Terrorists Are Always Muslim but Never White: At the Intersection of Critical Race Theory and Propaganda

When you hear the word “terrorist,” who do you picture? Chances are, it is not a white person. In the United States, two common though false narratives about terrorists who attack America abound. We see them on television, in the movies, on the news, and, currently, in the Trump administration. The first is that “terrorists are always (brown) Muslims.” The second is that “white people are never terrorists.” Different strands of critical race theory can help us understand these two narratives. One strand examines the role of unconscious cognitive biases in the production of stereotypes, such as the stereotype of the “Muslim terrorist.” Another strand focuses on white privilege, such as the privilege of avoiding the terrorist label. These false narratives play a crucial role in Trump’s propaganda. As the critical race analysis uncovers, these two narratives dovetail with two constituent parts of propaganda: flawed ideologies and aspirational myths. Propaganda relies on preexisting false ideologies, which is another way to describe racist stereotyping. Propaganda also relies on certain ideals and myths, in this case, the myth of white innocence and white superiority. Thus, the Trump administration’s intentional invocation of both narratives amounts to propaganda in more than just the colloquial sense. Part I illustrates each of the two narratives. Part II then analyzes them through a critical race lens, showing how they map onto two strands of critical race theory. Next, Part III examines how these narratives simultaneously enable and constitute propaganda. Finally, Part IV argues that the propagation of these false narratives hurts the nation’s security

Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter

Individual happiness is a fundamental societal metric. Normally measured through self-report, happiness has often been indirectly characterized and overshadowed by more readily quantifiable economic indicators such as gross domestic product. Here, we examine expressions made on the online, global microblog and social networking service Twitter, uncovering and explaining temporal variations in happiness and information levels over timescales ranging from hours to years. Our data set comprises over 46 billion words contained in nearly 4.6 billion expressions posted over a 33 month span by over 63 million unique users. In measuring happiness, we use a real-time, remote-sensing, non-invasive, text-based approach---a kind of hedonometer. In building our metric, made available with this paper, we conducted a survey to obtain happiness evaluations of over 10,000 individual words, representing a tenfold size improvement over similar existing word sets. Rather than being ad hoc, our word list is chosen solely by frequency of usage and we show how a highly robust metric can be constructed and defended.Comment: 27 pages, 17 figures, 3 tables. Supplementary Information: 1 table, 52 figure

The Battle for #Baltimore: Networked Counterpublics and the Contested Framing of Urban Unrest

A growing body of research suggests that Twitter has become a key resource for networked counterpublics to intervene in popular discourse about racism and policing in the United States. At the same time, claims that online communication necessarily results in polarized echo chambers are common. In response to these seemingly contrary impulses in communication research, we explore how the contested online network comprised of tweets about the April 2015 protests in Baltimore, Maryland, evolved as users constructed meaning and debated questions of protest and race. We find that even within this highly polarized debate, counterpublic frames found widespread support on Twitter. Progressive racial justice messages were advanced, in part, by brokers who worked across polarized subcommunities in the network to build mutual understanding and model effective strategies for reconciling disparate accounts of protest events

Twitter reciprocal reply networks exhibit assortativity with respect to happiness

The advent of social media has provided an extraordinary, if imperfect, 'big data' window into the form and evolution of social networks. Based on nearly 40 million message pairs posted to Twitter between September 2008 and February 2009, we construct and examine the revealed social network structure and dynamics over the time scales of days, weeks, and months. At the level of user behavior, we employ our recently developed hedonometric analysis methods to investigate patterns of sentiment expression. We find users' average happiness scores to be positively and significantly correlated with those of users one, two, and three links away. We strengthen our analysis by proposing and using a null model to test the effect of network topology on the assortativity of happiness. We also find evidence that more well connected users write happier status updates, with a transition occurring around Dunbar's number. More generally, our work provides evidence of a social sub-network structure within Twitter and raises several methodological points of interest with regard to social network reconstructions.Comment: 22 pages, 21 figures, 5 tables, In press at the Journal of Computational Scienc