trust society. ...

... First, the four items focused on strong avoidance behaviors. Using behavioral-based measures introduces fewer measurement errors than using perceptual-based measures (Prior, 2013). Second, in the context of social media where existing networks and algorithms largely influence information encounters (Bakshy, Messing, & Adamic, 2015), selective avoidance is conceptualized and operationalized as taking deliberate measures to cut off connections with attitude-challenging information/sources such as unfriending (Zhu, Skoric, & Shen, 2017) because such actions will substantially restrict information flows and prevent social interactions from the politically opposite side. ...

... Relatedly, the included studies rely on self-reports of media exposure and usage. It has often been noted that individuals have difficulties in accurately recalling their actual media use and consumption, which can often mean significant over and under reporting (Prior, 2009a(Prior, , 2009b(Prior, , 2012. While these are sometimes the best measures of individual exposure that are available (Slater, 2004), they do introduce a source of potential bias. ...

... Currently, we find that entertainment has permeated all public discourse and that there are political and media incentives that tend towards creating spectacle. This is profitable for the media insofar as it attracts audiences, and it is also profitable for politicians insofar as it attracts sympathisers and potential voters and guarantees their presence on the public agenda (Prior, 2003;Nguyen, 2012;Jebril, Albaek y De Vreese, 2013). The Mexican president's official Facebook account is an example of how the mediatisation of politics and the personalisation of politics take shape. ...

... television reports, websites, family, and friends). Because information behaviors in general and non-behaviors such as information avoidance, in particular, are difficult to remember adequately (Prior, 2009), references to certain sources are assessed as a crucial memory aid for adequate assessment of information avoidance. Participants' responses were reported on a 5-point Likerttype scale (1 "Does not apply at all" to 5 "Does apply fully") and mean indexes of sufficient reliability were calculated (see Table 2 for the descriptive measures and reliability scores per wave). ...

... Exposure to media contents lets users know, understand, and remember the object, situation, or event. People who like news in the media tend to be knowledgeable, and this knowledge ultimately encourages them to participate by voting in the election [30,31]. Moreover, many experts say that the main uniqueness of social media compared to other types of media is the interactivity that allows the two-way communication, and the users have a role as a consumer as well as a producer in the process of creating and sharing information [32]. ...

... Political elites habitually cite this research to explain the public's failure to heed facts consistent with the elite's policy proposal of the moment. 1 Another line of research, however, suggests that citizens are indeed capable of learning (Gerber and Green 1999;Howell and West 2009;Fishkin and Robert 2005). Sometimes with encouragements, and sometimes with small monetary incentives, citizens can absorb and retain complex new political information (Barnes et al. 2016;Fishkin 2005;Kuklinski and Quirk 2000); and factual receptivity can take place despite partisan differences (Bullock et al. 2013;Prior 2007;Prior et al. 2015). ...

... Beyond the influence of these relatively stable traits, community formation may also be driven by more volatile agent characteristics such as the extent to which individual agents share opinions or attitudes. For instance, researchers have sought to detail the role of social processes in the formation of political ideologies (Prior, 2013;Morales et al., 2015), religious extremism (Manrique et al., 2018;Badawy and Ferrara, 2018), healthcare choices (Kata, 2012), dietary preferences (Cole and Morgan, 2011;Reilly, 2016) and issues related to technologies and innovation (Coccia, 2016;Naranjo-Valencia et al., 2017). Analogously, engineers interested in designing or managing distributed multi-agent systems may have similar interests in understanding flows of influence within swarms of collaborating robots (Pitonakova et al., 2016a(Pitonakova et al., ,b, 2018 or populations of interacting software agents (Jacyno et al., 2009(Jacyno et al., , 2013. ...