Credibility of climate change denial in social media

Public perception about the reality of climate change has remained polarized and propagation of fake information on social media can be a potential cause. Homophily in communication, the tendency of people to communicate with others having similar beliefs, is understood to lead to the formation of echo chambers which reinforce individual beliefs and fuel further increase in polarization. Quite surprisingly, in an empirical analysis of the effect of homophily in communication on the level of polarization using evidence from Twitter conversations on the climate change topic during 2007–2017, we find that evolution of homophily over time negatively affects the evolution of polarization in the long run. Among various information about climate change to which people are exposed to, they are more likely to be influenced by information that have higher credibility. Therefore, we study a model of polarization of beliefs in social networks that accounts for credibility of propagating information in addition to homophily in communication. We find that polarization can not increase with increase in homophily in communication unless information propagating fake beliefs has minimal credibility. We therefore infer from the empirical results that anti-climate change tweets are largely not credible

The ecological dynamics of political polarization in Catalonia. Analysis of the structuring in communities of the Twitter debate network during the 14 February (14-F) election campaign

Les eleccions catalanes del 14 de febrer de 2021 es van veure marcades, com ja havia passat amb les eleccions espanyoles de 2019, per la irrupció de l’extrema dreta de Vox al Parlament. A més, van ser unes eleccions de recomposició dels partits independentistes, després dels fets de l’1 d’octubre de 2017 i de l’aplicació de l’article 155 per part del Govern espanyol (Guerrero-Solé, 2022). De nou, Twitter va ser una de les plataformes en què es va centrar el debat polític durant la campanya electoral. Tant la recomposició de l’anomenat sector independentista com l’impacte de la irrupció de Vox en l’escenari polític van tenir una expressió en l’estructuració de les comunitats d’usuaris a la plataforma de microblogs (microblogging platform). Aprofitant una línia de recerca molt ben establerta en els estudis de xarxes socials a Catalunya, aquest treball analitza la dinàmica d’evolució de l’estructura en comunitats dels partits polítics en els debats a Twitter fruit de la incorporació a aquesta estructura d’un nou actor com és l’extrema dreta de Vox. L’estudi conclou que, com en el cas dels mitjans (Scolari, 2012), les xarxes d’interacció entre grups ideològics s’han d’estudiar des d’una perspectiva ecològica, tenint en compte l’impacte de l’emergència d’un nou actor en el sistema de partits. Aquesta perspectiva es contraposa a la tendència generalitzada d’analitzar la polarització des de la bipolaritat nord-americana, com en el cas de les dinàmiques d’exposició selectiva (Trilling, Klingeren i Tsfati, 2017), per exemple, i dibuixa un nou esquema de comprensió de la polarització i de les distàncies ideològiques entre grups i partits polítics.The Catalan elections of 14 February, 2021 were marked, as had previously happened with the Spanish elections of 2019, by the irruption of the far-right party Vox into the Parliament. In addition, these elections involved the reconstitution of the proindependence parties after the events of 1 October, 2017 and the application of Article 155 by the Spanish government (Guerrero-Solé, 2022). Again, Twitter was one of the platforms on which the political debate took place during the election campaign. Both the reorganization of the so-called pro-independence sector and the impact of Vox’s irruption on the political scene were reflected in the structuring of the user communities on the microblogging network. Taking advantage of a well-established line of social network research in Catalonia, this article analyzes the evolution of the political parties’ structuring in communities in the debates on Twitter as a result of the incorporation of a new actor, Vox, into this structure. The research concludes that, as in the case of the media (Scolari, 2012), the networks of interaction between ideological groups must be studied from an ecological perspective, considering the impact of a new actor’s emergence in the party system. This perspective contrasts with the widespread tendency to analyze polarization in terms of US bipolarity, as in the case of selective exposure dynamics (Trilling, Van Klingeren and Tsfati, 2017), for example, and it sketches out a new framework for understanding the polarization and ideological distances between political parties and groups

Idus de marzo en México. La acción directa en las redes y en las calles de las multitudes conectadas feministas

