Echo Chambers: Emotional Contagion and Group Polarization on Facebook

Abstract

Recent findings showed that users on Facebook tend to select information that adhere to their system of beliefs and to form polarized groups – i.e., echo chambers. Such a tendency dominates information cascades and might affect public debates on social relevant issues. In this work we explore the structural evolution of communities of interest by accounting for users emotions and engagement. Focusing on the Facebook pages reporting on scientific and conspiracy content, we characterize the evolution of the size of the two communities by fitting daily resolution data with three growth models – i.e. the Gompertz model, the Logistic model, and the Log-logistic model. Although all the models appropriately describe the data structure, the Logistic one shows the best fit. Then, we explore the interplay between emotional state and engagement of users in the group dynamics. Our findings show that communities’ emotional behavior is affected by the users’ involvement inside the echo chamber. Indeed, to an higher involvement corresponds a more negative approach. Moreover, we observe that, on average, more active users show a faster shift towards the negativity than less active ones.

Extracting significant signal of news consumption from social networks: the case of Twitter in Italian political elections

“…Analogously, the dynamics of the electoral campaign online media has a (relatively) long literature, focusing, time by time, on USA [8][9][10][11][12][13][14], Australia [15,16], Norway [17], Spain [18], Italy [19][20][21], France [22] and UK [23,24]. Generally, the shift from mediated to disintermediated news consumption has led to a range of documented phenomena: users tend to focus on information reinforcing their opinion (confirmation bias [25][26][27][28][29]) and to group in clusters of people with similar viewpoints, forming the so called echo chambers [26][27][28][29][30][31]. The different dynamics that the public debate follows on social-network platforms is also remarkable: the time evolution of viral non-verified contents is more persistent than the verified equivalent [27] and "negative" messages spread faster than "positive" ones, even if the latter reach on average a wider audience [32].…”

Hypermedia to connect them all: Autonomous hypermedia agents and socio‐technical interactions

“…Similar requirements hold true for commercial and private smart buildings where we are currently witnessing an exploding heterogeneity of connected devices, and across diverse fields ranging from logistics to transportation and health care . The described vision would furthermore have broad impact beyond technical systems, for instance in psychology and the social sciences: here, autonomous agents that can interact with the Social Web could help explore and raise awareness on social phenomena and social media dynamics that get reflected in the hypermedia structure, such as emotional contagion, self‐reinforcing spirals and group polarization (commonly referred to as echo chambers )—similar effects are at work in economics (eg, chaotic dynamics such as stock market bubbles and crashes). Finally, enabling agents to autonomously use the hypermedia fabric could revolutionize online goods and service markets by enabling customers to circumvent lock‐in measures by providers: autonomous agents in the form of personal assistants could discover alternative offerings and dynamically and seamlessly enrich a customer's online experience, thereby increasing consumer choice—and, crucially, competition between service providers.…”

A Cultural Evolution Approach to Digital Media

“…While the understanding offered by ethnographic (e.g., Boyd, 2014) or critical-theory-inspired (e.g., Fuchs, 2014) perspectives remain clearly important, the cultural evolution approach is in a better position to make sense also of the quantitative data that digital media usage quasi-automatically produces. On the other side, computer scientists and physicists had promptly made use of these data to study the diffusion of information in digital social networks (see Weng et al, 2012; Adamic et al, 2014; Cooney et al, 2016; Del Vicario et al, 2016, for few recent examples). These works importantly include quantitative analysis and models, and they can offer valuable insights on online activity.…”

“…While, in the popular image, cultural “evolution” implies that ideas and behaviors spread by replicating gene-like from individual to individual, practitioners tend to be more cautious about the analogy genes-cultural traits, in particular regarding fidelity of transmission. The term “meme,” invented by Richard Dawkins, is dismissed by the majority of cultural evolutionists, even though sometimes used in social-media literature (e.g., Weng et al, 2012; Adamic et al, 2014). …”

“…It is not entirely clear why this is the case 4 , but, from the point of view we are discussing, copy-and-paste reintroduce variation in highly preservative digital transmission, allowing for modifications that could make the messages more successful (Acerbi and Mesoudi, 2015). Adamic et al (2014) estimated a “mutation rate” of μ = 0.11 for Facebook status updates asked to be copy-and-pasted, i.e., 11, every 100 copies, were different from the original, which is extremely high considering the fidelity provided by the digital support.…”