This article provides a comprehensive investigation on the relations between virality of news articles and the emotions they are found to evoke. Virality, in our view, is a phenomenon with many facets, i.e. under this generic term several different effects of persuasive communication are comprised. By exploiting a high-coverage and bilingual corpus of documents containing metrics of their spread on social networks as well as a massive affective annotation provided by readers, we present a thorough analysis of the interplay between evoked emotions and viral facets. We highlight and discuss our findings in light of a cross-lingual approach: while we discover differences in evoked emotions and corresponding viral effects, we provide preliminary evidence of a generalized explanatory model rooted in the deep structure of emotions: the Valence-Arousal-Dominance (VAD) circumplex. We find that viral facets appear to be consistently affected by particular VAD configurations, and these configurations indicate a clear connection with distinct phenomena underlying persuasive communication.
. @cite_32 have shown, via a massive experiment on Facebook, that emotional states can be transferred to others via emotional contagion, leading people to experience the same emotions without their awareness. The experiment included reducing the amount of emotional content in the News Feed of a user: when positive expressions were reduced, people produced fewer positive posts and more negative posts; when negative expressions were reduced, the opposite pattern occurred. While in @cite_32 the authors manipulated News Feed content, in our study we simply analyze existing and publicly available content voluntarly annotated by readers.
@cite_32
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Emotional states can be transferred to others via emotional contagion, leading people to experience the same emotions without their awareness. Emotional contagion is well established in laboratory experiments, with people transferring positive and negative emotions to others. Data from a large real-world social network, collected over a 20-y period suggests that longer-lasting moods (e.g., depression, happiness) can be transferred through networks [Fowler JH, Christakis NA (2008) BMJ 337:a2338], although the results are controversial. In an experiment with people who use Facebook, we test whether emotional contagion occurs outside of in-person interaction between individuals by reducing the amount of emotional content in the News Feed. When positive expressions were reduced, people produced fewer positive posts and more negative posts; when negative expressions were reduced, the opposite pattern occurred. These results indicate that emotions expressed by others on Facebook influence our own emotions, constituting experimental evidence for massive-scale contagion via social networks. This work also suggests that, in contrast to prevailing assumptions, in-person interaction and nonverbal cues are not strictly necessary for emotional contagion, and that the observation of others’ positive experiences constitutes a positive experience for people. The idea that group contexts can intensify emotions is centuries old. Yet, evidence that speaks to how, or if, emotions become more intense in groups remains elusive. Here we examine the novel possibility that group attention—the experience of simultaneous coattention with one’s group members—increases emotional intensity relative to attending alone, coattending with strangers, or attending nonsimultaneously with one’s group members. In Study 1, scary advertisements felt scarier under group attention. In Study 2, group attention intensified feelings of sadness to negative images, and feelings of happiness to positive images. In Study 3, group attention during a video depicting homelessness led to greater sadness that prompted larger donations to charities benefiting the homeless. In Studies 4 and 5, group attention increased the amount of cognitive resources allocated toward sad and amusing videos (as indexed by the percentage of thoughts referencing video content), leading to more sadness and happiness, respectively. In all, these effects could not be explained by differences in physiological arousal, emotional contagion, or vicarious emotional experience. Greater fear, gloom, and glee can thus result from group attention to scary, sad, and happy events, respectively. Contagion, a concept from epidemiology, has long been used to characterize social influence on people’s behavior and affective (emotional) states. While it has revealed many useful insights, it is not clear whether the contagion metaphor is sufficient to fully characterize the complex dynamics of psychological states in a social context. Using wearable sensors that capture daily face-to-face interaction, combined with three daily experience sampling surveys, we collected the most comprehensive data set of personality and emotion dynamics of an entire community of work. From this high-resolution data about actual (rather than self-reported) face-to-face interaction, a complex picture emerges where contagion (that can be seen as adaptation of behavioral responses to the behavior of other people) cannot fully capture the dynamics of transitory states. We found that social influence has two opposing effects on states: adaptation effects that go beyond mere contagion, and complementarity effects whereby individuals’ behaviors tend to complement the behaviors of others. Surprisingly, these effects can exhibit completely different directions depending on the stable personality or emotional dispositions (stable traits) of target individuals. Our findings provide a foundation for richer models of social dynamics, and have implications on organizational engineering and workplace well-being. This paper presents a new method for sentiment analysis in Facebook that, starting from messages written by users, supports: (i) to extract information about the users' sentiment polarity (positive, neutral or negative), as transmitted in the messages they write; and (ii) to model the users' usual sentiment polarity and to detect significant emotional changes. We have implemented this method in SentBuk, a Facebook application also presented in this paper. SentBuk retrieves messages written by users in Facebook and classifies them according to their polarity, showing the results to the users through an interactive interface. It also supports emotional change detection, friend's emotion finding, user classification according to their messages, and statistics, among others. The classification method implemented in SentBuk follows a hybrid approach: it combines lexical-based and machine-learning techniques. The results obtained through this approach show that it is feasible to perform sentiment analysis in Facebook with high accuracy (83.27 ). In the context of e-learning, it is very useful to have information about the users' sentiments available. On one hand, this information can be used by adaptive e-learning systems to support personalized learning, by considering the user's emotional state when recommending him her the most suitable activities to be tackled at each time. On the other hand, the students' sentiments towards a course can serve as feedback for teachers, especially in the case of online learning, where face-to-face contact is less frequent. The usefulness of this work in the context of e-learning, both for teachers and for adaptive systems, is described too.