Playing Conspiracy

Conspiracy

The novel coronavirus 2019 pandemic has brought about an overabundance of misinformation concerning the virus (SARS-CoV-2) and the coronavirus disease 2019 (COVID-19) it causes spreading rapidly on social media. While some more obviously untrustworthy sources may be easier for social media filters to identify and remove, an early feature was the cobranding of COVID-19 misinformation with other types of misinformation. To examine this, the top 10 Instagram posts (in English) were collected every day for 10 days (April 21-30th, 2020) for each of the hashtags #hoax, #governmentlies, and #plandemic. The #hoax was selected first as it is commonly used in conspiracy theory posts, and #governmentlies because it was the most commonly cotagged with #hoax. For comparison, we selected #plandemic as the most popular cotagged hashtag that was clearly COVID-19-related. This resulted in 300 Instagram posts available for our analysis. We conducted a content analysis by coding the themes contained in the posts, both for the images and the text caption shared by the Instagram users (including hashtags). The broad theme of general mistrust was the most common, including the idea that the government and/or media has fabricated or hidden information pertaining to COVID-19. Conspiracy theories were the second-most frequent theme among posts. Overall, COVID-19 was frequently presented in association with authority-questioning beliefs. Developing an understanding of how the public shares misinformation on COVID-19 alongside conspiracy theories and authority-questioning statements can aid public health officials and policymakers in limiting the spread of potentially life-threatening health misinformation.

Social media has been an effective vector for spreading disinformation about medicine and science. Informational hygiene can reduce the severity of falsehoods about health.

#FreeBritney and the Pleasures of Conspiracy

Burden of the Beast

Creating Conspiracy Beliefs: How Our Thoughts Are Shaped

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In today’s social media dominated world the effort to undermine democracy comes in an increasingly wide variety of forms. One example is the use of social media by contemporary leaders and groups where they engage in waging information warfare within their own nations upon their own citizens. This paper will examine how this has occurred with the group QANON. One of QANON’s central conspiracy theories maintains that a cabal of Satan-worshiping pedophiles is not only in control of running the world’s governments, but this cabal is also running a global child sex-trafficking ring. This cabal is also involved in plotting against US President Donald Trump, who is in turn engaged in combatting the cabal. Another QANON theory also claims that Trump is planning a day of reckoning against the cabal and its followers known as “The Storm.” This refers to an event when thousands of members of the supposed cabal will be arrested. There are currently no indications that any part of the theory is based on fact. The conspiracy theory began with an October 2017 online post by a supposed individual known as ‘Q’, who at the time was presumed to be a single American citizen. It is now also assumed and more likely that Q is actually a group of people. Q as an individual claimed to be a high-ranking government official with Q level clearance in the U.S. government with access to classified information related to the Trump administration. Perhaps more significantly Q also claimed to have classified information about the opponents of Trump in the United States. NBC News was the first member of the media to report that three distinct people took the original Q post and distributed it across multiple media and social media platforms in an effort to build an internet following. We take the activities related to Q to be a method of Hybrid Warfare, which here is defined in the context of social media. Hybrid warfare which is now often practiced in all forms of media but particularly within social media, does not have a universally recognized definition. In this analysis the phrase is employed to describe how any individual or group such as QANON can employ non-military tactics in social media in the effort to undermine and destabilize a government. We argue that disinformation and propaganda dissemination, are not new techniques, but that they have been adapted to current technologies and social media. It is assumed in this analysis that every conspiracy theory, when a conspiracy is identified, presents a moral issue. When a conspiracy theory is presented as an explanation for an action or as the cause of an event this serves the function of making an accusation. The accusation that lies at the center of the conspiracy is an attack upon the truthfulness of what is claimed to be true by the party that the conspiracy theorist is attacking. This analysis has as its goal an identification of the ethical issues with conspiracy theories used by political leaders and groups such as QANON in social media and attempts to anticipate ethical and political issues with the continued use of these conspiracy theories in social media in cthe effort to undermine democracy.

