Building on studies of the hybrid media system and attention economy, we develop the concept of amplification to explore how the activities of social media–based publics may enlarge the attention paid to a given person or message. We apply the concept to the 2016 US election, asking who constituted Donald Trump’s enormous Twitter following and how that following contributed to his success at attracting attention, including from the mainstream press. Using spectral clustering based on social network similarity, we identify key publics that constituted Trump’s Twitter following and demonstrate how particular publics amplified his social media presence in different ways. Our discussion raises questions about how algorithms “read” metrics to guide content on social media platforms, how journalists draw on social media metrics in their determinations of news value and worthiness, and how the process of amplification relates to possibilities of citizen action through digital communication.
How did efforts that prompted the sharing of personal experiences of sexual violence and harassment around #MeToo coalesce into calls for action across a range of institutions and communities? We argue that sharing experiences of trauma in digital spaces created a network of acknowledgment, which supported and sustained nascent #MeToo activism based on the logic of connective action. This article attempts to (a) understand the temporal dynamics of these different discourses within the #MeToo movement on Twitter, (b) reveal the accounts animating these discourses and the most prominent themes within them, and (c) model the overtime relationship between these discourses and their relationship to major news event and #MeToo revelations. To do so, we analyze a 1% sample of tweets from the 5-month period following the revelations about Harvey Weinstein in early October 2017, employing a range of computational approaches, including part-of-speech tagging, dependency analysis, hashtags extraction, and retweet network analysis—to identify key discourses, actors, and themes. We then conduct time series analysis to identify the relationship between the two discourses and predict how the ebbs and flows of each discourse are shaped by news events.
Except for improving the organisational characteristics, value congruence is a useful concept that managers can leverage to improve positive outcomes for both the organisation and its nurses.
Drought is a major abiotic stress that adversely affects the growth and productivity of plants. Malondialdehyde (MDA), a substance produced by membrane lipids in response to reactive oxygen species (ROS), can be used as a drought indicator to evaluate the degree of plasma membrane damage and the ability of plants to drought stress tolerance. Still measuring MDA is usually a labor- and time-consuming task. In this study, near-infrared (NIR) spectroscopy combined with partial least squares (PLS) was used to obtain rapid and high-throughput measurements of MDA, and the application of this technique to plant drought stress experiments was also investigated. Two exotic conifer tree species, namely, slash pine (Pinus elliottii) and loblolly pine (Pinus taeda), were used as plant material exposed to drought stress; different types of spectral preprocessing methods and important feature-selection algorithms were applied to the PLS model to calibrate it and obtain the best MDA-predicting model. The results show that the best PLS model is established via the combined treatment of detrended variable–significant multivariate correlation algorithm (DET-sMC), where latent variables (LVs) were 6. This model has a respectable predictive capability, with a correlation coefficient (R2) of 0.66, a root mean square error (RMSE) of 2.28%, and a residual prediction deviation (RPD) of 1.51, and it was successfully implemented in drought stress experiments as a reliable and non-destructive method to detect the MDA content in real time.
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