44,688 research outputs found
The one comparing narrative social network extraction techniques
Analysing narratives through their social networks is an expanding field in quantitative literary studies. Manually extracting a social network from any narrative can be time consuming, so automatic extraction methods of varying complexity have been developed. However, the effect of different extraction methods on the resulting networks is unknown. Here we model and compare three extraction methods for social networks in narratives: manual extraction, co-occurrence automated extraction and automated extraction using machine learning. Although the manual extraction method produces more precise results in the network analysis, it is highly time consuming. The automatic extraction methods yield comparable results for density, centrality measures and edge weights. Our results provide evidence that automatically-extracted social networks are reliable for many analyses. We also describe which aspects of analysis are not reliable with such a social network. Our findings provide a framework to analyse narratives, which help us improve our understanding of how stories are written and evolve, and how people interact with each other. Index Tenns-social networks, narratives, televisionMichelle Edwards, Jonathan Tuke, Matthew Roughan, Lewis Mitchel
A systematic review of recommended modifications of CBT for people with cognitive impairments following brain injury
Due to diverse cognitive, emotional and interpersonal changes that can follow brain injury, psychological therapies often need to be adapted to suit the complex needs of this population. The aims of the study were to synthesise published recommendations for therapy modifications following brain injury from non-progressive traumatic, vascular, or metabolic causes and to determine how often such modifications have been applied to cognitive behavioural therapy (CBT) for post-injury emotional adjustment problems. A systematic review and narrative synthesis of therapy modifications recommended in review articles and reported in intervention studies was undertaken. Database and manual searches identified 688 unique papers of which eight review articles and 16 intervention studies met inclusion criteria. The review articles were thematically analysed and a checklist of commonly recommended modifications composed. The checklist items clustered under themes of: therapeutic education and formulation; attention; communication; memory; and executive functioning. When this checklist was applied to the intervention studies, memory aids and an emphasis on socialising patients to the CBT model were most frequently reported as adaptations. It was concluded that the inconsistent reporting of psychological therapy adaptations for people with brain injury is a barrier to developing effective and replicable therapies. We present a comprehensive account of potential modifications that should be used to guide future research and practice
A framework for interrogating social media images to reveal an emergent archive of war
The visual image has long been central to how war is seen, contested and legitimised, remembered and forgotten. Archives are pivotal to these ends as is their ownership and access, from state and other official repositories through to the countless photographs scattered and hidden from a collective understanding of what war looks like in individual collections and dusty attics. With the advent and rapid development of social media, however, the amateur and the professional, the illicit and the sanctioned, the personal and the official, and the past and the present, all seem to inhabit the same connected and chaotic space.However, to even begin to render intelligible the complexity, scale and volume of what war looks like in social media archives is a considerable task, given the limitations of any traditional human-based method of collection and analysis. We thus propose the production of a series of ‘snapshots’, using computer-aided extraction and identification techniques to try to offer an experimental way in to conceiving a new imaginary of war. We were particularly interested in testing to see if twentieth century wars, obviously initially captured via pre-digital means, had become more ‘settled’ over time in terms of their remediated presence today through their visual representations and connections on social media, compared with wars fought in digital media ecologies (i.e. those fought and initially represented amidst the volume and pervasiveness of social media images).To this end, we developed a framework for automatically extracting and analysing war images that appear in social media, using both the features of the images themselves, and the text and metadata associated with each image. The framework utilises a workflow comprising four core stages: (1) information retrieval, (2) data pre-processing, (3) feature extraction, and (4) machine learning. Our corpus was drawn from the social media platforms Facebook and Flickr
Data Innovation for International Development: An overview of natural language processing for qualitative data analysis
Availability, collection and access to quantitative data, as well as its
limitations, often make qualitative data the resource upon which development
programs heavily rely. Both traditional interview data and social media
analysis can provide rich contextual information and are essential for
research, appraisal, monitoring and evaluation. These data may be difficult to
process and analyze both systematically and at scale. This, in turn, limits the
ability of timely data driven decision-making which is essential in fast
evolving complex social systems. In this paper, we discuss the potential of
using natural language processing to systematize analysis of qualitative data,
and to inform quick decision-making in the development context. We illustrate
this with interview data generated in a format of micro-narratives for the UNDP
Fragments of Impact project
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Conspiracy in the Time of Corona: Automatic detection of Emerging Covid-19 Conspiracy Theories in Social Media and the News
Abstract
Rumors and conspiracy theories thrive in environments of low confi- dence and low trust. Consequently, it is not surprising that ones related to the Covid-19 pandemic are proliferating given the lack of scientific consensus on the virus’s spread and containment, or on the long term social and economic ramifications of the pandemic. Among the stories currently circulating are ones suggesting that the 5G telecommunication network activates the virus, that the pandemic is a hoax perpetrated by a global cabal, that the virus is a bio-weapon released deliberately by the Chinese, or that Bill Gates is using it as cover to launch a broad vaccination program to facilitate a global surveillance regime. While some may be quick to dismiss these stories as having little impact on real-world behavior, recent events including the destruction of cell phone towers, racially fueled attacks against Asian Americans, demonstrations espousing resistance to public health orders, and wide-scale defiance of scientifically sound public mandates such as those to wear masks and practice social distancing, countermand such conclusions. Inspired by narrative theory, we crawl social media sites and news reports and, through the application of automated machine-learning methods, discover the underlying narrative frame- works supporting the generation of rumors and conspiracy theories. We show how the various narrative frameworks fueling these stories rely on the alignment of otherwise disparate domains of knowledge, and consider how they attach to the broader reporting on the pandemic. These alignments and attachments, which can be monitored in near real-time, may be useful for identifying areas in the news that are particularly vulnerable to reinterpretation by conspiracy theorists. Understanding the dynamics of storytelling on social media and the narrative frameworks that provide the generative basis for these stories may also be helpful for devising methods to disrupt their spread
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