Conspiracy Theories, Mistrust Take Root During Pandemic

How Google Autocomplete Algorithms about Conspiracy Theorists Mislead the Public

Lists and Social MediaLists have long been an ordering mechanism for computer-mediated social interaction. While far from being the first such mechanism, blogrolls offered an opportunity for bloggers to provide a list of their peers; the present generation of social media environments similarly provide lists of friends and followers. Where blogrolls and other earlier lists may have been user-generated, the social media lists of today are more likely to have been produced by the platforms themselves, and are of intrinsic value to the platform providers at least as much as to the users themselves; both Facebook and Twitter have highlighted the importance of their respective “social graphs” (their databases of user connections) as fundamental elements of their fledgling business models. This represents what Mejias describes as “nodocentrism,” which “renders all human interaction in terms of network dynamics (not just any network, but a digital network with a profit-driven infrastructure).”The communicative content of social media spaces is also frequently rendered in the form of lists. Famously, blogs are defined in the first place by their reverse-chronological listing of posts (Walker Rettberg), but the same is true for current social media platforms: Twitter, Facebook, and other social media platforms are inherently centred around an infinite, constantly updated and extended list of posts made by individual users and their connections.The concept of the list implies a certain degree of order, and the orderliness of content lists as provided through the latest generation of centralised social media platforms has also led to the development of more comprehensive and powerful, commercial as well as scholarly, research approaches to the study of social media. Using the example of Twitter, this article discusses the challenges of such “big data” research as it draws on the content lists provided by proprietary social media platforms.Twitter Archives for ResearchTwitter is a particularly useful source of social media data: using the Twitter API (the Application Programming Interface, which provides structured access to communication data in standardised formats) it is possible, with a little effort and sufficient technical resources, for researchers to gather very large archives of public tweets concerned with a particular topic, theme or event. Essentially, the API delivers very long lists of hundreds, thousands, or millions of tweets, and metadata about those tweets; such data can then be sliced, diced and visualised in a wide range of ways, in order to understand the dynamics of social media communication. Such research is frequently oriented around pre-existing research questions, but is typically conducted at unprecedented scale. The projects of media and communication researchers such as Papacharissi and de Fatima Oliveira, Wood and Baughman, or Lotan, et al.—to name just a handful of recent examples—rely fundamentally on Twitter datasets which now routinely comprise millions of tweets and associated metadata, collected according to a wide range of criteria. What is common to all such cases, however, is the need to make new methodological choices in the processing and analysis of such large datasets on mediated social interaction.Our own work is broadly concerned with understanding the role of social media in the contemporary media ecology, with a focus on the formation and dynamics of interest- and issues-based publics. We have mined and analysed large archives of Twitter data to understand contemporary crisis communication (Bruns et al), the role of social media in elections (Burgess and Bruns), and the nature of contemporary audience engagement with television entertainment and news media (Harrington, Highfield, and Bruns). Using a custom installation of the open source Twitter archiving tool yourTwapperkeeper, we capture and archive all the available tweets (and their associated metadata) containing a specified keyword (like “Olympics” or “dubstep”), name (Gillard, Bieber, Obama) or hashtag (#ausvotes, #royalwedding, #qldfloods). In their simplest form, such Twitter archives are commonly stored as delimited (e.g. comma- or tab-separated) text files, with each of the following values in a separate column: text: contents of the tweet itself, in 140 characters or less to_user_id: numerical ID of the tweet recipient (for @replies) from_user: screen name of the tweet sender id: numerical ID of the tweet itself from_user_id: numerical ID of the tweet sender iso_language_code: code (e.g. en, de, fr, ...) of the sender’s default language source: client software used to tweet (e.g. Web, Tweetdeck, ...) profile_image_url: URL of the tweet sender’s profile picture geo_type: format of the sender’s geographical coordinates geo_coordinates_0: first element of the geographical coordinates geo_coordinates_1: second element of the geographical coordinates created_at: tweet timestamp in human-readable format time: tweet timestamp as a numerical Unix timestampIn order to process the data, we typically run a number of our own scripts (written in the programming language Gawk) which manipulate or filter the records in various ways, and apply a series of temporal, qualitative and categorical metrics to the data, enabling us to discern patterns of activity over time, as well as to identify topics and themes, key actors, and the relations among them; in some circumstances we may also undertake further processes of filtering and close textual analysis of the content of the tweets. Network analysis (of the relationships among actors in a discussion; or among key themes) is undertaken using the open source application Gephi. While a detailed methodological discussion is beyond the scope of this article, further details and examples of our methods and tools for data analysis and visualisation, including copies of our Gawk scripts, are available on our comprehensive project website, Mapping Online Publics.In this article, we reflect on the technical, epistemological and political challenges of such uses of large-scale Twitter archives within media and communication studies research, positioning this work in the context of the phenomenon that Lev Manovich has called “big social data.” In doing so, we recognise that our empirical work on Twitter is concerned with a complex research site that is itself shaped by a complex range of human and non-human actors, within a dynamic, indeed volatile media ecology (Fuller), and using data collection and analysis methods that are in themselves deeply embedded in this ecology. “Big Social Data”As Manovich’s term implies, the Big Data paradigm has recently arrived in media, communication and cultural studies—significantly later than it did in the hard sciences, in more traditionally computational branches of social science, and perhaps even in the first wave of digital humanities research (which largely applied computational methods to pre-existing, historical “big data” corpora)—and this shift has been provoked in large part by the dramatic quantitative growth and apparently increased cultural importance of social media—hence, “big social data.” As Manovich puts it: For the first time, we can follow [the] imaginations, opinions, ideas, and feelings of hundreds of millions of people. We can see the images and the videos they create and comment on, monitor the conversations they are engaged in, read their blog posts and tweets, navigate their maps, listen to their track lists, and follow their trajectories in physical space. (Manovich 461) This moment has arrived in media, communication and cultural studies because of the increased scale of social media participation and the textual traces that this participation leaves behind—allowing researchers, equipped with digital tools and methods, to “study social and cultural processes and dynamics in new ways” (Manovich 461). However, and crucially for our purposes in this article, many of these scholarly possibilities would remain latent if it were not for the widespread availability of Open APIs for social software (including social media) platforms. APIs are technical specifications of how one software application should access another, thereby allowing the embedding or cross-publishing of social content across Websites (so that your tweets can appear in your Facebook timeline, for example), or allowing third-party developers to build additional applications on social media platforms (like the Twitter user ranking service Klout), while also allowing platform owners to impose de facto regulation on such third-party uses via the same code. While platform providers do not necessarily have scholarship in mind, the data access affordances of APIs are also available for research purposes. As Manovich notes, until very recently almost all truly “big data” approaches to social media research had been undertaken by computer scientists (464). But as part of a broader “computational turn” in the digital humanities (Berry), and because of the increased availability to non-specialists of data access and analysis tools, media, communication and cultural studies scholars are beginning to catch up. Many of the new, large-scale research projects examining the societal uses and impacts of social media—including our own—which have been initiated by various media, communication, and cultural studies research leaders around the world have begun their work by taking stock of, and often substantially extending through new development, the range of available tools and methods for data analysis. The research infrastructure developed by such projects, therefore, now reflects their own disciplinary backgrounds at least as much as it does the fundamental principles of computer science. In turn, such new and often experimental tools and methods necessarily also provoke new epistemological and methodological challenges. The Twitter API and Twitter ArchivesThe Open

