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Do you agree with the authors that “…therefore, to watch is to witness.Taken into consideration that Nepali is the only official language, it’s an efficient and smart way of taking advantage of this. In spite of the acknowledged limitation mentioned in the paper, languages can effectively distinguish the audience of the tweets. It is a good example of the importance of content analysis and future researchers should not settle with superficial statistical phenomena and should try to look at the data closely.Įspecially, I found their way of defining the boundary of local vs. This well structured analysis revealed that despite the strong statistical correlation between geotags and images, the pictures posted are mostly infographics from news media which doesn’t really tell much about the “objective” depict of the on-the-ground situations of those affected places. Their analysis is comprehensive and convincing, in that they combine complete and strong statistical analysis and in-depth content analysis. They looked at the tweets with image contents with three different research questions in mind, and studied different dimensions of such tweets as well as their correlations: between geotags and contents, between local and global audiences, and between actual pictures of the disaster and appropriated images from other time and/or locations. I appreciate the thorough analysis taken by the author. This paper investigated imagery contents on tweets about the two earthquakes in Nepal in 2015, as a example to analyze disaster representation on social media. The fact that those images are appropriated is acknowledged either through replies or the originaltweets “as an honest mistake”, as “another way of visually representing disaster and garnering support through compelling imagery”.
Letterbomb doesnt contain boot.elf full#
They found four images in the full dataset of 400 image tweets that were confirmed to be appropriated from other times and/or places in globally-sourced-after-first, and an additional image in locally-sourced-after-first that has ambiguous origins. The third question studies the two competing expectations: journalistic accuracy and drawing a collective gaze of photography. After the second earthquake, the results suggest some disaster fatigue for those not affected by the events and celebrity attention becomes the new mechanisms for maintaining the world’s gaze upon Nepal. The analysis results from hand-coding the Top 100 most retweeted image tweets in each of the four categories show a different diffusion of content, with locals focusing more on the response and the damage the earthquake caused in their cities, and the global population focused more on the images of people suffering. global), resulting in four categories (globally-sourced-after-first, globally-sourced-after-second, locally-sourced-after-first, and locally-sourced-after-second). They defined the boundary between “local” and “global” by the usage of Nepali, and pided the tweets into two dimensions: time (after first vs. The second question aims to understand how local and global audience perceive and relate to the disaster. They found that the distribution is significantly correlated statistically, however, a more in-depth analysis revealed that the geotags mean relatively little in relation to the finer aspects of geography and damage in Nepal: the images are less frequently photos of on-the-ground activity, and more frequently infographics and maps that originate from news and media. The first question aims to understand if the photos distributed on social media correlates to the actual structural damage geographically, and if such distribution can measure the disaster accurately. The authors combined both statistical analysis and content analysis in their investigation. investigate the appropriation of imagery into the telling of the disaster event.investigate what images the Nepali population versus the rest of the world distributed.examine the correlation between geotagged image tweets in the affected Nepali region and the distribution of structural damage.
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This paper investigated the representation of the two 2015 Nepal earthquakes in April and May, via images shared on Twitter in three ways: In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (pp.