Thematically analysing social network content during disasters through the lens of the disaster management lifecycle

Social Networks such as Twitter are often used for disseminating and collecting information during natural disasters. The potential for its use in Disaster Management has been acknowledged. However, more nuanced understanding of the communications that take place on social networks are required to more effectively integrate this information into the processes within disaster management. The type and value of information shared should be assessed, determining the benefits and issues, with credibility and reliability as known concerns. Mapping the tweets in relation to the modelled stages of a disaster can be a useful evaluation for determining the benefits/drawbacks of using data from social networks, such as Twitter, in disaster management.A thematic analysis of tweets' content, language and tone during the UK Storms and Floods 2013/14 was conducted. Manual scripting was used to determine the official sequence of events, and classify the stages of the disaster into the phases of the Disaster Management Lifecycle, to produce a timeline. Twenty-five topics discussed on Twitter emerged, and three key types of tweets, based on the language and tone, were identified. The timeline represents the events of the disaster, according to the Met Office reports, classed into B. Faulkner's Disaster Management Lifecycle framework. Context is provided when observing the analysed tweets against the timeline. This illustrates a potential basis and benefit for mapping tweets into the Disaster Management Lifecycle phases. Comparing the number of tweets submitted in each month with the timeline, suggests users tweet more as an event heightens and persists. Furthermore, users generally express greater emotion and urgency in their tweets.This paper concludes that the thematic analysis of content on social networks, such as Twitter, can be useful in gaining additional perspectives for disaster management. It demonstrates that mapping tweets into the phases of a Disaster Management Lifecycle model can have benefits in the recovery phase, not just in the response phase, to potentially improve future policies and activities

Surface chemical and color characterization of juvenile tectona grandis wood subjected to steam-drying treatments

ArtículoThe color of Tectona grandis wood is an attribute that favors its commercialization, however, wood color from fast-growth plantation trees is clear and lacks uniformity. The aim of this work is to characterize steamed teak wood by means of the Fourier transform infrared spectroscopy (FTIR) and L a b color systems. Two moisture conditions (green and 50%) and two grain patterns (°at and quarter) of boards were analyzed through the application of di®erent steaming times (0, 3, 6, 9, 12, 15 and 18 h). The FTIR results showed that the bands at 1158, 1231, 1373 and 1419 cm 1 did not show any change with steaming, whereas the bands at 1053, 1108, 1453, 1506, 1536, 1558, 1595, 1652, 1683, 1700 and 1733 cm 1 presented a decrease in the intensity with the steaming time. The band at 1318 cm 1 was the only one that increased. Lightness (L ) was the most a®ected parameter, followed by yellowness (b ), while redness (a ) showed the smallest change. Surface color change ( E ) presented the lowest value between 3 h and 6 h of steam-drying in the boards with °at grain, whereas for boards with quarter grain, the smallest E value was obtained after 18 h of steaming

Participant Perceptions of Twitter Research Ethics

Social computing systems such as Twitter present new research sites that have provided billions of data points to researchers. However, the availability of public social media data has also presented ethical challenges. As the research community works to create ethical norms, we should be considering users’ concerns as well. With this in mind, we report on an exploratory survey of Twitter users’ perceptions of the use of tweets in research. Within our survey sample, few users were previously aware that their public tweets could be used by researchers, and the majority felt that researchers should not be able to use tweets without consent. However, we find that these attitudes are highly contextual, depending on factors such as how the research is conducted or disseminated, who is conducting it, and what the study is about. The findings of this study point to potential best practices for researchers conducting observation and analysis of public data

The Brexit Botnet and User-Generated Hyperpartisan News

In this paper we uncover a network of Twitterbots comprising 13,493 accounts that tweeted the U.K. E.U. membership referendum, only to disappear from Twitter shortly after the ballot. We compare active users to this set of political bots with respect to temporal tweeting behavior, the size and speed of retweet cascades, and the composition of their retweet cascades (user-to-bot vs. bot-to-bot) to evidence strategies for bot deployment. Our results move forward the analysis of political bots by showing that Twitterbots can be effective at rapidly generating small to medium-sized cascades; that the retweeted content comprises user-generated hyperpartisan news, which is not strictly fake news, but whose shelf life is remarkably short; and, finally, that a botnet may be organized in specialized tiers or clusters dedicated to replicating either active users or content generated by other bots