Pandemics, Power, and Conspiracy Theories

The advent of the Internet and social media has revolutionized the way we communicate and access information, significantly changing daily interactions and our engagement with the world. However, this change has also brought about a worrying trend – the rapid spread of misinformation and misconceptions. These online platforms have become powerful of spreading misinformation, promoting mistrust and promoting harmful ideas.This paper explores the mechanisms behind the spread of false information and conspiracy theories on social media. It examines the inherent design elements of these platforms that make them highly vulnerable to the propagation of falsehoods. Algorithms that prioritize engagement and virality, combined with the ease of sharing information, create an environment where misinformation can spread rapidly without adequate editorial oversight.Furthermore, this paper highlights the challenges faced by social media platforms in dealing with misleading content. The sheer volume of posts, the delicate balance between free speech and content regulation, and the complexity of identifying lies before they spread widely present significant hurdles.Case studies on phenomena such as the anti-vaccination movement and the QAnon conspiracy theory illustrate the harmful effects of spreading misinformation online. Furthermore, the paper outlines the broader impact of misinformation on individuals, society, and democracy. It explores how the spread of misinformation erodes trust in institutions, promotes public confusion and potentially influences political processes and decisions, posing a threat to democratic structures

Conspiracy theories spread through social and other media often bringing easy explanations of events that cannot be easily explained. Beliefs in conspiracy theories may lead to simplified and radical viewpoints that can negatively influence one's behavior and actions. The paper analyzes the association between beliefs in popular conspiracy theories spread through social media and work performance using the results of an authors’ test of conspiracy theories applied to a sample of 178 students of the Faculty of Military Leadership, University of Defence in Brno, Czech Republic. The students were selected as representatives of high-profile professions that should be trained to deal with potential disinformation and conspiracy theories. The assumption was that the students would be generally immune to the impact of conspiracy theories. The analysis did not confirm a hypothesis that individuals with top work performance are less prone to beliefs in conspiracy theories than individuals with solid/poor work performance. The findings confirm the necessity to systematically train people working in high-profile professions to work with available information and deal with potential disinformation and conspiracy theories. The findings are useful in the HR management practice of organizations that care about the professional qualities of their people and encourage further research on the origin, spread, and impact of conspiracy theories in the workplace.

Theoretical typology of deceptive content (Conspiracy Theories)