Participatory Militias: An Analysis of an Armed Movement's Online Audience

Armed groups of civilians known as "self-defense forces" have ousted the powerful Knights Templar drug cartel from several towns in Michoacan. This militia uprising has unfolded on social media, particularly in the "VXM" ("Valor por Michoacan," Spanish for "Courage for Michoacan") Facebook page, gathering more than 170,000 fans. Previous work on the Drug War has documented the use of social media for real-time reports of violent clashes. However, VXM goes one step further by taking on a pro-militia propagandist role, engaging in two-way communication with its audience. This paper presents a descriptive analysis of VXM and its audience. We examined nine months of posts, from VXM's inception until May 2014, totaling 6,000 posts by VXM administrators and more than 108,000 comments from its audience. We describe the main conversation themes, post frequency and relationships with offline events and public figures. We also characterize the behavior of VXM's most active audience members. Our work illustrates VXM's online mobilization strategies, and how its audience takes part in defining the narrative of this armed conflict. We conclude by discussing possible applications of our findings for the design of future communication technologies.Comment: Participatory Militias: An Analysis of an Armed Movement's Online Audience. Saiph Savage, Andres Monroy-Hernandez. CSCW: ACM Conference on Computer-Supported Cooperative Work 201

Narratiivin variaatio: mediakertomusten visualisointi

The media plays an increasingly large role in shaping social reality, and even small shifts in its narrative content or tone can have widespread repercussions in the public’s perception of past and present phenomena. Being able to track changes in media coverage over time, particularly visually, could have many conceivable applications and offer the potential for aiding social change in journalism. This case study explores how data visualization could be used to examine differences in media narrative patterns over time and across publications. The findings indicate that while there are many existing means of visualizing patterns in such narrative data on a timeline axis, few if any address the aspect of co-occurrence of variables. Comparing co-occurrence chronologically, particularly when applied to word and topic choices in media coverage, can shed more light on currents in public opinion than simply counting the occurrence of terms independently. Furthermore, the findings suggest that visualizing such patterns in this case could be best accomplished using a form of set visualization, specifically a simplified vertical version of linear diagrams repeated horizontally across parallel timeline axes. This case study also outlines the methods, ethical considerations, and examples of employing such a visualization prototype using a sample dataset of full text news articles.Medialla on yhä suurempi rooli yhteiskunnan todellisuuden tuottamisessa, ja jopa pienet muutokset sisällössä voivat laajalti muokata yleisön käsitystä menneistä ja nykyisistä ilmiöistä. Mediasisältöjen muutosten seuranta, erityisesti visuaalisesti, soveltuisi moneen tarkoitukseen ja voisi edistää vastuullisen journalismin kehitystä ja käyttöä yhteiskunnassa. Tässä tapaustutkimuksessa selvitetään, miten tiedon visualisointia voitaisiin käyttää tutkimaan eroja mediakertomuksissa ajan myötä eri julkaisuissa. Tulokset osoittavat, että vaikka olemassa olevia keinoja vastaavan tiedon visualisointiin löytyy, yksikään ei tuo esille muuttujien samanaikaisuuden näkökulmaa. Samanaikaisuuden vertailu kronologisesti, erityisesti sana- ja aihevalintoihin sovellettuna mediasisällön osalta, voi paremmin valaista yleisen mielipiteen virtoja kuin yksittäisten sanavalintojen laskeminen. Lisäksi havainnot viittaavat siihen, että tällaisten mallien visualisointi voitaisiin parhaiten toteuttaa käyttämällä joukko-opin visualisointeja, erityisesti lineaaristen kaavioiden yksinkertaistettua vertikaalista versiota rinnakkaisilla aikajana-akseleilla. Tässä tapaustutkimuksessa esitetään myös menetelmät, eettiset näkökulmat ja esimerkit tällaisen visualisointiprototyypin tuotosta ja käytöstä uutisartikkelidataa hyödyntäen

Conspiracy in the Time of Corona: Automatic detection of Emerging Covid-19 Conspiracy Theories in Social Media and the News

Abstract Rumors and conspiracy theories thrive in environments of low confi- dence and low trust. Consequently, it is not surprising that ones related to the Covid-19 pandemic are proliferating given the lack of scientific consensus on the virus’s spread and containment, or on the long term social and economic ramifications of the pandemic. Among the stories currently circulating are ones suggesting that the 5G telecommunication network activates the virus, that the pandemic is a hoax perpetrated by a global cabal, that the virus is a bio-weapon released deliberately by the Chinese, or that Bill Gates is using it as cover to launch a broad vaccination program to facilitate a global surveillance regime. While some may be quick to dismiss these stories as having little impact on real-world behavior, recent events including the destruction of cell phone towers, racially fueled attacks against Asian Americans, demonstrations espousing resistance to public health orders, and wide-scale defiance of scientifically sound public mandates such as those to wear masks and practice social distancing, countermand such conclusions. Inspired by narrative theory, we crawl social media sites and news reports and, through the application of automated machine-learning methods, discover the underlying narrative frame- works supporting the generation of rumors and conspiracy theories. We show how the various narrative frameworks fueling these stories rely on the alignment of otherwise disparate domains of knowledge, and consider how they attach to the broader reporting on the pandemic. These alignments and attachments, which can be monitored in near real-time, may be useful for identifying areas in the news that are particularly vulnerable to reinterpretation by conspiracy theorists. Understanding the dynamics of storytelling on social media and the narrative frameworks that provide the generative basis for these stories may also be helpful for devising methods to disrupt their spread

The computational turn: thinking about the digital humanities

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Investigating Rumor Propagation with TwitterTrails

Social media have become part of modern news reporting, used by journalists to spread information and find sources, or as a news source by individuals. The quest for prominence and recognition on social media sites like Twitter can sometimes eclipse accuracy and lead to the spread of false information. As a way to study and react to this trend, we introduce {\sc TwitterTrails}, an interactive, web-based tool ({\tt twittertrails.com}) that allows users to investigate the origin and propagation characteristics of a rumor and its refutation, if any, on Twitter. Visualizations of burst activity, propagation timeline, retweet and co-retweeted networks help its users trace the spread of a story. Within minutes {\sc TwitterTrails} will collect relevant tweets and automatically answer several important questions regarding a rumor: its originator, burst characteristics, propagators and main actors according to the audience. In addition, it will compute and report the rumor's level of visibility and, as an example of the power of crowdsourcing, the audience's skepticism towards it which correlates with the rumor's credibility. We envision {\sc TwitterTrails} as valuable tool for individual use, but we especially for amateur and professional journalists investigating recent and breaking stories. Further, its expanding collection of investigated rumors can be used to answer questions regarding the amount and success of misinformation on Twitter.Comment: 10 pages, 8 figures, under revie