89 research outputs found
Weaponizing white thymos:Flows of rage in the online audiences of the alt-right
The alt-right is a growing radical right-wing network that is particularly effective at mobilizing emotion through digital communications. Introducing âwhite thymosâ as a framework to theorize the role of rage, anger, and indignation in alt-right communications, this study argues that emotive communication connects alt-right users and mobilizes white thymos to the benefit of populist radical right politics. By combining linguistic, computational, and interpretive techniques on data collected from Twitter, this study demonstrates that the alt-right weaponizes white thymos in three ways: visual documentation of white victimization, processes of legitimization of racialized pride, and reinforcement of the rectitude of rage and indignation. The weaponization of white thymos is then shown to be central to the culture of the alt-right and its connectivity with populist radical right politics
Exploring Patterns in November Snowfall Using GIS Mapping and Analysis
A Geographic Information System (GIS) analysis was generated to examine patterns in November snowfall climatology for 47 observing stations surrounding Lake Michigan. Snowfall data compiled for each year from 1950 to 2012 was mapped using an interpolation method called kriging, which is a spatial analyst tool. Some of the data mapped includes average snowfall for the Lake Michigan region, number of days with measurable snowfall, number of days with snow on the ground, and correlation between snowfall and temperature. Overall, these maps show maximums in north-central and southwestern Michigan decreasing to the south, as expected. The first three loadings of principal component analysis were also mapped using the same data. Principle Component 1 (PC1) explains most of the data variance and shows that northern and southwestern Michigan do not correlate with areas on the western side of the lake, illustrating that the amount of snow that northern Michigan receives doesn\u27t correspond to the amount of snow that Chicago receives and vice versa. Once this primary pattern is captured, the second principal component shows an inverse relationship between the main NW and SW lake-effect regions in Michigan and the third principal component reflects the influence of snowfall from synoptic systems NW of Lake Michigan
A âEurope des Nationsâ:Far right imaginative geographies and the politicization of cultural crisis on Twitter in Western Europe
Contestation over European integration has been widely studied in the rhetoric of parties, leaders, and movements on the far right in a variety of media. Focusing on Twitter use by far right actors in Western Europe, we apply corpus-aided discourse analysis to explore how imaginative geographies are used to politicize Europe among their digital publics. We find that the idea of a crisis of cultural identity pervades imaginaries of Europe amongst far right digital publics. While Europe is presented as facing a crisis of cultural identity, we find that the far right articulates an aspirational imaginary of Europe, the âEurope des Nationsâ that rejects liberal-democratic pluralism in the EU and the âestablishmentâ. We find that the contestation of Europe in far right digital publics relies on a crisis of cultural identity, representing a translation of Nouvelle Droite imaginaries of Europe into the social media space
Exploring Patterns in November Snowfall Using GIS Mapping and Analysis
A Geographic Information System (GIS) analysis was generated to examine patterns in November snowfall climatology for 47 observing stations surrounding Lake Michigan. Snowfall data compiled for each year from 1950 to 2012 was mapped using an interpolation method called kriging, which is a spatial analyst tool. Some of the data mapped includes average snowfall for the Lake Michigan region, number of days with measurable snowfall, number of days with snow on the ground, and correlation between snowfall and temperature. Overall, these maps show maximums in north-central and southwestern Michigan decreasing to the south, as expected. The first three loadings of principal component analysis were also mapped using the same data. Principle Component 1 (PC1) explains most of the data variance and shows that northern and southwestern Michigan do not correlate with areas on the western side of the lake, illustrating that the amount of snow that northern Michigan receives doesn\u27t correspond to the amount of snow that Chicago receives and vice versa. Once this primary pattern is captured, the second principal component shows an inverse relationship between the main NW and SW lake-effect regions in Michigan and the third principal component reflects the influence of snowfall from synoptic systems NW of Lake Michigan
Does Campaigning on Social Media Make a Difference? Evidence from candidate use of Twitter during the 2015 and 2017 UK Elections
Social media are now a routine part of political campaigns all over the
world. However, studies of the impact of campaigning on social platform have
thus far been limited to cross-sectional datasets from one election period
which are vulnerable to unobserved variable bias. Hence empirical evidence on
the effectiveness of political social media activity is thin. We address this
deficit by analysing a novel panel dataset of political Twitter activity in the
2015 and 2017 elections in the United Kingdom. We find that Twitter based
campaigning does seem to help win votes, a finding which is consistent across a
variety of different model specifications including a first difference
regression. The impact of Twitter use is small in absolute terms, though
comparable with that of campaign spending. Our data also support the idea that
effects are mediated through other communication channels, hence challenging
the relevance of engaging in an interactive fashion
Smart Technology and the Emergence of Algorithmic Bureaucracy:Artificial Intelligence in UK Local Authorities
In recent years, local authorities in the UK have begun to adopt a variety of âsmartâ technological changes to enhance service delivery. These changes are producing profound impacts on the structure of public administration. Focusing on the particular case of artificial intelligence, specifically autonomous agents and predictive analytics, a combination of desk research, a survey questionnaire, and interviews were used to better understand the extent and nature of these changes in local government. Findings suggest that local authorities are beginning to adopt smart technologies and that these technologies are having an unanticipated impact on how public administrators and computational algorithms become imbricated in the delivery of public services. This imbrication is described as algorithmic bureaucracy and it provides a framework within which to explore how these technologies transform both the socioâtechnical relationship between workers and their tools, as well as the ways that work is organized in the public sector
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