Social media is a transformative digital technology, collapsing the “six degrees ofseparation” which have previously characterized many social networks, and breaking down many of the barriers to individuals communicating with each other. Some commentators suggest that this is having profound effects across society, that social media have opened up new channels for public debates and have revolutionized the communication of prominent public issues such as climate change. In this article we provide the first systematic and critical review of the literature on social media and climate change. We highlight three key findings from the literature: a substantial bias toward Twitter studies, the prevalent approaches to researching climate change on social media (publics, themes, and professional communication), and important empirical findings (the use of mainstream information sources, discussions of “settled science,” polarization, and responses to temperature anomalies).Following this, we identify gaps in the existing literature that should beaddressed by future research: namely, researchers should consider qualitativestudies, visual communication and alternative social media platforms to Twitter.We conclude by arguing for further research that goes beyond a focus on sciencecommunication to a deeper examination of how publics imagine climate changeand its future role in social life.
Social media firestorms pose a significant challenge for firms in the digital age. Tackling firestorms is difficult because the judgments and responses from social media users are influenced by not only the nature of the transgressions but also by the reactions and opinions of other social media users. Drawing on the heuristic-systematic information processing model, we propose a research model to explain the effects of social impact (the heuristic mode) and argument quality and moral intensity (the systematic mode) on perceptions of firm wrongness (the judgment outcome) as well as the effects of perceptions of firm wrongness on vindictive complaining and patronage reduction. We adopted a mixed methods approach in our investigation, including a survey, an experiment, and a focus group study. Our findings show that the heuristic and systematic modes of information processing exert both direct and interaction effects on individuals’ judgment. Specifically, the heuristic mode of information processing dominates overall and also biases the systematic mode. Our study advances the literature by offering an alternative explanation for the emergence of social media firestorms and identifying a novel context in which the heuristic mode dominates in dual information processing. It also sheds light on the formulation of response strategies to mitigate the adverse impacts resulting from social media firestorms. We conclude our paper with limitations and future research directions.
As part of my PhD research, I investigate the factors of student success and the influence of the use of social media by first year students in higher education. For this I use the insights provided by the highly influential and leading integration theory of Tinto and diminished the amount of variables by only using the best predictive ones. Hereby, avoiding the capitalization of chance and establishing a more easy to use model for teachers and management. Furthermore, I enriched the model with the use of social media, in particular Facebook, to better suit students’ contemporary society in the developed world. Principal component analysis on Facebook usage provided different integration/engagement components, which I coined peer-engagement and knowledge engagement. Both consisted of various purposes of Facebook use (information, education, social and leisure) and the use of different pages amongst students. To uncover if these latent variables play a significant role in student success or if Facebook is a multi-distracting platform, two models were compared using structural equation modeling with SPSS AMOS; one with and one without the peer-, and knowledge engagement variables. The fit of both models are compared using the normed fit index (NFI), the comparative fit index (CFI), the Tucker-Lewis Index (TLI) and the root mean square error of approximation (RMSEA). In addition, the direct influence and indirect influence of all variables are compared to provide a better insight into what kind of influence social media can have upon student success.
