30 years of HIV in Kenya

30 years of HIV in Kenya

Organisation: Internews in Kenya (Kenya)

Publication Date: 04/02/2015

Description

2014 marks 30 years since the first documented case of HIV in Kenya. This occasion inspired Internews in Kenya to create 30 Years of HIV, an interactive digital project that explores the media coverage of the HIV epidemic in Kenya over time and marks key milestones along the way. The project is intended to help journalists and society reflect on its own evolving understanding of the political, social, economic and human impact of HIV and whether the information provided over three decades has done justice to the complexity of one of the greatest challenges of our time. The project (http://www.internewskenya.org/dataportal/content?page=hiv@30) does this through a 3D timeline, interactive visualisations of 30 years of coverage of HIV by The Nation newspaper and HIV data, infographics, photographic essays of people living with HIV and multimedia pieces that share the experiences of experts who are and have been on the frontline of the epidemic. A review of data reveals that the country’s HIV epidemic is not as generalized as previously thought. Hyper-epidemics persist in parts of the country and this means that to succeed the HIV response must give special attention to populations at higher risk of HIV. IMPACT After creating the project that was presented online on the Internews site and on Internews in Kenya and the National Aids Control Council partnered to present a month-long digital and photographic exhibition from 21 November to 20 December 2014. Kenya’s largest circulation newspaper, The Nation, whose archives helped create #HIV30Kenya hosted the platform on its online news site: http://www.nation.co.ke/hiv30kenya The projects shaped the telling of the HIV story in Kenya around World Aids Day 2014, including: • Violet Otindo of K24: https://www.youtube.com/watch?v=JALrMxG-VBk&list=UUt3SE-Mvs3WwP7UW-PiFdqQ https://www.youtube.com/watch?v=hywlx9DE5aM&list=UUt3SE-Mvs3WwP7UW-PiFdqQ • Kiundu Waweru of The Standard newspaper reported on the photo exhibition that documents 30 years of HIV(http://www.internewskenya.org/summaries.php?id=7326) • This is how the Daily Nation`s DN2 pull out (http://www.internewskenya.org/summaries.php?id=7327) commemorated 30 years of HIV in Kenya on World Aids Day • KBC coverage: -KBC Good Morning Kenya interview with Ida Jooste, Country Director for Internews (https://www.youtube.com/watch?v=hq_LzUSIbvg) -GMK Interview: World AIDS day (https://www.youtube.com/watch?v=fQM-ZimNz9M) More stories in the About section of project

Technologies used for this project:

DATA ANALYSIS The project scraped (transferred data from human-readable input to digital) HIV-related data from PDF reports and publications, journals, government ministries, ‘dump files’ and online data portals. The data was analysed using Stata and Excel and displayed graphically using google fusion maps. Infographics were created using Adobe illustrator and isotype interactive charts were created using d3 and polymer. THE NATION NEWSPAPER COVERAGE ON HIV ANALYSIS VISUALISATION Internews in Kenya's retrospective on HIV in the Kenyan media considered equally the news content as well as the language associated with that coverage. Our approach to this project studied the general tag trends as seen in the “Words through Time” section. The Nation newspaper articles were sourced from the media house’s archives by querying articles tagged HIV and AIDS and from the Internews in Kenya library. The process took several weeks and resulted in 9,419 articles. Using Microsoft OneNote, the PDF articles were converted into word or text documents to enable analysis in Overview Project. About 30 percent of the articles were not analysed because they were illegible following conversion into text. Tableau Public Visualisation: Words through time In order to derive meaning from the text of each of The Nation articles, we began in Overview Project, an online open-source tool originally designed to help journalists find stories in large numbers of documents. Overview Project goes through all the words in a document and identifies trends: words that appear often, words that appear frequently with other words, topics and subtopics. Overview enabled us to analyse the text of all the articles by year and begin to identify trends, both topics and then specific words under these topics, and track these changes over time. It displays findings in a tree structure like this with the size of the box corresponding to the number of articles that contain those words: Once we ran all the articles for all the years through Overview Project, we had a good idea of the important words and topics in each year. There were some surprises, such as the frequency of women and education, which we had not expected. From this initial analysis we made our own list of words to search for. These fell into three categories. 1. Words: This included all words or groups of words (such as “commercial sex workers”) that we wanted to count in each year. 2. Terms: This allowed us to group words that are basically synonyms. For example, we grouped “commercial sex workers,” “prostitutes” and “female sex workers” into the same term since they are different words to describe the same thing. 3. Categories: This is where subjective analysis came into play as we started to make sense of the terms as they fell into certain topics. For example we included the terms: men who have sex with men, people who inject drugs, sex workers, truck drivers and women into the category high risk groups. Once we had a list of words, terms and categories, we added a layer of 92 tags in Overview Project. This means, we typed in the specific word and Overview Project counted the number of mentions in the year and tagged it with a different colour. The result of tagging appears like this: On the right hand side you can see a display of how many times each word tag appears in each article. If you click on one of the articles it will show you where it appears in the text. This makes it easier to quickly review the context the word was used in. Once we searched for all the tags in all the years, we exported this list by year. This gave us a total count of how many times each of the 92 words were mentioned each year. Next, we opened these counts in Excel and added a few new columns. We added a column to classify each word by term and each term by category. We also added a column for the year. In order to show the relative importance of each word, term and category, we did a few calculations: 1. We calculated the percentage of each word under each term. For example, if prostitute was mentioned two times out of a total four words tagged under sex worker, the frequency of prostitute under sex worker would be 50 percent. 2. We calculated the percentage of each term under each category. For example, if the term sex worker was mentioned four times in high risk groups, which had 16 total mentions for that year, then 25 per cent of content under high risk groups related to sex workers. 3. Finally, to give a broad overview, we calculated how many words were mentioned under five broad categories: High-risk groups, Prevention, Research, Stigma and Treatment. To do this, we took the total number of mentions of each word under each category and divided it by the total number of words. For example, if there were 50 words categorised under high-risk groups and a total of 500 words, then 10 percent of the content fell under the category high-risk groups. Our next step was to visualise our findings using Tableau Public. We broke down the visualisation into three parts. 1. The timeline gives a broad overview of how the discussion around HIV has evolved by tracking the popularity of categories over time. Therefore, the timeline displays the percentage popularity of each category by year. 2. The treemap displays more specific data: it shows the term frequency under the category you selected on the timeline. The bigger the square, the more frequently that term appeared under that category. Note, the treemap does not allow you to compare terms in different categories. You can only display the terms under one category at a time. 3. The most granular data is the word cloud. The word cloud shows the frequency of each word grouped under each term. The size of the word corresponds to the frequency of the word under the term. A time slider allows you to visualise how term and word frequency change over time. The colours display the relationships between the categories, terms and words from each part of the visualisation.
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