This article analyses the cycle of mass protests by digitally-connected Mexican feminists between March 2019 and March 2020. Taking a descriptive approach to the analysis, the study triangulates qualitative and ethnographic methods with social network analysis. The article recounts how this period, which was marked by increasingly transgressive conflict, began with the #MeToo campaign between March and April 2019 and culminated on March 8 2020 with the women’s march and strike. The article considers how the feminist movement was increasingly radicalized through the emergence of repertoires of direct action and became a collective social actor of major relevance in the country. This change was evident in its disruptive power within professional guilds, study centers, and institutional politics, as well as the establishment of strategies and synergies with new, unexpected, and a very diverse range of social actors. Este artículo analiza el ciclo de protestas de las multitudes conectadas feministas mexicanas entre marzo de 2019 a marzo de 2020. Este periodo marcado por una creciente conflictividad transgresora inicia con la intensa campaña del #MeToo entre marzo y abril de 2019 y culmina con la marcha del 8 de marzo de 2020 y el Paro de Mujeres. El movimiento feminista se radicaliza con la aparición de repertorios de acción directa y se convierte en un actor colectivo de gran relevancia en el país, con un poder disruptivo en los gremios profesionales, en los centros de estudio y en la política institucional, estableciendo estrategias y sinergias con actores nuevos, inesperados y enormemente diversos.  Para una aproximación descriptiva de este ciclo como estudio de caso, se aplica la triangulación entre métodos cualitativos y etnográficos con el análisis de redes sociales

Exploring How Homophily and Accessibility Can Facilitate Polarization in Social Networks

Polarization in online social networks has gathered a significant amount of attention in the research community and in the public sphere due to stark disagreements with millions of participants on topics surrounding politics, climate, the economy and other areas where an agreement is required. This work investigates into greater depth a type of model that can produce ideological segregation as a result of polarization depending on the strength of homophily and the ability of users to access similar minded individuals. Whether increased access can induce larger amounts of societal separation is important to investigate, and this work sheds further insight into the phenomenon. Center to the hypothesis of homophilic alignments in friendship generation is that of a discussion group or community. These are modeled and the investigation into their effect on the dynamics of polarization is presented. The social implications demonstrate that initial phases of an ideological exchange can result in increased polarization, although a consensus in the long run is expected and that the separation between groups is amplified when groups are constructed with ideological homophilic preferences

Stochastic Sampling and Machine Learning Techniques for Social Media State Production

The rise in the importance of social media platforms as communication tools has been both a blessing and a curse. For scientists, they offer an unparalleled opportunity to study human social networks. However, these platforms have also been used to propagate misinformation and hate speech with alarming velocity and frequency. The overarching aim of our research is to leverage the data from social media platforms to create and evaluate a high-fidelity, at-scale computational simulation of online social behavior which can provide a deep quantitative understanding of adversaries\u27 use of the global information environment. Our hope is that this type of simulation can be used to predict and understand the spread of misinformation, false narratives, fraudulent financial pump and dump schemes, and cybersecurity threats. To do this, our research team has created an agent-based model that can handle a variety of prediction tasks. This dissertation introduces a set of sampling and deep learning techniques that we developed to predict specific aspects of the evolution of online social networks that have proven to be challenging to accurately predict with the agent-based model. First, we compare different strategies for predicting network evolution with sampled historical data based on community features. We demonstrate that our community-based model outperforms the global one at predicting population, user, and content activity, along with network topology over different datasets. Second, we introduce a deep learning model for burst prediction. Bursts may serve as a signal of topics that are of growing real-world interest. Since bursts can be caused by exogenous phenomena and are indicative of burgeoning popularity, leveraging cross-platform social media data is valuable for predicting bursts within a single social media platform. An LSTM model is proposed in order to capture the temporal dependencies and associations based upon activity information. These volume predictions can also serve as a valuable input for our agent-based model. Finally, we conduct an exploration of Graph Convolutional Networks to investigate the value of weak-ties in classifying academic literature with the use of graph convolutional neural networks. Our experiments look at the results of treating weak-ties as if they were strong-ties to determine if that assumption improves performance. We also examine how node removal affects prediction accuracy by selecting nodes according to different centrality measures. These experiments provide insight for which nodes are most important for the performance of targeted graph convolutional networks. Graph Convolutional Networks are important in the social network context as the sociological and anthropological concept of \u27homophily\u27 allows for the method to use network associations in assisting the attribute predictions in a social network